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null | null | transformers | This model is a model that has successfully merged without warnings from Mergekit. I hope quantization works.
Merging and quantization were successful, but of course the results were not good. | {} | text-generation | rhplus0831/maid-yuzu-v6 | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T15:18:09+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model is a model that has successfully merged without warnings from Mergekit. I hope quantization works.
Merging and quantization were successful, but of course the results were not good. | [] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
51
] | [
"passage: TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-squad-model2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 74
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-squad-model2", "results": []}]} | question-answering | varun-v-rao/roberta-base-squad-model2 | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-08T15:22:51+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-base #license-mit #endpoints_compatible #region-us
|
# roberta-base-squad-model2
This model is a fine-tuned version of roberta-base on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 74
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"# roberta-base-squad-model2\n\nThis model is a fine-tuned version of roberta-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 74\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-base #license-mit #endpoints_compatible #region-us \n",
"# roberta-base-squad-model2\n\nThis model is a fine-tuned version of roberta-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 74\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
69,
32,
6,
12,
8,
3,
90,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-base #license-mit #endpoints_compatible #region-us \n# roberta-base-squad-model2\n\nThis model is a fine-tuned version of roberta-base on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 74\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# image_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0856
- Accuracy: 0.0813
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 2.0855 | 0.0813 |
| No log | 2.0 | 80 | 2.0880 | 0.0813 |
| No log | 3.0 | 120 | 2.0852 | 0.0813 |
| No log | 4.0 | 160 | 2.0861 | 0.0813 |
| No log | 5.0 | 200 | 2.0858 | 0.0813 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "image_classification", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.08125, "name": "Accuracy"}]}]}]} | image-classification | miifta-hs/image_classification | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T15:25:32+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| image\_classification
=====================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0856
* Accuracy: 0.0813
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
86,
97,
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"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | shahzebnaveed/t5-large_PREFIX_TUNING_SEQ2SEQ | [
"transformers",
"safetensors",
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"1910.09700"
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#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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## Uses
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### Out-of-Scope Use
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### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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] |
null | null | transformers |
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| {"library_name": "transformers", "tags": []} | text-generation | MasakiK/stack-llama-2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T15:29:59+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
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### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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"# Model Card for Model ID",
"## Model Details",
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"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Training Details",
"### Training Data",
"### Training Procedure",
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"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
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"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emotion_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1249
- Accuracy: 0.6188
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 1.8344 | 0.3 |
| No log | 2.0 | 80 | 1.5609 | 0.4375 |
| No log | 3.0 | 120 | 1.4819 | 0.4562 |
| No log | 4.0 | 160 | 1.3477 | 0.5188 |
| No log | 5.0 | 200 | 1.2618 | 0.5813 |
| No log | 6.0 | 240 | 1.1946 | 0.5813 |
| No log | 7.0 | 280 | 1.1800 | 0.5875 |
| No log | 8.0 | 320 | 1.1921 | 0.5625 |
| No log | 9.0 | 360 | 1.1274 | 0.6 |
| No log | 10.0 | 400 | 1.0886 | 0.65 |
| No log | 11.0 | 440 | 1.0750 | 0.6125 |
| No log | 12.0 | 480 | 1.1349 | 0.575 |
| 1.0832 | 13.0 | 520 | 1.0841 | 0.5875 |
| 1.0832 | 14.0 | 560 | 1.1195 | 0.5813 |
| 1.0832 | 15.0 | 600 | 1.0865 | 0.6188 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "emotion_classification", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.61875, "name": "Accuracy"}]}]}]} | image-classification | malifiahm/emotion_classification | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T15:31:51+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| emotion\_classification
=======================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1249
* Accuracy: 0.6188
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
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"### Training results",
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec_RTSplit0208_19
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-japanese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0073
- Wer: 0.1871
- Cer: 0.1524
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 13
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 3.5447 | 1.0 | 120 | 3.4564 | 0.9849 | 0.9954 |
| 1.4868 | 2.0 | 240 | 1.3054 | 0.9996 | 0.7825 |
| 0.7829 | 3.0 | 360 | 0.6057 | 0.8097 | 0.5026 |
| 0.4487 | 4.0 | 480 | 0.2791 | 0.4736 | 0.2457 |
| 0.325 | 5.0 | 600 | 0.1375 | 0.3020 | 0.1677 |
| 0.2221 | 6.0 | 720 | 0.0720 | 0.2321 | 0.1591 |
| 0.2002 | 7.0 | 840 | 0.0372 | 0.2123 | 0.1295 |
| 0.1661 | 8.0 | 960 | 0.0276 | 0.2050 | 0.1247 |
| 0.129 | 9.0 | 1080 | 0.0153 | 0.1955 | 0.1571 |
| 0.0707 | 10.0 | 1200 | 0.0124 | 0.1925 | 0.1378 |
| 0.0737 | 11.0 | 1320 | 0.0107 | 0.1897 | 0.1601 |
| 0.0818 | 12.0 | 1440 | 0.0084 | 0.1879 | 0.1535 |
| 0.0672 | 13.0 | 1560 | 0.0073 | 0.1871 | 0.1524 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "model-index": [{"name": "wav2vec_RTSplit0208_19", "results": []}]} | automatic-speech-recognition | tndklab/wav2vec_RTSplit0208_19 | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:jonatasgrosman/wav2vec2-large-xlsr-53-japanese",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-08T15:33:16+00:00 | [] | [] | TAGS
#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec\_RTSplit0208\_19
========================
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-japanese on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0073
* Wer: 0.1871
* Cer: 0.1524
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 6e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 13
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 13",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 13",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
80,
116,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 13### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | beshoy25/llama_finetuning | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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- Demo [optional]:
## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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] |
null | null | transformers | # English to Lao Translation Model
Welcome to the forefront of linguistic innovation with our groundbreaking T5 language model designed specifically for English to Lao translation. In a rapidly globalizing world where effective communication is paramount, our T5 model stands as a beacon of excellence, offering unparalleled accuracy, fluency, and efficiency in bridging the language gap between Lao and English.
Built on state-of-the-art deep learning architecture and trained on vast datasets of Lao and English texts, our language model (LLM) harnesses the power of transformer-based technology to deliver seamless and precise translations. Whether you're a business expanding into Laotian markets, a researcher seeking to access Lao-language resources, or an individual connecting with Lao-speaking communities, our T5 model is your ultimate solution for unlocking linguistic barriers and fostering meaningful cross-cultural exchanges.
With a commitment to quality and innovation, our translation model not only translates words but also preserves context, tone, and cultural nuances, ensuring that the essence of the original message remains intact in every translated sentence. Whether it's documents, websites, or multimedia content, our LLM model offers unmatched versatility and reliability, empowering users to communicate effortlessly across languages and borders.
## How to use
### On GPU
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("minhtoan/t5-translate-english-lao")
model = AutoModelForSeq2SeqLM.from_pretrained("minhtoan/t5-translate-english-lao")
model.cuda()
src = "I want to buy a new book"
tokenized_text = tokenizer.encode(src, return_tensors="pt").cuda()
model.eval()
translate_ids = model.generate(tokenized_text, max_length=200)
output = tokenizer.decode(translate_ids[0], skip_special_tokens=True)
output
```
'ຂ້ອຍຢາກຊື້ປຶ້ມໃຫມ່'
### On CPU
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("minhtoan/t5-translate-english-lao")
model = AutoModelForSeq2SeqLM.from_pretrained("minhtoan/t5-translate-english-lao")
src = "I want to buy a new book"
input_ids = tokenizer(src, max_length=140, return_tensors="pt", padding="max_length", truncation=True).input_ids
outputs = model.generate(input_ids=input_ids, max_new_tokens=200)
output = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
output
```
'ຂ້ອຍຢາກຊື້ປຶ້ມໃຫມ່'
## Author
`
Phan Minh Toan
` | {"language": ["en", "lo"], "license": "mit", "library_name": "transformers", "tags": ["translation"], "widget": [{"text": "i love you so much"}], "inference": {"parameters": {"max_length": 200}}, "pipeline_tag": "translation"} | translation | minhtoan/t5-translate-english-lao | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"translation",
"en",
"lo",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T15:38:23+00:00 | [] | [
"en",
"lo"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #translation #en #lo #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # English to Lao Translation Model
Welcome to the forefront of linguistic innovation with our groundbreaking T5 language model designed specifically for English to Lao translation. In a rapidly globalizing world where effective communication is paramount, our T5 model stands as a beacon of excellence, offering unparalleled accuracy, fluency, and efficiency in bridging the language gap between Lao and English.
Built on state-of-the-art deep learning architecture and trained on vast datasets of Lao and English texts, our language model (LLM) harnesses the power of transformer-based technology to deliver seamless and precise translations. Whether you're a business expanding into Laotian markets, a researcher seeking to access Lao-language resources, or an individual connecting with Lao-speaking communities, our T5 model is your ultimate solution for unlocking linguistic barriers and fostering meaningful cross-cultural exchanges.
With a commitment to quality and innovation, our translation model not only translates words but also preserves context, tone, and cultural nuances, ensuring that the essence of the original message remains intact in every translated sentence. Whether it's documents, websites, or multimedia content, our LLM model offers unmatched versatility and reliability, empowering users to communicate effortlessly across languages and borders.
## How to use
### On GPU
'ຂ້ອຍຢາກຊື້ປຶ້ມໃຫມ່'
### On CPU
'ຂ້ອຍຢາກຊື້ປຶ້ມໃຫມ່'
## Author
'
Phan Minh Toan
' | [
"# English to Lao Translation Model\nWelcome to the forefront of linguistic innovation with our groundbreaking T5 language model designed specifically for English to Lao translation. In a rapidly globalizing world where effective communication is paramount, our T5 model stands as a beacon of excellence, offering unparalleled accuracy, fluency, and efficiency in bridging the language gap between Lao and English.\n\nBuilt on state-of-the-art deep learning architecture and trained on vast datasets of Lao and English texts, our language model (LLM) harnesses the power of transformer-based technology to deliver seamless and precise translations. Whether you're a business expanding into Laotian markets, a researcher seeking to access Lao-language resources, or an individual connecting with Lao-speaking communities, our T5 model is your ultimate solution for unlocking linguistic barriers and fostering meaningful cross-cultural exchanges.\n\nWith a commitment to quality and innovation, our translation model not only translates words but also preserves context, tone, and cultural nuances, ensuring that the essence of the original message remains intact in every translated sentence. Whether it's documents, websites, or multimedia content, our LLM model offers unmatched versatility and reliability, empowering users to communicate effortlessly across languages and borders.",
"## How to use",
"### On GPU\n\n'ຂ້ອຍຢາກຊື້ປຶ້ມໃຫມ່'",
"### On CPU\n\n'ຂ້ອຍຢາກຊື້ປຶ້ມໃຫມ່'",
"## Author\n'\nPhan Minh Toan \n'"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #translation #en #lo #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# English to Lao Translation Model\nWelcome to the forefront of linguistic innovation with our groundbreaking T5 language model designed specifically for English to Lao translation. In a rapidly globalizing world where effective communication is paramount, our T5 model stands as a beacon of excellence, offering unparalleled accuracy, fluency, and efficiency in bridging the language gap between Lao and English.\n\nBuilt on state-of-the-art deep learning architecture and trained on vast datasets of Lao and English texts, our language model (LLM) harnesses the power of transformer-based technology to deliver seamless and precise translations. Whether you're a business expanding into Laotian markets, a researcher seeking to access Lao-language resources, or an individual connecting with Lao-speaking communities, our T5 model is your ultimate solution for unlocking linguistic barriers and fostering meaningful cross-cultural exchanges.\n\nWith a commitment to quality and innovation, our translation model not only translates words but also preserves context, tone, and cultural nuances, ensuring that the essence of the original message remains intact in every translated sentence. Whether it's documents, websites, or multimedia content, our LLM model offers unmatched versatility and reliability, empowering users to communicate effortlessly across languages and borders.",
"## How to use",
"### On GPU\n\n'ຂ້ອຍຢາກຊື້ປຶ້ມໃຫມ່'",
"### On CPU\n\n'ຂ້ອຍຢາກຊື້ປຶ້ມໃຫມ່'",
"## Author\n'\nPhan Minh Toan \n'"
] | [
61,
308,
4,
13,
13,
8
] | [
"passage: TAGS\n#transformers #pytorch #mt5 #text2text-generation #translation #en #lo #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# English to Lao Translation Model\nWelcome to the forefront of linguistic innovation with our groundbreaking T5 language model designed specifically for English to Lao translation. In a rapidly globalizing world where effective communication is paramount, our T5 model stands as a beacon of excellence, offering unparalleled accuracy, fluency, and efficiency in bridging the language gap between Lao and English.\n\nBuilt on state-of-the-art deep learning architecture and trained on vast datasets of Lao and English texts, our language model (LLM) harnesses the power of transformer-based technology to deliver seamless and precise translations. Whether you're a business expanding into Laotian markets, a researcher seeking to access Lao-language resources, or an individual connecting with Lao-speaking communities, our T5 model is your ultimate solution for unlocking linguistic barriers and fostering meaningful cross-cultural exchanges.\n\nWith a commitment to quality and innovation, our translation model not only translates words but also preserves context, tone, and cultural nuances, ensuring that the essence of the original message remains intact in every translated sentence. Whether it's documents, websites, or multimedia content, our LLM model offers unmatched versatility and reliability, empowering users to communicate effortlessly across languages and borders.## How to use### On GPU\n\n'ຂ້ອຍຢາກຊື້ປຶ້ມໃຫມ່'### On CPU\n\n'ຂ້ອຍຢາກຊື້ປຶ້ມໃຫມ່'## Author\n'\nPhan Minh Toan \n'"
] | [
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null | null | null |
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` | {"license": "other", "tags": ["autotrain", "text-generation"], "widget": [{"text": "I love AutoTrain because "}]} | text-generation | Tonystark1/Ultimus-Zephyr-7B | [
"safetensors",
"autotrain",
"text-generation",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] | 2024-02-08T15:39:28+00:00 | [] | [] | TAGS
#safetensors #autotrain #text-generation #conversational #license-other #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit AutoTrain.
# Usage
| [
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
"TAGS\n#safetensors #autotrain #text-generation #conversational #license-other #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
37,
29,
3
] | [
"passage: TAGS\n#safetensors #autotrain #text-generation #conversational #license-other #endpoints_compatible #region-us \n# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.# Usage"
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null | null | transformers |
# Model Card for Model ID
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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**BibTeX:**
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# Model Card for Model ID
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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### Model Sources [optional]
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
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## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
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null | null | transformers |
# Uploaded model
- **Developed by:** smotoc
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "gguf"], "base_model": "unsloth/mistral-7b-bnb-4bit"} | null | smotoc/foxy_7B_unsloth_temp | [
"transformers",
"safetensors",
"gguf",
"text-generation-inference",
"unsloth",
"mistral",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-08T15:40:43+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #gguf #text-generation-inference #unsloth #mistral #en #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: smotoc
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: smotoc\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #safetensors #gguf #text-generation-inference #unsloth #mistral #en #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: smotoc\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
69,
79
] | [
"passage: TAGS\n#transformers #safetensors #gguf #text-generation-inference #unsloth #mistral #en #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n# Uploaded model\n\n- Developed by: smotoc\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sparse_sparse_80_percent_pretraining_warmup_20K_0_2_steps_5k
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the openwebtext dataset.
It achieves the following results on the evaluation set:
- Loss: 4.9832
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 0
- distributed_type: multi-GPU
- num_devices: 3
- gradient_accumulation_steps: 2
- total_train_batch_size: 48
- total_eval_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2964 | 0.02 | 50 | 1.2517 |
| 1.1086 | 0.05 | 100 | 1.0714 |
| 0.9727 | 0.07 | 150 | 0.9857 |
| 0.9326 | 0.1 | 200 | 0.9357 |
| 0.8944 | 0.12 | 250 | 0.8988 |
| 0.872 | 0.15 | 300 | 0.8700 |
| 0.8523 | 0.17 | 350 | 0.8516 |
| 0.8369 | 0.19 | 400 | 0.8358 |
| 0.8372 | 0.22 | 450 | 0.8226 |
| 0.8221 | 0.24 | 500 | 0.8116 |
| 0.8093 | 0.27 | 550 | 0.8020 |
| 0.804 | 0.29 | 600 | 0.7937 |
| 0.8111 | 0.32 | 650 | 0.7935 |
| 0.7949 | 0.34 | 700 | 0.7872 |
| 0.7947 | 0.36 | 750 | 0.7815 |
| 0.8045 | 0.39 | 800 | 0.7771 |
| 0.7706 | 0.41 | 850 | 0.7724 |
| 0.7669 | 0.44 | 900 | 0.7683 |
| 0.7691 | 0.46 | 950 | 0.7825 |
| 0.7737 | 0.48 | 1000 | 0.7779 |
| 0.7595 | 0.51 | 1050 | 0.7748 |
| 0.7672 | 0.53 | 1100 | 0.7709 |
| 0.7725 | 0.56 | 1150 | 0.7681 |
| 0.7551 | 0.58 | 1200 | 0.7658 |
| 0.8035 | 0.61 | 1250 | 0.8159 |
| 0.804 | 0.63 | 1300 | 0.8068 |
| 0.8074 | 0.65 | 1350 | 0.8016 |
| 0.7801 | 0.68 | 1400 | 0.7982 |
| 0.7842 | 0.7 | 1450 | 0.7951 |
| 0.7938 | 0.73 | 1500 | 0.7907 |
| 0.8625 | 0.75 | 1550 | 0.8568 |
| 0.8467 | 0.78 | 1600 | 0.8443 |
| 0.8216 | 0.8 | 1650 | 0.8379 |
| 0.8334 | 0.82 | 1700 | 0.8332 |
| 0.8287 | 0.85 | 1750 | 0.8292 |
| 0.8251 | 0.87 | 1800 | 0.8250 |
| 0.8969 | 0.9 | 1850 | 0.8790 |
| 0.8619 | 0.92 | 1900 | 0.8696 |
| 0.8566 | 0.95 | 1950 | 0.8645 |
| 0.8633 | 0.97 | 2000 | 0.8599 |
| 0.8622 | 0.99 | 2050 | 0.8558 |
| 0.8336 | 1.02 | 2100 | 0.8520 |
| 0.918 | 1.04 | 2150 | 0.9045 |
| 0.8755 | 1.07 | 2200 | 0.8960 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["openwebtext"], "base_model": "mistralai/Mistral-7B-Instruct-v0.1", "model-index": [{"name": "sparse_sparse_80_percent_pretraining_warmup_20K_0_2_steps_5k", "results": []}]} | text-generation | thrunlab/sparse_sparse_80_percent_pretraining_warmup_20K_0_2_steps_5k | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"dataset:openwebtext",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T15:42:07+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #generated_from_trainer #dataset-openwebtext #base_model-mistralai/Mistral-7B-Instruct-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| sparse\_sparse\_80\_percent\_pretraining\_warmup\_20K\_0\_2\_steps\_5k
======================================================================
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.1 on the openwebtext dataset.
It achieves the following results on the evaluation set:
* Loss: 4.9832
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 0
* distributed\_type: multi-GPU
* num\_devices: 3
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 48
* total\_eval\_batch\_size: 48
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 5000
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.1+cu121
* Datasets 2.15.0
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 0\n* distributed\\_type: multi-GPU\n* num\\_devices: 3\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 48\n* total\\_eval\\_batch\\_size: 48\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 5000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #generated_from_trainer #dataset-openwebtext #base_model-mistralai/Mistral-7B-Instruct-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 0\n* distributed\\_type: multi-GPU\n* num\\_devices: 3\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 48\n* total\\_eval\\_batch\\_size: 48\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 5000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
88,
159,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #generated_from_trainer #dataset-openwebtext #base_model-mistralai/Mistral-7B-Instruct-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 0\n* distributed\\_type: multi-GPU\n* num\\_devices: 3\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 48\n* total\\_eval\\_batch\\_size: 48\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 5000### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
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null | null | null | ips rssi coor prediction | {} | null | naphatkmitl/ips-rssi | [
"region:us"
] | 2024-02-08T15:49:19+00:00 | [] | [] | TAGS
#region-us
| ips rssi coor prediction | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# INTERNAL_BEST-safety-utcustom-train-SF-RGB-b5
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0472
- Mean Iou: 0.8728
- Mean Accuracy: 0.9195
- Overall Accuracy: 0.9919
- Accuracy Unlabeled: nan
- Accuracy Safe: 0.8426
- Accuracy Unsafe: 0.9964
- Iou Unlabeled: nan
- Iou Safe: 0.7540
- Iou Unsafe: 0.9917
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Safe | Accuracy Unsafe | Iou Unlabeled | Iou Safe | Iou Unsafe |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:|
| 1.2199 | 2.0 | 20 | 1.1474 | 0.0765 | 0.4399 | 0.2168 | nan | 0.6770 | 0.2028 | 0.0 | 0.0279 | 0.2014 |
| 1.1542 | 4.0 | 40 | 1.0616 | 0.1558 | 0.6082 | 0.4365 | nan | 0.7908 | 0.4257 | 0.0 | 0.0436 | 0.4237 |
| 1.0324 | 6.0 | 60 | 0.9140 | 0.2569 | 0.6886 | 0.7091 | nan | 0.6667 | 0.7104 | 0.0 | 0.0672 | 0.7036 |
| 0.8058 | 8.0 | 80 | 0.7629 | 0.2970 | 0.6780 | 0.8115 | nan | 0.5360 | 0.8199 | 0.0 | 0.0823 | 0.8086 |
| 0.681 | 10.0 | 100 | 0.5545 | 0.3510 | 0.6964 | 0.9135 | nan | 0.4657 | 0.9271 | 0.0 | 0.1407 | 0.9123 |
| 0.5248 | 12.0 | 120 | 0.4153 | 0.3738 | 0.6747 | 0.9462 | nan | 0.3861 | 0.9633 | 0.0 | 0.1757 | 0.9456 |
| 0.3372 | 14.0 | 140 | 0.3050 | 0.3896 | 0.6548 | 0.9628 | nan | 0.3276 | 0.9821 | 0.0 | 0.2064 | 0.9624 |
| 0.2818 | 16.0 | 160 | 0.2346 | 0.4313 | 0.7331 | 0.9703 | nan | 0.4810 | 0.9852 | 0.0 | 0.3239 | 0.9699 |
| 0.2081 | 18.0 | 180 | 0.1802 | 0.4762 | 0.7973 | 0.9782 | nan | 0.6050 | 0.9896 | 0.0 | 0.4509 | 0.9778 |
| 0.141 | 20.0 | 200 | 0.1362 | 0.4968 | 0.7945 | 0.9830 | nan | 0.5943 | 0.9948 | 0.0 | 0.5078 | 0.9827 |
| 0.0963 | 22.0 | 220 | 0.1032 | 0.7409 | 0.7652 | 0.9841 | nan | 0.5326 | 0.9979 | nan | 0.4978 | 0.9839 |
| 0.0842 | 24.0 | 240 | 0.0770 | 0.7840 | 0.8200 | 0.9863 | nan | 0.6432 | 0.9968 | nan | 0.5818 | 0.9861 |
| 0.0702 | 26.0 | 260 | 0.0669 | 0.7836 | 0.8193 | 0.9863 | nan | 0.6417 | 0.9968 | nan | 0.5812 | 0.9861 |
| 0.0706 | 28.0 | 280 | 0.0671 | 0.8065 | 0.8593 | 0.9872 | nan | 0.7234 | 0.9953 | nan | 0.6261 | 0.9870 |
| 0.0747 | 30.0 | 300 | 0.0551 | 0.7808 | 0.7980 | 0.9870 | nan | 0.5971 | 0.9988 | nan | 0.5748 | 0.9867 |
| 0.057 | 32.0 | 320 | 0.0492 | 0.8267 | 0.8736 | 0.9888 | nan | 0.7511 | 0.9961 | nan | 0.6648 | 0.9886 |
| 0.0435 | 34.0 | 340 | 0.0507 | 0.7956 | 0.8134 | 0.9878 | nan | 0.6280 | 0.9988 | nan | 0.6035 | 0.9876 |
| 0.0326 | 36.0 | 360 | 0.0418 | 0.8422 | 0.8895 | 0.9898 | nan | 0.7830 | 0.9961 | nan | 0.6947 | 0.9896 |
| 0.0262 | 38.0 | 380 | 0.0420 | 0.8280 | 0.8550 | 0.9895 | nan | 0.7120 | 0.9979 | nan | 0.6667 | 0.9893 |
| 0.0268 | 40.0 | 400 | 0.0392 | 0.8407 | 0.8822 | 0.9899 | nan | 0.7676 | 0.9967 | nan | 0.6918 | 0.9897 |
| 0.0395 | 42.0 | 420 | 0.0466 | 0.5436 | 0.8370 | 0.9889 | nan | 0.6755 | 0.9984 | 0.0 | 0.6422 | 0.9887 |
| 0.0279 | 44.0 | 440 | 0.0439 | 0.8321 | 0.8946 | 0.9887 | nan | 0.7945 | 0.9947 | nan | 0.6758 | 0.9885 |
| 0.0468 | 46.0 | 460 | 0.0360 | 0.8480 | 0.8894 | 0.9904 | nan | 0.7822 | 0.9967 | nan | 0.7059 | 0.9901 |
| 0.0233 | 48.0 | 480 | 0.0376 | 0.8507 | 0.8962 | 0.9905 | nan | 0.7960 | 0.9964 | nan | 0.7113 | 0.9902 |
| 0.0288 | 50.0 | 500 | 0.0386 | 0.8404 | 0.8845 | 0.9898 | nan | 0.7725 | 0.9964 | nan | 0.6913 | 0.9896 |
| 0.0266 | 52.0 | 520 | 0.0361 | 0.8455 | 0.8768 | 0.9905 | nan | 0.7560 | 0.9976 | nan | 0.7008 | 0.9902 |
| 0.0241 | 54.0 | 540 | 0.0367 | 0.8504 | 0.9039 | 0.9902 | nan | 0.8122 | 0.9957 | nan | 0.7109 | 0.9900 |
| 0.0239 | 56.0 | 560 | 0.0414 | 0.8401 | 0.8809 | 0.9899 | nan | 0.7650 | 0.9967 | nan | 0.6906 | 0.9896 |
| 0.0221 | 58.0 | 580 | 0.0375 | 0.8536 | 0.8971 | 0.9907 | nan | 0.7977 | 0.9966 | nan | 0.7167 | 0.9905 |
| 0.0324 | 60.0 | 600 | 0.0390 | 0.8566 | 0.9017 | 0.9908 | nan | 0.8069 | 0.9964 | nan | 0.7226 | 0.9906 |
| 0.0264 | 62.0 | 620 | 0.0405 | 0.8506 | 0.9088 | 0.9901 | nan | 0.8224 | 0.9952 | nan | 0.7114 | 0.9899 |
| 0.0146 | 64.0 | 640 | 0.0356 | 0.8627 | 0.9158 | 0.9911 | nan | 0.8358 | 0.9958 | nan | 0.7346 | 0.9909 |
| 0.0252 | 66.0 | 660 | 0.0310 | 0.8667 | 0.9010 | 0.9917 | nan | 0.8046 | 0.9974 | nan | 0.7418 | 0.9915 |
| 0.0155 | 68.0 | 680 | 0.0359 | 0.8567 | 0.9056 | 0.9908 | nan | 0.8152 | 0.9961 | nan | 0.7229 | 0.9905 |
| 0.0169 | 70.0 | 700 | 0.0490 | 0.8476 | 0.9182 | 0.9896 | nan | 0.8422 | 0.9941 | nan | 0.7058 | 0.9894 |
| 0.0142 | 72.0 | 720 | 0.0357 | 0.8442 | 0.8771 | 0.9903 | nan | 0.7568 | 0.9974 | nan | 0.6982 | 0.9901 |
| 0.0244 | 74.0 | 740 | 0.0400 | 0.8523 | 0.9070 | 0.9903 | nan | 0.8183 | 0.9956 | nan | 0.7146 | 0.9901 |
| 0.016 | 76.0 | 760 | 0.0302 | 0.8644 | 0.9037 | 0.9915 | nan | 0.8105 | 0.9970 | nan | 0.7376 | 0.9913 |
| 0.0137 | 78.0 | 780 | 0.0325 | 0.8664 | 0.9118 | 0.9915 | nan | 0.8271 | 0.9965 | nan | 0.7415 | 0.9913 |
| 0.0115 | 80.0 | 800 | 0.0347 | 0.8678 | 0.9162 | 0.9915 | nan | 0.8362 | 0.9962 | nan | 0.7443 | 0.9913 |
| 0.0117 | 82.0 | 820 | 0.0320 | 0.8697 | 0.9084 | 0.9918 | nan | 0.8197 | 0.9971 | nan | 0.7478 | 0.9916 |
| 0.0108 | 84.0 | 840 | 0.0348 | 0.8691 | 0.9192 | 0.9916 | nan | 0.8423 | 0.9961 | nan | 0.7468 | 0.9913 |
| 0.0101 | 86.0 | 860 | 0.0342 | 0.8683 | 0.9081 | 0.9917 | nan | 0.8192 | 0.9970 | nan | 0.7452 | 0.9915 |
| 0.0085 | 88.0 | 880 | 0.0441 | 0.8639 | 0.9214 | 0.9911 | nan | 0.8474 | 0.9954 | nan | 0.7370 | 0.9908 |
| 0.009 | 90.0 | 900 | 0.0428 | 0.8619 | 0.9086 | 0.9912 | nan | 0.8209 | 0.9963 | nan | 0.7330 | 0.9909 |
| 0.009 | 92.0 | 920 | 0.0444 | 0.8620 | 0.9089 | 0.9912 | nan | 0.8215 | 0.9963 | nan | 0.7331 | 0.9909 |
| 0.0089 | 94.0 | 940 | 0.0410 | 0.8645 | 0.9147 | 0.9913 | nan | 0.8334 | 0.9961 | nan | 0.7380 | 0.9910 |
| 0.0091 | 96.0 | 960 | 0.0418 | 0.8663 | 0.9155 | 0.9914 | nan | 0.8349 | 0.9962 | nan | 0.7413 | 0.9912 |
| 0.0079 | 98.0 | 980 | 0.0398 | 0.8629 | 0.9085 | 0.9913 | nan | 0.8205 | 0.9965 | nan | 0.7348 | 0.9910 |
| 0.0084 | 100.0 | 1000 | 0.0497 | 0.8553 | 0.9109 | 0.9905 | nan | 0.8262 | 0.9955 | nan | 0.7204 | 0.9903 |
| 0.0088 | 102.0 | 1020 | 0.0399 | 0.8558 | 0.9058 | 0.9907 | nan | 0.8156 | 0.9960 | nan | 0.7212 | 0.9905 |
| 0.0089 | 104.0 | 1040 | 0.0388 | 0.8678 | 0.9225 | 0.9914 | nan | 0.8494 | 0.9957 | nan | 0.7444 | 0.9912 |
| 0.008 | 106.0 | 1060 | 0.0449 | 0.8622 | 0.9225 | 0.9909 | nan | 0.8498 | 0.9952 | nan | 0.7337 | 0.9907 |
| 0.0084 | 108.0 | 1080 | 0.0429 | 0.8687 | 0.9233 | 0.9914 | nan | 0.8510 | 0.9957 | nan | 0.7462 | 0.9912 |
| 0.0084 | 110.0 | 1100 | 0.0405 | 0.8687 | 0.9169 | 0.9916 | nan | 0.8375 | 0.9963 | nan | 0.7460 | 0.9914 |
| 0.007 | 112.0 | 1120 | 0.0544 | 0.8620 | 0.9180 | 0.9910 | nan | 0.8404 | 0.9956 | nan | 0.7333 | 0.9907 |
| 0.0079 | 114.0 | 1140 | 0.0501 | 0.8602 | 0.9176 | 0.9908 | nan | 0.8399 | 0.9954 | nan | 0.7299 | 0.9906 |
| 0.0084 | 116.0 | 1160 | 0.0508 | 0.8605 | 0.9212 | 0.9908 | nan | 0.8473 | 0.9951 | nan | 0.7304 | 0.9905 |
| 0.0113 | 118.0 | 1180 | 0.0511 | 0.8601 | 0.9228 | 0.9907 | nan | 0.8507 | 0.9950 | nan | 0.7298 | 0.9905 |
| 0.0076 | 120.0 | 1200 | 0.0556 | 0.8602 | 0.9264 | 0.9906 | nan | 0.8582 | 0.9947 | nan | 0.7299 | 0.9904 |
| 0.0081 | 122.0 | 1220 | 0.0471 | 0.8665 | 0.9256 | 0.9912 | nan | 0.8559 | 0.9953 | nan | 0.7420 | 0.9910 |
| 0.0054 | 124.0 | 1240 | 0.0504 | 0.8652 | 0.9174 | 0.9913 | nan | 0.8389 | 0.9959 | nan | 0.7394 | 0.9910 |
| 0.0054 | 126.0 | 1260 | 0.0502 | 0.8666 | 0.9209 | 0.9913 | nan | 0.8461 | 0.9957 | nan | 0.7420 | 0.9911 |
| 0.0092 | 128.0 | 1280 | 0.0540 | 0.8642 | 0.9223 | 0.9911 | nan | 0.8492 | 0.9954 | nan | 0.7376 | 0.9908 |
| 0.007 | 130.0 | 1300 | 0.0533 | 0.8637 | 0.9207 | 0.9911 | nan | 0.8459 | 0.9955 | nan | 0.7366 | 0.9908 |
| 0.0063 | 132.0 | 1320 | 0.0526 | 0.8644 | 0.9259 | 0.9910 | nan | 0.8567 | 0.9951 | nan | 0.7380 | 0.9908 |
| 0.0101 | 134.0 | 1340 | 0.0462 | 0.8653 | 0.9067 | 0.9915 | nan | 0.8166 | 0.9968 | nan | 0.7393 | 0.9913 |
| 0.0183 | 136.0 | 1360 | 0.0516 | 0.5675 | 0.9024 | 0.9904 | nan | 0.8089 | 0.9959 | 0.0 | 0.7123 | 0.9901 |
| 0.0102 | 138.0 | 1380 | 0.0388 | 0.8366 | 0.8716 | 0.9898 | nan | 0.7460 | 0.9972 | nan | 0.6837 | 0.9896 |
| 0.0277 | 140.0 | 1400 | 0.0649 | 0.8159 | 0.9590 | 0.9850 | nan | 0.9313 | 0.9866 | nan | 0.6472 | 0.9846 |
| 0.0169 | 142.0 | 1420 | 0.0340 | 0.8444 | 0.9148 | 0.9894 | nan | 0.8355 | 0.9941 | nan | 0.6996 | 0.9891 |
| 0.0359 | 144.0 | 1440 | 0.0314 | 0.8667 | 0.8987 | 0.9918 | nan | 0.7997 | 0.9976 | nan | 0.7419 | 0.9916 |
| 0.0117 | 146.0 | 1460 | 0.0307 | 0.8517 | 0.8869 | 0.9908 | nan | 0.7765 | 0.9973 | nan | 0.7129 | 0.9905 |
| 0.0097 | 148.0 | 1480 | 0.0323 | 0.8757 | 0.9070 | 0.9924 | nan | 0.8162 | 0.9977 | nan | 0.7593 | 0.9922 |
| 0.0063 | 150.0 | 1500 | 0.0302 | 0.8808 | 0.9155 | 0.9926 | nan | 0.8335 | 0.9975 | nan | 0.7692 | 0.9924 |
| 0.0085 | 152.0 | 1520 | 0.0352 | 0.8697 | 0.9113 | 0.9918 | nan | 0.8258 | 0.9968 | nan | 0.7478 | 0.9916 |
| 0.0078 | 154.0 | 1540 | 0.0428 | 0.8649 | 0.9190 | 0.9912 | nan | 0.8423 | 0.9957 | nan | 0.7388 | 0.9910 |
| 0.0056 | 156.0 | 1560 | 0.0340 | 0.8709 | 0.9170 | 0.9918 | nan | 0.8376 | 0.9965 | nan | 0.7502 | 0.9916 |
| 0.0063 | 158.0 | 1580 | 0.0359 | 0.8661 | 0.9201 | 0.9913 | nan | 0.8445 | 0.9958 | nan | 0.7412 | 0.9911 |
| 0.0083 | 160.0 | 1600 | 0.0375 | 0.8684 | 0.9186 | 0.9915 | nan | 0.8410 | 0.9961 | nan | 0.7456 | 0.9913 |
| 0.0065 | 162.0 | 1620 | 0.0370 | 0.8699 | 0.9210 | 0.9916 | nan | 0.8459 | 0.9960 | nan | 0.7484 | 0.9914 |
| 0.0063 | 164.0 | 1640 | 0.0388 | 0.8699 | 0.9228 | 0.9916 | nan | 0.8498 | 0.9959 | nan | 0.7484 | 0.9913 |
| 0.0056 | 166.0 | 1660 | 0.0386 | 0.8702 | 0.9238 | 0.9916 | nan | 0.8517 | 0.9958 | nan | 0.7491 | 0.9914 |
| 0.0049 | 168.0 | 1680 | 0.0394 | 0.8703 | 0.9199 | 0.9917 | nan | 0.8436 | 0.9962 | nan | 0.7491 | 0.9914 |
| 0.0054 | 170.0 | 1700 | 0.0400 | 0.8704 | 0.9195 | 0.9917 | nan | 0.8428 | 0.9962 | nan | 0.7494 | 0.9915 |
| 0.0046 | 172.0 | 1720 | 0.0398 | 0.8728 | 0.9187 | 0.9919 | nan | 0.8410 | 0.9965 | nan | 0.7539 | 0.9917 |
| 0.0058 | 174.0 | 1740 | 0.0402 | 0.8711 | 0.9166 | 0.9918 | nan | 0.8367 | 0.9965 | nan | 0.7507 | 0.9916 |
| 0.005 | 176.0 | 1760 | 0.0400 | 0.8720 | 0.9196 | 0.9918 | nan | 0.8428 | 0.9963 | nan | 0.7525 | 0.9916 |
| 0.0061 | 178.0 | 1780 | 0.0417 | 0.8714 | 0.9226 | 0.9917 | nan | 0.8492 | 0.9960 | nan | 0.7513 | 0.9915 |
| 0.0061 | 180.0 | 1800 | 0.0407 | 0.8731 | 0.9249 | 0.9918 | nan | 0.8538 | 0.9960 | nan | 0.7545 | 0.9916 |
| 0.0065 | 182.0 | 1820 | 0.0420 | 0.8712 | 0.9235 | 0.9917 | nan | 0.8511 | 0.9959 | nan | 0.7509 | 0.9914 |
| 0.0045 | 184.0 | 1840 | 0.0421 | 0.8718 | 0.9250 | 0.9917 | nan | 0.8541 | 0.9959 | nan | 0.7522 | 0.9915 |
| 0.0056 | 186.0 | 1860 | 0.0435 | 0.8703 | 0.9157 | 0.9917 | nan | 0.8349 | 0.9965 | nan | 0.7490 | 0.9915 |
| 0.0059 | 188.0 | 1880 | 0.0436 | 0.8707 | 0.9191 | 0.9917 | nan | 0.8419 | 0.9963 | nan | 0.7499 | 0.9915 |
| 0.0042 | 190.0 | 1900 | 0.0436 | 0.8707 | 0.9210 | 0.9917 | nan | 0.8458 | 0.9961 | nan | 0.7499 | 0.9915 |
| 0.006 | 192.0 | 1920 | 0.0426 | 0.8697 | 0.9193 | 0.9916 | nan | 0.8425 | 0.9962 | nan | 0.7480 | 0.9914 |
| 0.0053 | 194.0 | 1940 | 0.0447 | 0.8697 | 0.9199 | 0.9916 | nan | 0.8437 | 0.9961 | nan | 0.7480 | 0.9914 |
| 0.0044 | 196.0 | 1960 | 0.0441 | 0.8710 | 0.9238 | 0.9916 | nan | 0.8516 | 0.9959 | nan | 0.7505 | 0.9914 |
| 0.0049 | 198.0 | 1980 | 0.0453 | 0.8693 | 0.9219 | 0.9915 | nan | 0.8479 | 0.9959 | nan | 0.7473 | 0.9913 |
| 0.0059 | 200.0 | 2000 | 0.0444 | 0.8726 | 0.9233 | 0.9918 | nan | 0.8506 | 0.9961 | nan | 0.7537 | 0.9916 |
| 0.005 | 202.0 | 2020 | 0.0447 | 0.8717 | 0.9256 | 0.9917 | nan | 0.8555 | 0.9958 | nan | 0.7519 | 0.9914 |
| 0.005 | 204.0 | 2040 | 0.0451 | 0.8711 | 0.9227 | 0.9917 | nan | 0.8494 | 0.9960 | nan | 0.7507 | 0.9915 |
| 0.0043 | 206.0 | 2060 | 0.0458 | 0.8707 | 0.9220 | 0.9916 | nan | 0.8480 | 0.9960 | nan | 0.7499 | 0.9914 |
| 0.0043 | 208.0 | 2080 | 0.0462 | 0.8696 | 0.9221 | 0.9916 | nan | 0.8482 | 0.9959 | nan | 0.7479 | 0.9913 |
| 0.0062 | 210.0 | 2100 | 0.0451 | 0.8715 | 0.9211 | 0.9917 | nan | 0.8461 | 0.9962 | nan | 0.7515 | 0.9915 |
| 0.0056 | 212.0 | 2120 | 0.0470 | 0.8706 | 0.9213 | 0.9917 | nan | 0.8466 | 0.9961 | nan | 0.7498 | 0.9914 |
| 0.0049 | 214.0 | 2140 | 0.0480 | 0.8679 | 0.9229 | 0.9914 | nan | 0.8500 | 0.9957 | nan | 0.7447 | 0.9912 |
| 0.0038 | 216.0 | 2160 | 0.0474 | 0.8700 | 0.9194 | 0.9916 | nan | 0.8427 | 0.9962 | nan | 0.7486 | 0.9914 |
| 0.0043 | 218.0 | 2180 | 0.0472 | 0.8693 | 0.9231 | 0.9915 | nan | 0.8503 | 0.9958 | nan | 0.7474 | 0.9913 |
| 0.005 | 220.0 | 2200 | 0.0471 | 0.8695 | 0.9156 | 0.9917 | nan | 0.8348 | 0.9964 | nan | 0.7475 | 0.9915 |
| 0.0041 | 222.0 | 2220 | 0.0472 | 0.8719 | 0.9187 | 0.9918 | nan | 0.8411 | 0.9964 | nan | 0.7522 | 0.9916 |
| 0.0041 | 224.0 | 2240 | 0.0471 | 0.8720 | 0.9219 | 0.9918 | nan | 0.8477 | 0.9962 | nan | 0.7525 | 0.9916 |
| 0.005 | 226.0 | 2260 | 0.0479 | 0.8720 | 0.9191 | 0.9918 | nan | 0.8418 | 0.9964 | nan | 0.7524 | 0.9916 |
| 0.0041 | 228.0 | 2280 | 0.0489 | 0.8706 | 0.9183 | 0.9917 | nan | 0.8403 | 0.9963 | nan | 0.7498 | 0.9915 |
| 0.005 | 230.0 | 2300 | 0.0472 | 0.8728 | 0.9195 | 0.9919 | nan | 0.8426 | 0.9964 | nan | 0.7540 | 0.9917 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
| {"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "INTERNAL_BEST-safety-utcustom-train-SF-RGB-b5", "results": []}]} | image-segmentation | sam1120/INTERNAL_BEST-safety-utcustom-train-SF-RGB-b5 | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"license:other",
"endpoints_compatible",
"region:us"
] | 2024-02-08T15:49:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us
| INTERNAL\_BEST-safety-utcustom-train-SF-RGB-b5
==============================================
This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/safety-utcustom-TRAIN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0472
* Mean Iou: 0.8728
* Mean Accuracy: 0.9195
* Overall Accuracy: 0.9919
* Accuracy Unlabeled: nan
* Accuracy Safe: 0.8426
* Accuracy Unsafe: 0.9964
* Iou Unlabeled: nan
* Iou Safe: 0.7540
* Iou Unsafe: 0.9917
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 2000
### Training results
### Framework versions
* Transformers 4.30.2
* Pytorch 2.0.1+cu117
* Datasets 2.13.1
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 2000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 2000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3"
] | [
48,
116,
4,
33
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 2000### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# INTERNAL_BEST-safety-utcustom-train-SF-RGBD-b5
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0506
- Mean Iou: 0.8519
- Mean Accuracy: 0.9125
- Overall Accuracy: 0.9902
- Accuracy Unlabeled: nan
- Accuracy Safe: 0.8300
- Accuracy Unsafe: 0.9950
- Iou Unlabeled: nan
- Iou Safe: 0.7138
- Iou Unsafe: 0.9899
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Safe | Accuracy Unsafe | Iou Unlabeled | Iou Safe | Iou Unsafe |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:|
| 1.4012 | 2.0 | 20 | 1.1653 | 0.0379 | 0.0923 | 0.0926 | nan | 0.0920 | 0.0926 | 0.0 | 0.0214 | 0.0923 |
| 1.2461 | 4.0 | 40 | 0.9899 | 0.2255 | 0.3670 | 0.6307 | nan | 0.0868 | 0.6473 | 0.0 | 0.0379 | 0.6386 |
| 1.0596 | 6.0 | 60 | 0.7738 | 0.2703 | 0.4188 | 0.7941 | nan | 0.0199 | 0.8177 | 0.0 | 0.0143 | 0.7967 |
| 0.8267 | 8.0 | 80 | 0.6767 | 0.2902 | 0.4512 | 0.8507 | nan | 0.0265 | 0.8758 | 0.0 | 0.0183 | 0.8522 |
| 0.7282 | 10.0 | 100 | 0.5637 | 0.3086 | 0.4776 | 0.9098 | nan | 0.0183 | 0.9370 | 0.0 | 0.0149 | 0.9110 |
| 0.5124 | 12.0 | 120 | 0.4667 | 0.3254 | 0.5053 | 0.9512 | nan | 0.0314 | 0.9792 | 0.0 | 0.0247 | 0.9516 |
| 0.3126 | 14.0 | 140 | 0.3585 | 0.3325 | 0.5147 | 0.9662 | nan | 0.0349 | 0.9945 | 0.0 | 0.0313 | 0.9663 |
| 0.2862 | 16.0 | 160 | 0.2890 | 0.3346 | 0.5168 | 0.9703 | nan | 0.0349 | 0.9988 | 0.0 | 0.0336 | 0.9703 |
| 0.2374 | 18.0 | 180 | 0.2102 | 0.3647 | 0.5637 | 0.9725 | nan | 0.1291 | 0.9982 | 0.0 | 0.1218 | 0.9724 |
| 0.1583 | 20.0 | 200 | 0.1730 | 0.6293 | 0.6574 | 0.9761 | nan | 0.3186 | 0.9961 | nan | 0.2827 | 0.9759 |
| 0.1082 | 22.0 | 220 | 0.1317 | 0.6306 | 0.6566 | 0.9765 | nan | 0.3166 | 0.9966 | nan | 0.2849 | 0.9763 |
| 0.1025 | 24.0 | 240 | 0.1116 | 0.6494 | 0.6766 | 0.9777 | nan | 0.3565 | 0.9967 | nan | 0.3212 | 0.9775 |
| 0.1158 | 26.0 | 260 | 0.0965 | 0.7200 | 0.7978 | 0.9791 | nan | 0.6051 | 0.9905 | nan | 0.4612 | 0.9787 |
| 0.0882 | 28.0 | 280 | 0.0857 | 0.7356 | 0.7857 | 0.9822 | nan | 0.5769 | 0.9946 | nan | 0.4893 | 0.9819 |
| 0.07 | 30.0 | 300 | 0.0829 | 0.6717 | 0.6934 | 0.9799 | nan | 0.3890 | 0.9979 | nan | 0.3637 | 0.9797 |
| 0.0911 | 32.0 | 320 | 0.0677 | 0.7680 | 0.8244 | 0.9843 | nan | 0.6545 | 0.9944 | nan | 0.5521 | 0.9840 |
| 0.0807 | 34.0 | 340 | 0.0696 | 0.7779 | 0.8716 | 0.9834 | nan | 0.7528 | 0.9904 | nan | 0.5727 | 0.9830 |
| 0.0531 | 36.0 | 360 | 0.0611 | 0.7761 | 0.8781 | 0.9829 | nan | 0.7668 | 0.9895 | nan | 0.5698 | 0.9825 |
| 0.0407 | 38.0 | 380 | 0.0567 | 0.7828 | 0.8396 | 0.9854 | nan | 0.6846 | 0.9945 | nan | 0.5805 | 0.9851 |
| 0.0449 | 40.0 | 400 | 0.0639 | 0.7725 | 0.8200 | 0.9851 | nan | 0.6446 | 0.9954 | nan | 0.5602 | 0.9848 |
| 0.0932 | 42.0 | 420 | 0.0503 | 0.7726 | 0.7983 | 0.9861 | nan | 0.5987 | 0.9979 | nan | 0.5593 | 0.9858 |
| 0.0362 | 44.0 | 440 | 0.0634 | 0.7553 | 0.8670 | 0.9805 | nan | 0.7464 | 0.9876 | nan | 0.5306 | 0.9801 |
| 0.0324 | 46.0 | 460 | 0.0501 | 0.8024 | 0.8615 | 0.9867 | nan | 0.7284 | 0.9946 | nan | 0.6184 | 0.9864 |
| 0.036 | 48.0 | 480 | 0.0454 | 0.8010 | 0.8454 | 0.9872 | nan | 0.6947 | 0.9961 | nan | 0.6151 | 0.9869 |
| 0.0356 | 50.0 | 500 | 0.0495 | 0.8061 | 0.8760 | 0.9866 | nan | 0.7585 | 0.9936 | nan | 0.6260 | 0.9863 |
| 0.0333 | 52.0 | 520 | 0.0483 | 0.7743 | 0.8128 | 0.9856 | nan | 0.6292 | 0.9964 | nan | 0.5632 | 0.9853 |
| 0.0277 | 54.0 | 540 | 0.0445 | 0.7714 | 0.7932 | 0.9862 | nan | 0.5880 | 0.9983 | nan | 0.5569 | 0.9859 |
| 0.0298 | 56.0 | 560 | 0.0460 | 0.8034 | 0.8518 | 0.9872 | nan | 0.7078 | 0.9957 | nan | 0.6198 | 0.9869 |
| 0.0256 | 58.0 | 580 | 0.0416 | 0.8181 | 0.8548 | 0.9886 | nan | 0.7126 | 0.9970 | nan | 0.6479 | 0.9883 |
| 0.0336 | 60.0 | 600 | 0.0442 | 0.7957 | 0.8168 | 0.9877 | nan | 0.6351 | 0.9984 | nan | 0.6039 | 0.9875 |
| 0.0283 | 62.0 | 620 | 0.0425 | 0.8141 | 0.8812 | 0.9873 | nan | 0.7684 | 0.9940 | nan | 0.6413 | 0.9870 |
| 0.0198 | 64.0 | 640 | 0.0455 | 0.8059 | 0.8401 | 0.9879 | nan | 0.6830 | 0.9971 | nan | 0.6242 | 0.9876 |
| 0.0181 | 66.0 | 660 | 0.0444 | 0.8144 | 0.8733 | 0.9876 | nan | 0.7519 | 0.9948 | nan | 0.6415 | 0.9873 |
| 0.0188 | 68.0 | 680 | 0.0456 | 0.8179 | 0.8696 | 0.9881 | nan | 0.7436 | 0.9955 | nan | 0.6479 | 0.9878 |
| 0.0165 | 70.0 | 700 | 0.0431 | 0.8208 | 0.8985 | 0.9875 | nan | 0.8040 | 0.9930 | nan | 0.6544 | 0.9872 |
| 0.0184 | 72.0 | 720 | 0.0421 | 0.8165 | 0.8785 | 0.9876 | nan | 0.7625 | 0.9945 | nan | 0.6457 | 0.9874 |
| 0.0336 | 74.0 | 740 | 0.0441 | 0.8081 | 0.8792 | 0.9867 | nan | 0.7650 | 0.9935 | nan | 0.6298 | 0.9864 |
| 0.0165 | 76.0 | 760 | 0.0374 | 0.8200 | 0.8555 | 0.9887 | nan | 0.7139 | 0.9971 | nan | 0.6515 | 0.9885 |
| 0.0127 | 78.0 | 780 | 0.0402 | 0.8222 | 0.8780 | 0.9882 | nan | 0.7608 | 0.9952 | nan | 0.6563 | 0.9880 |
| 0.0152 | 80.0 | 800 | 0.0430 | 0.8230 | 0.8687 | 0.9886 | nan | 0.7413 | 0.9961 | nan | 0.6576 | 0.9883 |
| 0.0143 | 82.0 | 820 | 0.0410 | 0.8087 | 0.8422 | 0.9881 | nan | 0.6873 | 0.9972 | nan | 0.6297 | 0.9878 |
| 0.0134 | 84.0 | 840 | 0.0335 | 0.8429 | 0.8893 | 0.9899 | nan | 0.7823 | 0.9962 | nan | 0.6962 | 0.9897 |
| 0.0122 | 86.0 | 860 | 0.0396 | 0.8312 | 0.8749 | 0.9892 | nan | 0.7534 | 0.9964 | nan | 0.6734 | 0.9890 |
| 0.0126 | 88.0 | 880 | 0.0405 | 0.8341 | 0.8805 | 0.9893 | nan | 0.7649 | 0.9962 | nan | 0.6791 | 0.9891 |
| 0.0121 | 90.0 | 900 | 0.0400 | 0.8390 | 0.8810 | 0.9898 | nan | 0.7654 | 0.9966 | nan | 0.6884 | 0.9895 |
| 0.0104 | 92.0 | 920 | 0.0372 | 0.8453 | 0.8990 | 0.9899 | nan | 0.8024 | 0.9956 | nan | 0.7010 | 0.9896 |
| 0.0128 | 94.0 | 940 | 0.0394 | 0.8411 | 0.8893 | 0.9897 | nan | 0.7825 | 0.9961 | nan | 0.6927 | 0.9895 |
| 0.0124 | 96.0 | 960 | 0.0409 | 0.8395 | 0.8948 | 0.9895 | nan | 0.7943 | 0.9954 | nan | 0.6899 | 0.9892 |
| 0.0095 | 98.0 | 980 | 0.0413 | 0.8258 | 0.8903 | 0.9882 | nan | 0.7863 | 0.9944 | nan | 0.6637 | 0.9880 |
| 0.0147 | 100.0 | 1000 | 0.0468 | 0.8181 | 0.9044 | 0.9870 | nan | 0.8167 | 0.9922 | nan | 0.6496 | 0.9867 |
| 0.0125 | 102.0 | 1020 | 0.0379 | 0.8213 | 0.8961 | 0.9876 | nan | 0.7989 | 0.9933 | nan | 0.6553 | 0.9873 |
| 0.0142 | 104.0 | 1040 | 0.0328 | 0.8449 | 0.9154 | 0.9894 | nan | 0.8366 | 0.9941 | nan | 0.7006 | 0.9892 |
| 0.0101 | 106.0 | 1060 | 0.0428 | 0.8407 | 0.9144 | 0.9891 | nan | 0.8351 | 0.9937 | nan | 0.6927 | 0.9888 |
| 0.0097 | 108.0 | 1080 | 0.0397 | 0.8296 | 0.8847 | 0.9888 | nan | 0.7740 | 0.9953 | nan | 0.6707 | 0.9885 |
| 0.01 | 110.0 | 1100 | 0.0384 | 0.8457 | 0.8935 | 0.9901 | nan | 0.7910 | 0.9961 | nan | 0.7016 | 0.9898 |
| 0.0084 | 112.0 | 1120 | 0.0385 | 0.8421 | 0.8874 | 0.9899 | nan | 0.7784 | 0.9963 | nan | 0.6945 | 0.9896 |
| 0.0086 | 114.0 | 1140 | 0.0413 | 0.8488 | 0.8882 | 0.9905 | nan | 0.7795 | 0.9969 | nan | 0.7074 | 0.9903 |
| 0.0112 | 116.0 | 1160 | 0.0427 | 0.8459 | 0.8942 | 0.9901 | nan | 0.7924 | 0.9961 | nan | 0.7020 | 0.9898 |
| 0.0132 | 118.0 | 1180 | 0.0407 | 0.8510 | 0.9011 | 0.9904 | nan | 0.8062 | 0.9960 | nan | 0.7118 | 0.9901 |
| 0.0084 | 120.0 | 1200 | 0.0432 | 0.8510 | 0.9015 | 0.9903 | nan | 0.8071 | 0.9959 | nan | 0.7118 | 0.9901 |
| 0.008 | 122.0 | 1220 | 0.0431 | 0.8504 | 0.9077 | 0.9901 | nan | 0.8202 | 0.9953 | nan | 0.7109 | 0.9899 |
| 0.0069 | 124.0 | 1240 | 0.0424 | 0.8522 | 0.8982 | 0.9905 | nan | 0.8001 | 0.9963 | nan | 0.7141 | 0.9903 |
| 0.006 | 126.0 | 1260 | 0.0447 | 0.8537 | 0.9114 | 0.9904 | nan | 0.8275 | 0.9953 | nan | 0.7173 | 0.9901 |
| 0.0123 | 128.0 | 1280 | 0.0464 | 0.8529 | 0.9102 | 0.9903 | nan | 0.8250 | 0.9954 | nan | 0.7157 | 0.9901 |
| 0.0073 | 130.0 | 1300 | 0.0441 | 0.8520 | 0.9025 | 0.9904 | nan | 0.8090 | 0.9959 | nan | 0.7139 | 0.9902 |
| 0.0066 | 132.0 | 1320 | 0.0447 | 0.8524 | 0.9086 | 0.9903 | nan | 0.8217 | 0.9954 | nan | 0.7148 | 0.9901 |
| 0.0063 | 134.0 | 1340 | 0.0434 | 0.8546 | 0.9077 | 0.9905 | nan | 0.8197 | 0.9957 | nan | 0.7189 | 0.9903 |
| 0.0068 | 136.0 | 1360 | 0.0475 | 0.8518 | 0.9090 | 0.9902 | nan | 0.8226 | 0.9953 | nan | 0.7135 | 0.9900 |
| 0.0056 | 138.0 | 1380 | 0.0458 | 0.8549 | 0.9122 | 0.9905 | nan | 0.8291 | 0.9954 | nan | 0.7195 | 0.9902 |
| 0.007 | 140.0 | 1400 | 0.0455 | 0.8554 | 0.9126 | 0.9905 | nan | 0.8298 | 0.9954 | nan | 0.7205 | 0.9903 |
| 0.0064 | 142.0 | 1420 | 0.0476 | 0.8542 | 0.9047 | 0.9906 | nan | 0.8133 | 0.9960 | nan | 0.7180 | 0.9903 |
| 0.0065 | 144.0 | 1440 | 0.0437 | 0.8556 | 0.9107 | 0.9906 | nan | 0.8258 | 0.9956 | nan | 0.7210 | 0.9903 |
| 0.005 | 146.0 | 1460 | 0.0455 | 0.8551 | 0.9098 | 0.9905 | nan | 0.8239 | 0.9956 | nan | 0.7198 | 0.9903 |
| 0.005 | 148.0 | 1480 | 0.0458 | 0.8539 | 0.9084 | 0.9905 | nan | 0.8212 | 0.9956 | nan | 0.7175 | 0.9902 |
| 0.0048 | 150.0 | 1500 | 0.0462 | 0.8558 | 0.9041 | 0.9907 | nan | 0.8121 | 0.9962 | nan | 0.7211 | 0.9905 |
| 0.0063 | 152.0 | 1520 | 0.0453 | 0.8560 | 0.9175 | 0.9904 | nan | 0.8400 | 0.9950 | nan | 0.7217 | 0.9902 |
| 0.006 | 154.0 | 1540 | 0.0473 | 0.8531 | 0.9073 | 0.9904 | nan | 0.8190 | 0.9956 | nan | 0.7160 | 0.9902 |
| 0.0043 | 156.0 | 1560 | 0.0448 | 0.8562 | 0.9100 | 0.9906 | nan | 0.8243 | 0.9957 | nan | 0.7220 | 0.9904 |
| 0.0049 | 158.0 | 1580 | 0.0480 | 0.8518 | 0.9137 | 0.9901 | nan | 0.8324 | 0.9949 | nan | 0.7138 | 0.9899 |
| 0.0065 | 160.0 | 1600 | 0.0475 | 0.8556 | 0.9095 | 0.9906 | nan | 0.8233 | 0.9957 | nan | 0.7209 | 0.9903 |
| 0.0052 | 162.0 | 1620 | 0.0479 | 0.8531 | 0.9087 | 0.9904 | nan | 0.8218 | 0.9955 | nan | 0.7161 | 0.9901 |
| 0.0063 | 164.0 | 1640 | 0.0488 | 0.8571 | 0.9115 | 0.9907 | nan | 0.8273 | 0.9956 | nan | 0.7238 | 0.9904 |
| 0.0053 | 166.0 | 1660 | 0.0514 | 0.8515 | 0.9152 | 0.9901 | nan | 0.8357 | 0.9948 | nan | 0.7132 | 0.9898 |
| 0.0046 | 168.0 | 1680 | 0.0476 | 0.8540 | 0.9040 | 0.9906 | nan | 0.8119 | 0.9960 | nan | 0.7177 | 0.9903 |
| 0.0039 | 170.0 | 1700 | 0.0483 | 0.5699 | 0.9121 | 0.9905 | nan | 0.8289 | 0.9954 | 0.0 | 0.7195 | 0.9902 |
| 0.0044 | 172.0 | 1720 | 0.0494 | 0.8550 | 0.9114 | 0.9905 | nan | 0.8273 | 0.9954 | nan | 0.7197 | 0.9902 |
| 0.0051 | 174.0 | 1740 | 0.0503 | 0.8556 | 0.9103 | 0.9906 | nan | 0.8250 | 0.9956 | nan | 0.7208 | 0.9903 |
| 0.0041 | 176.0 | 1760 | 0.0499 | 0.8545 | 0.9118 | 0.9904 | nan | 0.8283 | 0.9954 | nan | 0.7188 | 0.9902 |
| 0.0049 | 178.0 | 1780 | 0.0525 | 0.8541 | 0.9066 | 0.9905 | nan | 0.8174 | 0.9958 | nan | 0.7179 | 0.9903 |
| 0.0048 | 180.0 | 1800 | 0.0496 | 0.8556 | 0.9165 | 0.9904 | nan | 0.8380 | 0.9951 | nan | 0.7210 | 0.9902 |
| 0.008 | 182.0 | 1820 | 0.0487 | 0.8528 | 0.9085 | 0.9904 | nan | 0.8215 | 0.9955 | nan | 0.7155 | 0.9901 |
| 0.0041 | 184.0 | 1840 | 0.0506 | 0.8519 | 0.9125 | 0.9902 | nan | 0.8300 | 0.9950 | nan | 0.7138 | 0.9899 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
| {"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "INTERNAL_BEST-safety-utcustom-train-SF-RGBD-b5", "results": []}]} | image-segmentation | sam1120/INTERNAL_BEST-safety-utcustom-train-SF-RGBD-b5 | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"license:other",
"endpoints_compatible",
"region:us"
] | 2024-02-08T15:52:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us
| INTERNAL\_BEST-safety-utcustom-train-SF-RGBD-b5
===============================================
This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/safety-utcustom-TRAIN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0506
* Mean Iou: 0.8519
* Mean Accuracy: 0.9125
* Overall Accuracy: 0.9902
* Accuracy Unlabeled: nan
* Accuracy Safe: 0.8300
* Accuracy Unsafe: 0.9950
* Iou Unlabeled: nan
* Iou Safe: 0.7138
* Iou Unsafe: 0.9899
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 2000
### Training results
### Framework versions
* Transformers 4.30.2
* Pytorch 2.0.1+cu117
* Datasets 2.13.1
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 2000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 2000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3"
] | [
48,
116,
4,
33
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 2000### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-squad-model3
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 79
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-squad-model3", "results": []}]} | question-answering | varun-v-rao/roberta-base-squad-model3 | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-08T15:52:12+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-base #license-mit #endpoints_compatible #region-us
|
# roberta-base-squad-model3
This model is a fine-tuned version of roberta-base on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 79
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"# roberta-base-squad-model3\n\nThis model is a fine-tuned version of roberta-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 79\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-base #license-mit #endpoints_compatible #region-us \n",
"# roberta-base-squad-model3\n\nThis model is a fine-tuned version of roberta-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 79\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
69,
32,
6,
12,
8,
3,
90,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-base #license-mit #endpoints_compatible #region-us \n# roberta-base-squad-model3\n\nThis model is a fine-tuned version of roberta-base on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 79\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "roberta-base"} | null | vgaraujov/roberta-base-peft-prefix-tuning | [
"peft",
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"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-roberta-base #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
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APA:
## Glossary [optional]
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| {"library_name": "peft", "base_model": "meta-llama/Llama-2-13b-chat-hf"} | null | bmehrba/Llama-2-13b-chat-hf-fine-tuned-adapters_Gpt4_t1_Llama13b_Seed101 | [
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-13b-chat-hf",
"region:us"
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"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-meta-llama/Llama-2-13b-chat-hf #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
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null | null | peft |
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
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## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
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- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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- Compute Region:
- Carbon Emitted:
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[optional]
BibTeX:
APA:
## Glossary [optional]
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## Training procedure
The following 'bitsandbytes' quantization config was used during training:
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- bnb_4bit_quant_type: nf4
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### Framework versions
- PEFT 0.7.0.dev0
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | khanhnto/kyt-dragon-13b | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T15:57:18+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
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"## Glossary [optional]",
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] | [
"TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | stable-baselines3 |
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.28 +/- 0.10", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | bmschopp/a2c-PandaReachDense-v3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-08T16:04:52+00:00 | [] | [] | TAGS
#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing PandaReachDense-v3
This is a trained model of a A2C agent playing PandaReachDense-v3
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
41,
45,
17
] | [
"passage: TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-small-finetuned-xsum
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 1.2294
- Rouge2: 0.0439
- Rougel: 1.149
- Rougelsum: 1.1538
- Gen Len: 14.526
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0 | 1.0 | 625 | nan | 1.2294 | 0.0439 | 1.149 | 1.1538 | 14.526 |
| 0.0 | 2.0 | 1250 | nan | 1.2294 | 0.0439 | 1.149 | 1.1538 | 14.526 |
| 0.0 | 3.0 | 1875 | nan | 1.2294 | 0.0439 | 1.149 | 1.1538 | 14.526 |
| 0.0 | 4.0 | 2500 | nan | 1.2294 | 0.0439 | 1.149 | 1.1538 | 14.526 |
| 0.0 | 5.0 | 3125 | nan | 1.2294 | 0.0439 | 1.149 | 1.1538 | 14.526 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/flan-t5-small", "model-index": [{"name": "flan-t5-small-finetuned-xsum", "results": []}]} | text2text-generation | Americo/flan-t5-small-finetuned-xsum | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T16:05:02+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/flan-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| flan-t5-small-finetuned-xsum
============================
This model is a fine-tuned version of google/flan-t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* Rouge1: 1.2294
* Rouge2: 0.0439
* Rougel: 1.149
* Rougelsum: 1.1538
* Gen Len: 14.526
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/flan-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Tokenizers 0.15.1"
] | [
81,
113,
4,
27
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/flan-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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"transformers",
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# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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- Cloud Provider:
- Compute Region:
- Carbon Emitted:
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null | null | transformers |
# NeuralTrix-7B-v1
NeuralTrix-7B-v1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mlabonne/OmniBeagle-7B](https://huggingface.co/mlabonne/OmniBeagle-7B)
* [flemmingmiguel/MBX-7B-v3](https://huggingface.co/flemmingmiguel/MBX-7B-v3)
* [AiMavenAi/AiMaven-Prometheus](https://huggingface.co/AiMavenAi/AiMaven-Prometheus)
## 🧩 Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
# no parameters necessary for base model
- model: mlabonne/OmniBeagle-7B
parameters:
density: 0.65
weight: 0.4
- model: flemmingmiguel/MBX-7B-v3
parameters:
density: 0.6
weight: 0.35
- model: AiMavenAi/AiMaven-Prometheus
parameters:
density: 0.6
weight: 0.35
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
int8_mask: true
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "CultriX/NeuralTrix-7B-v1"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "mlabonne/OmniBeagle-7B", "flemmingmiguel/MBX-7B-v3", "AiMavenAi/AiMaven-Prometheus"], "base_model": ["mlabonne/OmniBeagle-7B", "flemmingmiguel/MBX-7B-v3", "AiMavenAi/AiMaven-Prometheus"]} | text-generation | CultriX/NeuralTrix-7B-v1 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"mlabonne/OmniBeagle-7B",
"flemmingmiguel/MBX-7B-v3",
"AiMavenAi/AiMaven-Prometheus",
"base_model:mlabonne/OmniBeagle-7B",
"base_model:flemmingmiguel/MBX-7B-v3",
"base_model:AiMavenAi/AiMaven-Prometheus",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T16:09:29+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mlabonne/OmniBeagle-7B #flemmingmiguel/MBX-7B-v3 #AiMavenAi/AiMaven-Prometheus #base_model-mlabonne/OmniBeagle-7B #base_model-flemmingmiguel/MBX-7B-v3 #base_model-AiMavenAi/AiMaven-Prometheus #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# NeuralTrix-7B-v1
NeuralTrix-7B-v1 is a merge of the following models using LazyMergekit:
* mlabonne/OmniBeagle-7B
* flemmingmiguel/MBX-7B-v3
* AiMavenAi/AiMaven-Prometheus
## Configuration
## Usage
| [
"# NeuralTrix-7B-v1\n\nNeuralTrix-7B-v1 is a merge of the following models using LazyMergekit:\n* mlabonne/OmniBeagle-7B\n* flemmingmiguel/MBX-7B-v3\n* AiMavenAi/AiMaven-Prometheus",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mlabonne/OmniBeagle-7B #flemmingmiguel/MBX-7B-v3 #AiMavenAi/AiMaven-Prometheus #base_model-mlabonne/OmniBeagle-7B #base_model-flemmingmiguel/MBX-7B-v3 #base_model-AiMavenAi/AiMaven-Prometheus #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# NeuralTrix-7B-v1\n\nNeuralTrix-7B-v1 is a merge of the following models using LazyMergekit:\n* mlabonne/OmniBeagle-7B\n* flemmingmiguel/MBX-7B-v3\n* AiMavenAi/AiMaven-Prometheus",
"## Configuration",
"## Usage"
] | [
156,
72,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mlabonne/OmniBeagle-7B #flemmingmiguel/MBX-7B-v3 #AiMavenAi/AiMaven-Prometheus #base_model-mlabonne/OmniBeagle-7B #base_model-flemmingmiguel/MBX-7B-v3 #base_model-AiMavenAi/AiMaven-Prometheus #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# NeuralTrix-7B-v1\n\nNeuralTrix-7B-v1 is a merge of the following models using LazyMergekit:\n* mlabonne/OmniBeagle-7B\n* flemmingmiguel/MBX-7B-v3\n* AiMavenAi/AiMaven-Prometheus## Configuration## Usage"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-lora-1.57M-squad-model2
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 65
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "roberta-large", "model-index": [{"name": "roberta-large-lora-1.57M-squad-model2", "results": []}]} | question-answering | varun-v-rao/roberta-large-lora-1.57M-squad-model2 | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:roberta-large",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-08T16:15:55+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-large #license-mit #endpoints_compatible #region-us
|
# roberta-large-lora-1.57M-squad-model2
This model is a fine-tuned version of roberta-large on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 65
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"# roberta-large-lora-1.57M-squad-model2\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 65\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-large #license-mit #endpoints_compatible #region-us \n",
"# roberta-large-lora-1.57M-squad-model2\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 65\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
70,
41,
6,
12,
8,
3,
90,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-large #license-mit #endpoints_compatible #region-us \n# roberta-large-lora-1.57M-squad-model2\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 65\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: croissantllm/CroissantLLMBase
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizerFast
is_llama_derived_model: true
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: manu/mmlu_auxiliary_train_formatted_extra
split: train
type: completion
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./out
sequence_len: 2048
sample_packing: false
pad_to_sequence_len: false
adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 2
micro_batch_size: 24
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
flash_attn_cross_entropy: false
flash_attn_rms_norm: true
flash_attn_fuse_qkv: false
flash_attn_fuse_mlp: true
warmup_steps: 50
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: #deepspeed_configs/zero2.json # multi-gpu only
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
```
</details><br>
# out
This model is a fine-tuned version of [croissantllm/CroissantLLMBase](https://huggingface.co/croissantllm/CroissantLLMBase) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4229
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.2302 | 0.0 | 1 | 3.1796 |
| 2.3772 | 0.25 | 134 | 2.3697 |
| 1.8538 | 0.5 | 268 | 1.8599 |
| 1.5117 | 0.75 | 402 | 1.4229 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "croissantllm/CroissantLLMBase", "model-index": [{"name": "out", "results": []}]} | text-generation | manu/croissant_mmlu | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:croissantllm/CroissantLLMBase",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T16:18:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #llama #text-generation #generated_from_trainer #base_model-croissantllm/CroissantLLMBase #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| <img src="URL alt="Built with Axolotl" width="200" height="32"/>
See axolotl config
axolotl version: '0.4.0'
out
===
This model is a fine-tuned version of croissantllm/CroissantLLMBase on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4229
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 24
* eval\_batch\_size: 24
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 48
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.38.0.dev0
* Pytorch 2.1.2+cu121
* Datasets 2.16.1
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 48\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 48\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
83,
144,
4,
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"passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #llama #text-generation #generated_from_trainer #base_model-croissantllm/CroissantLLMBase #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 48\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
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null | null | transformers |
# Model Card for Model ID
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"transformers",
"safetensors",
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"1910.09700"
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#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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### Model Sources [optional]
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## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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] |
null | null | peft |
# Model Card for Model ID
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### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "mistralai/Mistral-7B-Instruct-v0.1"} | null | razla/Mistral7b-4 | [
"peft",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"region:us"
] | 2024-02-08T16:24:06+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-Instruct-v0.1 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
- PEFT 0.7.1 | [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.1"
] | [
"TAGS\n#peft #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-Instruct-v0.1 #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.1"
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"passage: TAGS\n#peft #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-Instruct-v0.1 #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.7.1"
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] |
null | null | transformers | A Moe model built on top of Qwen1.5-7B-Chat, Qwen1.5-7B and Crystalcareai/CrystalQwen-1.5-7B, then qlora via mlx
```
pip install mlx-moe
```
```
python -m mlx_moe.generate --model mzbac/qwen-1.5-2x3-sft-hf-4bit-mlx --prompt "how backpropagation works?" --eos-token "<|im_end|>"
``` | {} | text-generation | mzbac/qwen-1.5-2x3-sft-hf-4bit-mlx | [
"transformers",
"safetensors",
"qwen2moe",
"text-generation",
"conversational",
"custom_code",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T16:24:55+00:00 | [] | [] | TAGS
#transformers #safetensors #qwen2moe #text-generation #conversational #custom_code #autotrain_compatible #endpoints_compatible #region-us
| A Moe model built on top of Qwen1.5-7B-Chat, Qwen1.5-7B and Crystalcareai/CrystalQwen-1.5-7B, then qlora via mlx
| [] | [
"TAGS\n#transformers #safetensors #qwen2moe #text-generation #conversational #custom_code #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
50
] | [
"passage: TAGS\n#transformers #safetensors #qwen2moe #text-generation #conversational #custom_code #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-classification | technocrat3128/sentiment_analysis_FB_roberta_fine_tune_hashtag_removed | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T16:24:57+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #roberta #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
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"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #roberta #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #roberta #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [hyeogi/SOLAR-10.7B-dpo-v1](https://huggingface.co/hyeogi/SOLAR-10.7B-dpo-v1)
* [LDCC/LDCC-SOLAR-10.7B](https://huggingface.co/LDCC/LDCC-SOLAR-10.7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: LDCC/LDCC-SOLAR-10.7B
layer_range: [0, 48]
- model: hyeogi/SOLAR-10.7B-dpo-v1
layer_range: [0, 48]
merge_method: slerp
tokenizer_source: base
base_model: LDCC/LDCC-SOLAR-10.7B
embed_slerp: true
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## Datasets
Finetuned using LoRA with [kyujinpy/OpenOrca-KO](https://huggingface.co/datasets/kyujinpy/OpenOrca-KO) | {"language": ["ko"], "license": "apache-2.0", "tags": ["mergekit", "merge", "LDCC/LDCC-SOLAR-10.7B", "hyeogi/SOLAR-10.7B-dpo-v1"], "base_model": ["LDCC/LDCC-SOLAR-10.7B", "hyeogi/SOLAR-10.7B-dpo-v1"]} | text-generation | jumtul/LDCC-Hyeogi.05 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"LDCC/LDCC-SOLAR-10.7B",
"hyeogi/SOLAR-10.7B-dpo-v1",
"ko",
"base_model:LDCC/LDCC-SOLAR-10.7B",
"base_model:hyeogi/SOLAR-10.7B-dpo-v1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T16:25:02+00:00 | [] | [
"ko"
] | TAGS
#transformers #safetensors #llama #text-generation #mergekit #merge #LDCC/LDCC-SOLAR-10.7B #hyeogi/SOLAR-10.7B-dpo-v1 #ko #base_model-LDCC/LDCC-SOLAR-10.7B #base_model-hyeogi/SOLAR-10.7B-dpo-v1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* hyeogi/SOLAR-10.7B-dpo-v1
* LDCC/LDCC-SOLAR-10.7B
### Configuration
The following YAML configuration was used to produce this model:
## Datasets
Finetuned using LoRA with kyujinpy/OpenOrca-KO | [
"# merge\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* hyeogi/SOLAR-10.7B-dpo-v1\n* LDCC/LDCC-SOLAR-10.7B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:",
"## Datasets\n\nFinetuned using LoRA with kyujinpy/OpenOrca-KO"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #LDCC/LDCC-SOLAR-10.7B #hyeogi/SOLAR-10.7B-dpo-v1 #ko #base_model-LDCC/LDCC-SOLAR-10.7B #base_model-hyeogi/SOLAR-10.7B-dpo-v1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merge\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* hyeogi/SOLAR-10.7B-dpo-v1\n* LDCC/LDCC-SOLAR-10.7B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:",
"## Datasets\n\nFinetuned using LoRA with kyujinpy/OpenOrca-KO"
] | [
132,
18,
4,
18,
46,
17,
21
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #LDCC/LDCC-SOLAR-10.7B #hyeogi/SOLAR-10.7B-dpo-v1 #ko #base_model-LDCC/LDCC-SOLAR-10.7B #base_model-hyeogi/SOLAR-10.7B-dpo-v1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# merge\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the SLERP merge method.### Models Merged\n\nThe following models were included in the merge:\n* hyeogi/SOLAR-10.7B-dpo-v1\n* LDCC/LDCC-SOLAR-10.7B### Configuration\n\nThe following YAML configuration was used to produce this model:## Datasets\n\nFinetuned using LoRA with kyujinpy/OpenOrca-KO"
] | [
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null | null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - sdubail/alanmarmot_LoRA_DS_extended
<Gallery />
## Model description
These are sdubail/alanmarmot_LoRA_DS_extended LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of alanmarmot to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](sdubail/alanmarmot_LoRA_DS_extended/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of alanmarmot", "widget": []} | text-to-image | sdubail/alanmarmot_LoRA_DS_extended | [
"diffusers",
"tensorboard",
"text-to-image",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"has_space",
"region:us"
] | 2024-02-08T16:30:17+00:00 | [] | [] | TAGS
#diffusers #tensorboard #text-to-image #stable-diffusion-xl #stable-diffusion-xl-diffusers #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us
|
# SDXL LoRA DreamBooth - sdubail/alanmarmot_LoRA_DS_extended
<Gallery />
## Model description
These are sdubail/alanmarmot_LoRA_DS_extended LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using DreamBooth.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of alanmarmot to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | [
"# SDXL LoRA DreamBooth - sdubail/alanmarmot_LoRA_DS_extended\n\n<Gallery />",
"## Model description\n\nThese are sdubail/alanmarmot_LoRA_DS_extended LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.",
"## Trigger words\n\nYou should use a photo of alanmarmot to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Training details\n\n[TODO: describe the data used to train the model]"
] | [
"TAGS\n#diffusers #tensorboard #text-to-image #stable-diffusion-xl #stable-diffusion-xl-diffusers #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n",
"# SDXL LoRA DreamBooth - sdubail/alanmarmot_LoRA_DS_extended\n\n<Gallery />",
"## Model description\n\nThese are sdubail/alanmarmot_LoRA_DS_extended LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.",
"## Trigger words\n\nYou should use a photo of alanmarmot to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Training details\n\n[TODO: describe the data used to train the model]"
] | [
86,
30,
95,
19,
28,
9,
5,
24,
16
] | [
"passage: TAGS\n#diffusers #tensorboard #text-to-image #stable-diffusion-xl #stable-diffusion-xl-diffusers #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n# SDXL LoRA DreamBooth - sdubail/alanmarmot_LoRA_DS_extended\n\n<Gallery />## Model description\n\nThese are sdubail/alanmarmot_LoRA_DS_extended LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.## Trigger words\n\nYou should use a photo of alanmarmot to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.## Intended uses & limitations#### How to use#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]## Training details\n\n[TODO: describe the data used to train the model]"
] | [
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null | null | transformers |
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 1.5556857585906982
f1_macro: 0.2
f1_micro: 0.2631578947368421
f1_weighted: 0.21052631578947367
precision_macro: 0.27272727272727276
precision_micro: 0.2631578947368421
precision_weighted: 0.2535885167464115
recall_macro: 0.22666666666666666
recall_micro: 0.2631578947368421
recall_weighted: 0.2631578947368421
accuracy: 0.2631578947368421
| {"tags": ["autotrain", "image-classification"], "datasets": ["autotrain-vhhui-btea8/autotrain-data"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]} | image-classification | basavaakash002/autotrain-vhhui-btea8 | [
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain",
"dataset:autotrain-vhhui-btea8/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T16:30:23+00:00 | [] | [] | TAGS
#transformers #safetensors #vit #image-classification #autotrain #dataset-autotrain-vhhui-btea8/autotrain-data #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 1.5556857585906982
f1_macro: 0.2
f1_micro: 0.2631578947368421
f1_weighted: 0.21052631578947367
precision_macro: 0.27272727272727276
precision_micro: 0.2631578947368421
precision_weighted: 0.2535885167464115
recall_macro: 0.22666666666666666
recall_micro: 0.2631578947368421
recall_weighted: 0.2631578947368421
accuracy: 0.2631578947368421
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Image Classification",
"## Validation Metricsg\nloss: 1.5556857585906982\n\nf1_macro: 0.2\n\nf1_micro: 0.2631578947368421\n\nf1_weighted: 0.21052631578947367\n\nprecision_macro: 0.27272727272727276\n\nprecision_micro: 0.2631578947368421\n\nprecision_weighted: 0.2535885167464115\n\nrecall_macro: 0.22666666666666666\n\nrecall_micro: 0.2631578947368421\n\nrecall_weighted: 0.2631578947368421\n\naccuracy: 0.2631578947368421"
] | [
"TAGS\n#transformers #safetensors #vit #image-classification #autotrain #dataset-autotrain-vhhui-btea8/autotrain-data #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Image Classification",
"## Validation Metricsg\nloss: 1.5556857585906982\n\nf1_macro: 0.2\n\nf1_micro: 0.2631578947368421\n\nf1_weighted: 0.21052631578947367\n\nprecision_macro: 0.27272727272727276\n\nprecision_micro: 0.2631578947368421\n\nprecision_weighted: 0.2535885167464115\n\nrecall_macro: 0.22666666666666666\n\nrecall_micro: 0.2631578947368421\n\nrecall_weighted: 0.2631578947368421\n\naccuracy: 0.2631578947368421"
] | [
61,
16,
142
] | [
"passage: TAGS\n#transformers #safetensors #vit #image-classification #autotrain #dataset-autotrain-vhhui-btea8/autotrain-data #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoTrain\n\n- Problem type: Image Classification## Validation Metricsg\nloss: 1.5556857585906982\n\nf1_macro: 0.2\n\nf1_micro: 0.2631578947368421\n\nf1_weighted: 0.21052631578947367\n\nprecision_macro: 0.27272727272727276\n\nprecision_micro: 0.2631578947368421\n\nprecision_weighted: 0.2535885167464115\n\nrecall_macro: 0.22666666666666666\n\nrecall_micro: 0.2631578947368421\n\nrecall_weighted: 0.2631578947368421\n\naccuracy: 0.2631578947368421"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_AsymmetricLoss_25K_bs64_P4_N1
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6203
- Accuracy: 0.7448
- Precision: 0.0101
- Recall: 0.2592
- F1: 0.0194
- Hamming: 0.2552
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 40
- eval_batch_size: 40
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Hamming |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.6901 | 0.0 | 5 | 0.6457 | 0.6626 | 0.0099 | 0.3394 | 0.0192 | 0.3374 |
| 0.6344 | 0.0 | 10 | 0.6203 | 0.7448 | 0.0101 | 0.2592 | 0.0194 | 0.2552 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.14.1
| {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "allenai/scibert_scivocab_uncased", "model-index": [{"name": "test_AsymmetricLoss_25K_bs64_P4_N1", "results": []}]} | text-classification | bdpc/test_AsymmetricLoss_25K_bs64_P4_N1 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:allenai/scibert_scivocab_uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T16:32:27+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us
| test\_AsymmetricLoss\_25K\_bs64\_P4\_N1
=======================================
This model is a fine-tuned version of allenai/scibert\_scivocab\_uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6203
* Accuracy: 0.7448
* Precision: 0.0101
* Recall: 0.2592
* F1: 0.0194
* Hamming: 0.2552
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 40
* eval\_batch\_size: 40
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 10
### Training results
### Framework versions
* Transformers 4.35.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.7.1
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 40\n* eval\\_batch\\_size: 40\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.7.1\n* Tokenizers 0.14.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 40\n* eval\\_batch\\_size: 40\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.7.1\n* Tokenizers 0.14.1"
] | [
61,
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"passage: TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 40\n* eval\\_batch\\_size: 40\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 10### Training results### Framework versions\n\n\n* Transformers 4.35.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.7.1\n* Tokenizers 0.14.1"
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null | null | transformers | <p>
This is the organization grouping all the models and datasets used in the <a href="https://huggingface.co/course/chapter1" class="underline">Hugging Face course</a>.
</p> | {} | token-classification | ramin153/bert-per-ai-token-classification | [
"transformers",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T16:35:04+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| <p>
This is the organization grouping all the models and datasets used in the <a href="URL class="underline">Hugging Face course</a>.
</p> | [] | [
"TAGS\n#transformers #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
38
] | [
"passage: TAGS\n#transformers #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | bitsoko/gumzo-tiny-00 | [
"transformers",
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"llama",
"text-generation",
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|
# Model Card for Model ID
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## How to Get Started with the Model
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## Training Details
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### Training Procedure
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#### Speeds, Sizes, Times [optional]
## Evaluation
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#### Testing Data
#### Factors
#### Metrics
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec_RTSplit0208_20
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-japanese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0148
- Wer: 0.2021
- Cer: 0.1741
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 3.7322 | 1.0 | 120 | 3.5160 | 1.0 | 0.9489 |
| 1.4724 | 2.0 | 240 | 1.3042 | 0.9991 | 0.6951 |
| 0.8148 | 3.0 | 360 | 0.6661 | 0.8175 | 0.5193 |
| 0.527 | 4.0 | 480 | 0.4013 | 0.6019 | 0.3703 |
| 0.3803 | 5.0 | 600 | 0.2061 | 0.4203 | 0.2173 |
| 0.253 | 6.0 | 720 | 0.1038 | 0.3175 | 0.2049 |
| 0.2069 | 7.0 | 840 | 0.0560 | 0.3128 | 0.1882 |
| 0.1898 | 8.0 | 960 | 0.0380 | 0.2984 | 0.1802 |
| 0.1484 | 9.0 | 1080 | 0.0204 | 0.2139 | 0.1662 |
| 0.0758 | 10.0 | 1200 | 0.0148 | 0.2021 | 0.1741 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "model-index": [{"name": "wav2vec_RTSplit0208_20", "results": []}]} | automatic-speech-recognition | tndklab/wav2vec_RTSplit0208_20 | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:jonatasgrosman/wav2vec2-large-xlsr-53-japanese",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-08T16:36:44+00:00 | [] | [] | TAGS
#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec\_RTSplit0208\_20
========================
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-japanese on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0148
* Wer: 0.2021
* Cer: 0.1741
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 6e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
80,
116,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | Prajvi/Llama2_7B_qlora_FT_bush_crisis | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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## Evaluation
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null | null | transformers |
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 1.574344515800476
f1_macro: 0.08333333333333334
f1_micro: 0.2631578947368421
f1_weighted: 0.10964912280701755
precision_macro: 0.05263157894736842
precision_micro: 0.2631578947368421
precision_weighted: 0.06925207756232686
recall_macro: 0.2
recall_micro: 0.2631578947368421
recall_weighted: 0.2631578947368421
accuracy: 0.2631578947368421
| {"tags": ["autotrain", "image-classification"], "datasets": ["autotrain-z7maa-wyroe/autotrain-data"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]} | image-classification | basavaakash002/autotrain-z7maa-wyroe | [
"transformers",
"safetensors",
"swin",
"image-classification",
"autotrain",
"dataset:autotrain-z7maa-wyroe/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T16:45:48+00:00 | [] | [] | TAGS
#transformers #safetensors #swin #image-classification #autotrain #dataset-autotrain-z7maa-wyroe/autotrain-data #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 1.574344515800476
f1_macro: 0.08333333333333334
f1_micro: 0.2631578947368421
f1_weighted: 0.10964912280701755
precision_macro: 0.05263157894736842
precision_micro: 0.2631578947368421
precision_weighted: 0.06925207756232686
recall_macro: 0.2
recall_micro: 0.2631578947368421
recall_weighted: 0.2631578947368421
accuracy: 0.2631578947368421
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Image Classification",
"## Validation Metricsg\nloss: 1.574344515800476\n\nf1_macro: 0.08333333333333334\n\nf1_micro: 0.2631578947368421\n\nf1_weighted: 0.10964912280701755\n\nprecision_macro: 0.05263157894736842\n\nprecision_micro: 0.2631578947368421\n\nprecision_weighted: 0.06925207756232686\n\nrecall_macro: 0.2\n\nrecall_micro: 0.2631578947368421\n\nrecall_weighted: 0.2631578947368421\n\naccuracy: 0.2631578947368421"
] | [
"TAGS\n#transformers #safetensors #swin #image-classification #autotrain #dataset-autotrain-z7maa-wyroe/autotrain-data #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Image Classification",
"## Validation Metricsg\nloss: 1.574344515800476\n\nf1_macro: 0.08333333333333334\n\nf1_micro: 0.2631578947368421\n\nf1_weighted: 0.10964912280701755\n\nprecision_macro: 0.05263157894736842\n\nprecision_micro: 0.2631578947368421\n\nprecision_weighted: 0.06925207756232686\n\nrecall_macro: 0.2\n\nrecall_micro: 0.2631578947368421\n\nrecall_weighted: 0.2631578947368421\n\naccuracy: 0.2631578947368421"
] | [
63,
16,
140
] | [
"passage: TAGS\n#transformers #safetensors #swin #image-classification #autotrain #dataset-autotrain-z7maa-wyroe/autotrain-data #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoTrain\n\n- Problem type: Image Classification## Validation Metricsg\nloss: 1.574344515800476\n\nf1_macro: 0.08333333333333334\n\nf1_micro: 0.2631578947368421\n\nf1_weighted: 0.10964912280701755\n\nprecision_macro: 0.05263157894736842\n\nprecision_micro: 0.2631578947368421\n\nprecision_weighted: 0.06925207756232686\n\nrecall_macro: 0.2\n\nrecall_micro: 0.2631578947368421\n\nrecall_weighted: 0.2631578947368421\n\naccuracy: 0.2631578947368421"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | token-classification | sahillihas/merged-model-distil-bert-finetuned-ner | [
"transformers",
"safetensors",
"distilbert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T16:46:30+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #distilbert #token-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
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## Uses
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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- Hardware Type:
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[optional]
BibTeX:
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"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"passage: TAGS\n#transformers #safetensors #distilbert #token-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | null |
MHENN cat (generated by pixlr)
the primary base of all models is mistral-instruct-v0.1
this is a continuous fine-tune of mistral and is MHENN4 fine-tuned further. all models are continuous finetunes and started at the original MHENN version
| {"language": ["en"], "license": "cc", "tags": ["biology", "chemistry", "code", "text-generation-inference"], "datasets": ["netcat420/MHENN5"], "pipeline_tag": "text-generation"} | text-generation | netcat420/MHENN5-GGUF | [
"gguf",
"biology",
"chemistry",
"code",
"text-generation-inference",
"text-generation",
"en",
"dataset:netcat420/MHENN5",
"license:cc",
"region:us"
] | 2024-02-08T16:49:15+00:00 | [] | [
"en"
] | TAGS
#gguf #biology #chemistry #code #text-generation-inference #text-generation #en #dataset-netcat420/MHENN5 #license-cc #region-us
|
!image/png
MHENN cat (generated by pixlr)
the primary base of all models is mistral-instruct-v0.1
this is a continuous fine-tune of mistral and is MHENN4 fine-tuned further. all models are continuous finetunes and started at the original MHENN version
| [] | [
"TAGS\n#gguf #biology #chemistry #code #text-generation-inference #text-generation #en #dataset-netcat420/MHENN5 #license-cc #region-us \n"
] | [
51
] | [
"passage: TAGS\n#gguf #biology #chemistry #code #text-generation-inference #text-generation #en #dataset-netcat420/MHENN5 #license-cc #region-us \n"
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null | null | null |
# Wiedervereinigung-7b-dpo
This is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.
It is a merge of the best german 7B models with 7b parameters as a dare_ties merge.
Since the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model.
Therefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo
using our german fork of [LLaMA-Factory](https://github.com/mayflower/LLaMA-Factory-de).
## mt-bench-de
Is the merged model good? Well, of course. But it is even better with the help of some dpo tuning.
```json
{
"first_turn": 7.3,
"second_turn": 6.925,
"categories": {
"writing": 8.425,
"roleplay": 8.6,
"reasoning": 5.4,
"math": 4.35,
"coding": 4.3,
"extraction": 7.975,
"stem": 8.5,
"humanities": 9.35
},
"average": 7.1125
}
```
Wiedervereinigung-7b itself is a [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing) merge of:
* [DiscoResearch/DiscoLM_German_7b_v1](https://huggingface.co/DiscoResearch/DiscoLM_German_7b_v1)
* [DRXD1000/Phoenix](https://huggingface.co/DRXD1000/Phoenix)
* [VAGOsolutions/SauerkrautLM-7b-v1-mistral](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1-mistral)
* [malteos/hermeo-7b](https://huggingface.co/malteos/hermeo-7b)
All the actual heavylifting has been done by the creators of these models.
## 🧩 Configuration
```yaml
models:
- model: LeoLM/leo-mistral-hessianai-7b
# No parameters necessary for base model
- model: DiscoResearch/DiscoLM_German_7b_v1
parameters:
density: 0.6
weight: 0.25
- model: DRXD1000/Phoenix
parameters:
density: 0.6
weight: 0.25
- model: VAGOsolutions/SauerkrautLM-7b-v1-mistral
parameters:
density: 0.6
weight: 0.25
- model: malteos/hermeo-7b
parameters:
density: 0.6
weight: 0.25
merge_method: dare_ties
base_model: LeoLM/leo-mistral-hessianai-7b
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mayflowergmbh/Wiedervereinigung-7b-dpo"
messages = [{"role": "user", "content": "Was ist ein deutsches large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
***
Vanilla Quantization by [nold](https://huggingface.co/nold), Model by [mayflowergmbh](https://huggingface.co/mayflowergmbh/Wiedervereinigung-7b-dpo). Created using [llm-quantizer](https://github.com/Nold360/llm-quantizer) Pipeline - 4bc844478df79ecfd72815473b30ae09499e179d
| {"language": ["de", "en"], "license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "DiscoResearch/DiscoLM_German_7b_v1", "DRXD1000/Phoenix", "VAGOsolutions/SauerkrautLM-7b-v1-mistral", "malteos/hermeo-7b"], "base_model": ["DiscoResearch/DiscoLM_German_7b_v1", "DRXD1000/Phoenix", "VAGOsolutions/SauerkrautLM-7b-v1-mistral", "malteos/hermeo-7b"]} | null | nold/Wiedervereinigung-7b-dpo-GGUF | [
"gguf",
"merge",
"mergekit",
"lazymergekit",
"DiscoResearch/DiscoLM_German_7b_v1",
"DRXD1000/Phoenix",
"VAGOsolutions/SauerkrautLM-7b-v1-mistral",
"malteos/hermeo-7b",
"de",
"en",
"base_model:DiscoResearch/DiscoLM_German_7b_v1",
"base_model:DRXD1000/Phoenix",
"base_model:VAGOsolutions/SauerkrautLM-7b-v1-mistral",
"base_model:malteos/hermeo-7b",
"license:apache-2.0",
"region:us"
] | 2024-02-08T16:49:32+00:00 | [] | [
"de",
"en"
] | TAGS
#gguf #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #region-us
|
# Wiedervereinigung-7b-dpo
!image/png
This is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.
It is a merge of the best german 7B models with 7b parameters as a dare_ties merge.
Since the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model.
Therefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo
using our german fork of LLaMA-Factory.
## mt-bench-de
Is the merged model good? Well, of course. But it is even better with the help of some dpo tuning.
Wiedervereinigung-7b itself is a LazyMergekit merge of:
* DiscoResearch/DiscoLM_German_7b_v1
* DRXD1000/Phoenix
* VAGOsolutions/SauerkrautLM-7b-v1-mistral
* malteos/hermeo-7b
All the actual heavylifting has been done by the creators of these models.
## Configuration
## Usage
*
Vanilla Quantization by nold, Model by mayflowergmbh. Created using llm-quantizer Pipeline - 4bc844478df79ecfd72815473b30ae09499e179d
| [
"# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.",
"## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.",
"## Configuration",
"## Usage\n\n\n\n*\n\nVanilla Quantization by nold, Model by mayflowergmbh. Created using llm-quantizer Pipeline - 4bc844478df79ecfd72815473b30ae09499e179d"
] | [
"TAGS\n#gguf #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #region-us \n",
"# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.",
"## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.",
"## Configuration",
"## Usage\n\n\n\n*\n\nVanilla Quantization by nold, Model by mayflowergmbh. Created using llm-quantizer Pipeline - 4bc844478df79ecfd72815473b30ae09499e179d"
] | [
156,
148,
120,
4,
55
] | [
"passage: TAGS\n#gguf #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #region-us \n# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.## Configuration## Usage\n\n\n\n*\n\nVanilla Quantization by nold, Model by mayflowergmbh. Created using llm-quantizer Pipeline - 4bc844478df79ecfd72815473b30ae09499e179d"
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null | null | transformers |
# Model Card for Model ID
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## Model Details
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### Framework versions
- PEFT 0.8.2 | {"language": ["my"], "library_name": "transformers", "tags": ["text-generation-inference"], "base_model": "mistralai/Mistral-7B-v0.1", "pipeline_tag": "text-generation"} | text-generation | Pplus/mistral-health-faq | [
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|
# Model Card for Model ID
## Model Details
### Model Description
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- Language(s) (NLP):
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### Model Sources [optional]
- Repository:
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- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deepseek-7B-instr-1.5-26k
This model is a fine-tuned version of [deepseek-ai/deepseek-coder-7b-instruct-v1.5](https://huggingface.co/deepseek-ai/deepseek-coder-7b-instruct-v1.5) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
- Transformers 4.30.0
- Pytorch 2.0.0.post200
- Datasets 2.10.1
- Tokenizers 0.13.3
| {"license": "other", "library_name": "peft", "tags": ["generated_from_trainer"], "model-index": [{"name": "deepseek-7B-instr-1.5-26k", "results": []}]} | null | zzz99/deepseek-7B-instr-1.5-26k | [
"peft",
"generated_from_trainer",
"license:other",
"region:us"
] | 2024-02-08T16:56:01+00:00 | [] | [] | TAGS
#peft #generated_from_trainer #license-other #region-us
|
# deepseek-7B-instr-1.5-26k
This model is a fine-tuned version of deepseek-ai/deepseek-coder-7b-instruct-v1.5 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
- Transformers 4.30.0
- Pytorch 2.0.0.post200
- Datasets 2.10.1
- Tokenizers 0.13.3
| [
"# deepseek-7B-instr-1.5-26k\n\nThis model is a fine-tuned version of deepseek-ai/deepseek-coder-7b-instruct-v1.5 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 8\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- PEFT 0.4.0\n- PEFT 0.4.0\n- Transformers 4.30.0\n- Pytorch 2.0.0.post200\n- Datasets 2.10.1\n- Tokenizers 0.13.3"
] | [
"TAGS\n#peft #generated_from_trainer #license-other #region-us \n",
"# deepseek-7B-instr-1.5-26k\n\nThis model is a fine-tuned version of deepseek-ai/deepseek-coder-7b-instruct-v1.5 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 8\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- PEFT 0.4.0\n- PEFT 0.4.0\n- Transformers 4.30.0\n- Pytorch 2.0.0.post200\n- Datasets 2.10.1\n- Tokenizers 0.13.3"
] | [
21,
50,
6,
12,
8,
3,
112,
4,
45
] | [
"passage: TAGS\n#peft #generated_from_trainer #license-other #region-us \n# deepseek-7B-instr-1.5-26k\n\nThis model is a fine-tuned version of deepseek-ai/deepseek-coder-7b-instruct-v1.5 on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 8\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1### Training results### Framework versions\n\n- PEFT 0.4.0\n- PEFT 0.4.0\n- Transformers 4.30.0\n- Pytorch 2.0.0.post200\n- Datasets 2.10.1\n- Tokenizers 0.13.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SciBERT_AsymmetricLoss_25K_bs64_P1_N1
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 67.0896
- Accuracy: 0.9945
- Precision: 0.7586
- Recall: 0.6438
- F1: 0.6965
- Hamming: 0.0055
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 25000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Hamming |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 83.6475 | 0.16 | 5000 | 79.3653 | 0.9938 | 0.7361 | 0.5667 | 0.6404 | 0.0062 |
| 75.8712 | 0.32 | 10000 | 72.7250 | 0.9942 | 0.7513 | 0.6068 | 0.6714 | 0.0058 |
| 72.4202 | 0.47 | 15000 | 69.4174 | 0.9944 | 0.7568 | 0.6237 | 0.6838 | 0.0056 |
| 70.0693 | 0.63 | 20000 | 67.8098 | 0.9945 | 0.7561 | 0.6385 | 0.6923 | 0.0055 |
| 68.9765 | 0.79 | 25000 | 67.0896 | 0.9945 | 0.7586 | 0.6438 | 0.6965 | 0.0055 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.14.1
| {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "allenai/scibert_scivocab_uncased", "model-index": [{"name": "SciBERT_AsymmetricLoss_25K_bs64_P1_N1", "results": []}]} | text-classification | bdpc/SciBERT_AsymmetricLoss_25K_bs64_P1_N1 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:allenai/scibert_scivocab_uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T16:58:16+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us
| SciBERT\_AsymmetricLoss\_25K\_bs64\_P1\_N1
==========================================
This model is a fine-tuned version of allenai/scibert\_scivocab\_uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 67.0896
* Accuracy: 0.9945
* Precision: 0.7586
* Recall: 0.6438
* F1: 0.6965
* Hamming: 0.0055
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 25000
### Training results
### Framework versions
* Transformers 4.35.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.7.1
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 25000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.7.1\n* Tokenizers 0.14.1"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 25000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.7.1\n* Tokenizers 0.14.1"
] | [
61,
116,
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"passage: TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 25000### Training results### Framework versions\n\n\n* Transformers 4.35.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.7.1\n* Tokenizers 0.14.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SciBERT_AsymmetricLoss_25K_bs64_P4_N1
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 30.2502
- Accuracy: 0.9871
- Precision: 0.4247
- Recall: 0.8998
- F1: 0.5770
- Hamming: 0.0129
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 25000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Hamming |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 36.6287 | 0.16 | 5000 | 34.9978 | 0.9852 | 0.3863 | 0.8728 | 0.5355 | 0.0148 |
| 33.8929 | 0.32 | 10000 | 32.4942 | 0.9857 | 0.3958 | 0.8901 | 0.5480 | 0.0143 |
| 32.5419 | 0.47 | 15000 | 31.3170 | 0.9867 | 0.4162 | 0.8941 | 0.5680 | 0.0133 |
| 31.565 | 0.63 | 20000 | 30.6092 | 0.9869 | 0.4201 | 0.8975 | 0.5723 | 0.0131 |
| 31.105 | 0.79 | 25000 | 30.2502 | 0.9871 | 0.4247 | 0.8998 | 0.5770 | 0.0129 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.14.1
| {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "allenai/scibert_scivocab_uncased", "model-index": [{"name": "SciBERT_AsymmetricLoss_25K_bs64_P4_N1", "results": []}]} | text-classification | bdpc/SciBERT_AsymmetricLoss_25K_bs64_P4_N1 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:allenai/scibert_scivocab_uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T16:58:18+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us
| SciBERT\_AsymmetricLoss\_25K\_bs64\_P4\_N1
==========================================
This model is a fine-tuned version of allenai/scibert\_scivocab\_uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 30.2502
* Accuracy: 0.9871
* Precision: 0.4247
* Recall: 0.8998
* F1: 0.5770
* Hamming: 0.0129
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 25000
### Training results
### Framework versions
* Transformers 4.35.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.7.1
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 25000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.7.1\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 25000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.7.1\n* Tokenizers 0.14.1"
] | [
61,
116,
4,
36
] | [
"passage: TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 25000### Training results### Framework versions\n\n\n* Transformers 4.35.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.7.1\n* Tokenizers 0.14.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tinystarcoder-rlhf-model
This model is a fine-tuned version of [bigcode/tiny_starcoder_py](https://huggingface.co/bigcode/tiny_starcoder_py) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "bigcode-openrail-m", "tags": ["generated_from_trainer"], "base_model": "bigcode/tiny_starcoder_py", "model-index": [{"name": "tinystarcoder-rlhf-model", "results": []}]} | text-generation | vedantpalit/tinystarcoder-rlhf-model | [
"transformers",
"safetensors",
"gpt_bigcode",
"text-generation",
"generated_from_trainer",
"base_model:bigcode/tiny_starcoder_py",
"license:bigcode-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T16:59:00+00:00 | [] | [] | TAGS
#transformers #safetensors #gpt_bigcode #text-generation #generated_from_trainer #base_model-bigcode/tiny_starcoder_py #license-bigcode-openrail-m #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# tinystarcoder-rlhf-model
This model is a fine-tuned version of bigcode/tiny_starcoder_py on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# tinystarcoder-rlhf-model\n\nThis model is a fine-tuned version of bigcode/tiny_starcoder_py on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 2\n- eval_batch_size: 1\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 100\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #gpt_bigcode #text-generation #generated_from_trainer #base_model-bigcode/tiny_starcoder_py #license-bigcode-openrail-m #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# tinystarcoder-rlhf-model\n\nThis model is a fine-tuned version of bigcode/tiny_starcoder_py on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 2\n- eval_batch_size: 1\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 100\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
83,
39,
6,
12,
8,
3,
105,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #gpt_bigcode #text-generation #generated_from_trainer #base_model-bigcode/tiny_starcoder_py #license-bigcode-openrail-m #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# tinystarcoder-rlhf-model\n\nThis model is a fine-tuned version of bigcode/tiny_starcoder_py on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 2\n- eval_batch_size: 1\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 100\n- num_epochs: 1### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
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null | null | transformers |
# Nordavind-7B
A model trained on top of <https://huggingface.co/norallm/normistral-7b-warm> on various Norwegian instruct data with a context length of 4096. The name - *nordavind* - a cold wind from the north, is derived from Mistral. See https://en.wikipedia.org/wiki/Mistral_(wind)!
Runtime: ~20 hours on a V100 32GB. Final loss: `1.301`.
Prompt format:
```python
<s>{system_prompt} [INST] {inst} [/INST] \\n {res} </s>
```
Inference prompt:
```python
<s>{system_prompt} [INST] {inst} [/INST] \\n
```
## Examples:
*generate("Lag en skikkelig norsk setning", tokens=50, first_sent=True)*
> `Jeg liker å gå på tur i skogen.`
*generate("Lag en liste med tre forskjellige bruksområder (inkludert eksempler) for kunstig intelligens.")*
>1. Automatisering av oppgaver: AI kan brukes til å automatisere oppgaver som å svare på kundehenvendelser, behandle medisinske bilder og diagnostisere sykdommer, og til og med kjøre biler.
>2. Forbedre kundeservice: AI kan brukes for å forbedre kundeservicen ved å gi personlige anbefalinger, gi raskere svar og gi mer nøyaktige svar.
>3. Forbedre sikkerhet: AI kan brukes i sikkerhetssystemer for å oppdage og forhindre cyberangrep, oppdage svindel og oppdage mistenkelige aktiviteter.
*generate("Hvem tror du vinner valget i Bergen?", tokens=60)*
>Det er vanskelig å forutsi hvem som vinner valget i Bergen, men det er sannsynlig at enten Arbeiderpartiet eller Høyre vil vinne. Arbeiderpartiet har hatt makten i Bergen i mange år, men Høyre har hatt en sterk tilstedeværelse i byen de siste årene.
*generate("Forklar dyp læring til en femteklassing")*
>Dyp læring er en type maskinlæring som bruker nevrale nettverk for å lære komplekse mønstre i data. Det innebærer å trene en datamaskin til å gjenkjenne mønstre i data og ta beslutninger basert på disse mønstrene.
## Model loading
```python
from transformers import pipeline
pipe = pipeline("text-generation", model="tollefj/nordavind-7b-instruct-warm")
```
## Inference
```python
system_prompt = 'Du er "Nordavind", en hjelpsom assistent.'
def make_prompt(inst, res=None):
if not res:
return f"""<s>{system_prompt} [INST] {inst} [/INST] \\n"""
return f"""<s>{system_prompt} [INST] {inst} [/INST] \\n {res} </s>"""
def postprocess(output, first_sent=False):
output = output.split("\\n")[-1].strip()
# ignore hashtags as we often see #no_output
output = output.split("#")[0].strip()
# ignore incomplete sentences
if not output.endswith("."):
output = output.rsplit(".", 1)[0] + "."
if first_sent:
return output.split(".")[0] + "."
return output
def generate(prompt, tokens=100, first_sent=False, sample=False, temperature=1.0):
prompt = make_prompt(prompt)
output = pipe(
prompt,
max_length=tokens,
do_sample=sample,
temperature=temperature,
)
output = output[0]["generated_text"]
output = postprocess(output, first_sent=first_sent)
print(output)
```
# Training details
The model was fine-tuned in an 4bit BitsAndBytes config.
```python
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=getattr(torch, "float16"),
bnb_4bit_use_double_quant=False,
)
```
with the following LoRa-configuration:
```python
config = LoraConfig(
target_modules=[
"q_proj",
"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"up_proj",
"down_proj",
"lm_head",
],
bias="none",
lora_dropout=0.05,
task_type="CAUSAL_LM",
)
``` | {"language": ["no"], "datasets": ["tollefj/nor-instruct-combined"]} | text-generation | tollefj/nordavind-7b-instruct-warm | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"no",
"dataset:tollefj/nor-instruct-combined",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T17:03:44+00:00 | [] | [
"no"
] | TAGS
#transformers #safetensors #mistral #text-generation #no #dataset-tollefj/nor-instruct-combined #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Nordavind-7B
A model trained on top of <URL on various Norwegian instruct data with a context length of 4096. The name - *nordavind* - a cold wind from the north, is derived from Mistral. See URL
Runtime: ~20 hours on a V100 32GB. Final loss: '1.301'.
Prompt format:
Inference prompt:
## Examples:
*generate("Lag en skikkelig norsk setning", tokens=50, first_sent=True)*
> 'Jeg liker å gå på tur i skogen.'
*generate("Lag en liste med tre forskjellige bruksområder (inkludert eksempler) for kunstig intelligens.")*
>1. Automatisering av oppgaver: AI kan brukes til å automatisere oppgaver som å svare på kundehenvendelser, behandle medisinske bilder og diagnostisere sykdommer, og til og med kjøre biler.
>2. Forbedre kundeservice: AI kan brukes for å forbedre kundeservicen ved å gi personlige anbefalinger, gi raskere svar og gi mer nøyaktige svar.
>3. Forbedre sikkerhet: AI kan brukes i sikkerhetssystemer for å oppdage og forhindre cyberangrep, oppdage svindel og oppdage mistenkelige aktiviteter.
*generate("Hvem tror du vinner valget i Bergen?", tokens=60)*
>Det er vanskelig å forutsi hvem som vinner valget i Bergen, men det er sannsynlig at enten Arbeiderpartiet eller Høyre vil vinne. Arbeiderpartiet har hatt makten i Bergen i mange år, men Høyre har hatt en sterk tilstedeværelse i byen de siste årene.
*generate("Forklar dyp læring til en femteklassing")*
>Dyp læring er en type maskinlæring som bruker nevrale nettverk for å lære komplekse mønstre i data. Det innebærer å trene en datamaskin til å gjenkjenne mønstre i data og ta beslutninger basert på disse mønstrene.
## Model loading
## Inference
# Training details
The model was fine-tuned in an 4bit BitsAndBytes config.
with the following LoRa-configuration:
| [
"# Nordavind-7B\n\nA model trained on top of <URL on various Norwegian instruct data with a context length of 4096. The name - *nordavind* - a cold wind from the north, is derived from Mistral. See URL\nRuntime: ~20 hours on a V100 32GB. Final loss: '1.301'.\n\nPrompt format:\n\nInference prompt:",
"## Examples:\n\n*generate(\"Lag en skikkelig norsk setning\", tokens=50, first_sent=True)*\n\n> 'Jeg liker å gå på tur i skogen.'\n\n*generate(\"Lag en liste med tre forskjellige bruksområder (inkludert eksempler) for kunstig intelligens.\")*\n\n>1. Automatisering av oppgaver: AI kan brukes til å automatisere oppgaver som å svare på kundehenvendelser, behandle medisinske bilder og diagnostisere sykdommer, og til og med kjøre biler.\n>2. Forbedre kundeservice: AI kan brukes for å forbedre kundeservicen ved å gi personlige anbefalinger, gi raskere svar og gi mer nøyaktige svar.\n>3. Forbedre sikkerhet: AI kan brukes i sikkerhetssystemer for å oppdage og forhindre cyberangrep, oppdage svindel og oppdage mistenkelige aktiviteter.\n\n\n*generate(\"Hvem tror du vinner valget i Bergen?\", tokens=60)*\n>Det er vanskelig å forutsi hvem som vinner valget i Bergen, men det er sannsynlig at enten Arbeiderpartiet eller Høyre vil vinne. Arbeiderpartiet har hatt makten i Bergen i mange år, men Høyre har hatt en sterk tilstedeværelse i byen de siste årene.\n\n*generate(\"Forklar dyp læring til en femteklassing\")*\n>Dyp læring er en type maskinlæring som bruker nevrale nettverk for å lære komplekse mønstre i data. Det innebærer å trene en datamaskin til å gjenkjenne mønstre i data og ta beslutninger basert på disse mønstrene.",
"## Model loading",
"## Inference",
"# Training details\n\nThe model was fine-tuned in an 4bit BitsAndBytes config.\n\nwith the following LoRa-configuration:"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #no #dataset-tollefj/nor-instruct-combined #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Nordavind-7B\n\nA model trained on top of <URL on various Norwegian instruct data with a context length of 4096. The name - *nordavind* - a cold wind from the north, is derived from Mistral. See URL\nRuntime: ~20 hours on a V100 32GB. Final loss: '1.301'.\n\nPrompt format:\n\nInference prompt:",
"## Examples:\n\n*generate(\"Lag en skikkelig norsk setning\", tokens=50, first_sent=True)*\n\n> 'Jeg liker å gå på tur i skogen.'\n\n*generate(\"Lag en liste med tre forskjellige bruksområder (inkludert eksempler) for kunstig intelligens.\")*\n\n>1. Automatisering av oppgaver: AI kan brukes til å automatisere oppgaver som å svare på kundehenvendelser, behandle medisinske bilder og diagnostisere sykdommer, og til og med kjøre biler.\n>2. Forbedre kundeservice: AI kan brukes for å forbedre kundeservicen ved å gi personlige anbefalinger, gi raskere svar og gi mer nøyaktige svar.\n>3. Forbedre sikkerhet: AI kan brukes i sikkerhetssystemer for å oppdage og forhindre cyberangrep, oppdage svindel og oppdage mistenkelige aktiviteter.\n\n\n*generate(\"Hvem tror du vinner valget i Bergen?\", tokens=60)*\n>Det er vanskelig å forutsi hvem som vinner valget i Bergen, men det er sannsynlig at enten Arbeiderpartiet eller Høyre vil vinne. Arbeiderpartiet har hatt makten i Bergen i mange år, men Høyre har hatt en sterk tilstedeværelse i byen de siste årene.\n\n*generate(\"Forklar dyp læring til en femteklassing\")*\n>Dyp læring er en type maskinlæring som bruker nevrale nettverk for å lære komplekse mønstre i data. Det innebærer å trene en datamaskin til å gjenkjenne mønstre i data og ta beslutninger basert på disse mønstrene.",
"## Model loading",
"## Inference",
"# Training details\n\nThe model was fine-tuned in an 4bit BitsAndBytes config.\n\nwith the following LoRa-configuration:"
] | [
66,
85,
326,
4,
4,
32
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #no #dataset-tollefj/nor-instruct-combined #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Nordavind-7B\n\nA model trained on top of <URL on various Norwegian instruct data with a context length of 4096. The name - *nordavind* - a cold wind from the north, is derived from Mistral. See URL\nRuntime: ~20 hours on a V100 32GB. Final loss: '1.301'.\n\nPrompt format:\n\nInference prompt:## Examples:\n\n*generate(\"Lag en skikkelig norsk setning\", tokens=50, first_sent=True)*\n\n> 'Jeg liker å gå på tur i skogen.'\n\n*generate(\"Lag en liste med tre forskjellige bruksområder (inkludert eksempler) for kunstig intelligens.\")*\n\n>1. Automatisering av oppgaver: AI kan brukes til å automatisere oppgaver som å svare på kundehenvendelser, behandle medisinske bilder og diagnostisere sykdommer, og til og med kjøre biler.\n>2. Forbedre kundeservice: AI kan brukes for å forbedre kundeservicen ved å gi personlige anbefalinger, gi raskere svar og gi mer nøyaktige svar.\n>3. Forbedre sikkerhet: AI kan brukes i sikkerhetssystemer for å oppdage og forhindre cyberangrep, oppdage svindel og oppdage mistenkelige aktiviteter.\n\n\n*generate(\"Hvem tror du vinner valget i Bergen?\", tokens=60)*\n>Det er vanskelig å forutsi hvem som vinner valget i Bergen, men det er sannsynlig at enten Arbeiderpartiet eller Høyre vil vinne. Arbeiderpartiet har hatt makten i Bergen i mange år, men Høyre har hatt en sterk tilstedeværelse i byen de siste årene.\n\n*generate(\"Forklar dyp læring til en femteklassing\")*\n>Dyp læring er en type maskinlæring som bruker nevrale nettverk for å lære komplekse mønstre i data. Det innebærer å trene en datamaskin til å gjenkjenne mønstre i data og ta beslutninger basert på disse mønstrene.## Model loading## Inference"
] | [
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null | null | transformers | # MiquMaid-v2-70B 3bpw
## Description
Exllama quant of [NeverSleep/MiquMaid-v2-70B](https://huggingface.co/NeverSleep/MiquMaid-v2-70B)
## Other quants:
EXL2: [4bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-4bpw-exl2), [3.5bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-3.5bpw-exl2), [3bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-3bpw-exl2), [2.4bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-2.4bpw-exl2), [2.3bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-2.3bpw-exl2)
2.4bpw is probably the most you can fit in a 24gb card
GGUF:
[2bit Imatrix GGUF](https://huggingface.co/Kooten/MiquMaid-v2-70B-Imatrix-GGUF)
## Prompt format: Alpaca
```
### Instruction:
{system prompt}
### Input:
{input}
### Response:
{reply}
```
## Contact
Kooten on discord
[ko-fi.com/kooten](https://ko-fi.com/kooten) | {"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]} | text-generation | Kooten/MiquMaid-v2-70B-3.5bpw-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"not-for-all-audiences",
"nsfw",
"conversational",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T17:06:44+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # MiquMaid-v2-70B 3bpw
## Description
Exllama quant of NeverSleep/MiquMaid-v2-70B
## Other quants:
EXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw
2.4bpw is probably the most you can fit in a 24gb card
GGUF:
2bit Imatrix GGUF
## Prompt format: Alpaca
## Contact
Kooten on discord
URL | [
"# MiquMaid-v2-70B 3bpw",
"## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B",
"## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF",
"## Prompt format: Alpaca",
"## Contact\nKooten on discord\n\nURL"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# MiquMaid-v2-70B 3bpw",
"## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B",
"## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF",
"## Prompt format: Alpaca",
"## Contact\nKooten on discord\n\nURL"
] | [
75,
14,
21,
60,
8,
7
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# MiquMaid-v2-70B 3bpw## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF## Prompt format: Alpaca## Contact\nKooten on discord\n\nURL"
] | [
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null | null | peft |
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### Framework versions
- PEFT 0.8.2.dev0 | {"library_name": "peft", "base_model": "openai/whisper-medium"} | null | GuideU/whisper-medium-peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openai/whisper-medium",
"region:us"
] | 2024-02-08T17:09:10+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-openai/whisper-medium #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
- PEFT 0.8.2.dev0 | [
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"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.8.2.dev0"
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"#### Factors",
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null | null | transformers |
# AIFT-polyglot-ko-1.3b-ao-instruct-v0.91
베이스 모델 : EleutherAI/polyglot-ko-1.3b
학습 데이터 : 자체 제작한 Open Orca 스타일 데이터셋 약 48,000건 (중복 제거 및 데이터 분포 조정)
학습 방법 : Full finetuning
epoch : 3
## ko-lm-evaluation-harness(5-shot)
|kobest_boolq|kobest_copa|kobest_hellaswag|pawsx_ko|
|--|--|--|--|
|0.5398860398860399|0.71|0.436|0.476|
## Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.0.0
- Tokenizers 0.15.0 | {"license": "cc-by-nc-4.0"} | text-generation | mu0gum/AIFT-polyglot-ko-1.3b-ao-instruct-v0.91 | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T17:09:40+00:00 | [] | [] | TAGS
#transformers #safetensors #gpt_neox #text-generation #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| AIFT-polyglot-ko-1.3b-ao-instruct-v0.91
=======================================
베이스 모델 : EleutherAI/polyglot-ko-1.3b
학습 데이터 : 자체 제작한 Open Orca 스타일 데이터셋 약 48,000건 (중복 제거 및 데이터 분포 조정)
학습 방법 : Full finetuning
epoch : 3
ko-lm-evaluation-harness(5-shot)
--------------------------------
Framework versions
------------------
* Transformers 4.36.2
* Pytorch 2.1.2+cu121
* Datasets 2.0.0
* Tokenizers 0.15.0
| [] | [
"TAGS\n#transformers #safetensors #gpt_neox #text-generation #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
61
] | [
"passage: TAGS\n#transformers #safetensors #gpt_neox #text-generation #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# result
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/MiniLM-L12-H384-uncased", "model-index": [{"name": "result", "results": []}]} | question-answering | glzhangzhi/nlp_hw_6 | [
"transformers",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:microsoft/MiniLM-L12-H384-uncased",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-08T17:13:41+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #question-answering #generated_from_trainer #base_model-microsoft/MiniLM-L12-H384-uncased #license-mit #endpoints_compatible #region-us
|
# result
This model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
| [
"# result\n\nThis model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-05\n- train_batch_size: 12\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cu118\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #bert #question-answering #generated_from_trainer #base_model-microsoft/MiniLM-L12-H384-uncased #license-mit #endpoints_compatible #region-us \n",
"# result\n\nThis model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-05\n- train_batch_size: 12\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cu118\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
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38
] | [
"passage: TAGS\n#transformers #safetensors #bert #question-answering #generated_from_trainer #base_model-microsoft/MiniLM-L12-H384-uncased #license-mit #endpoints_compatible #region-us \n# result\n\nThis model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-05\n- train_batch_size: 12\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2.0### Training results### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cu118\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8508
- Matthews Correlation: 0.5222
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5175 | 1.0 | 535 | 0.4641 | 0.4737 |
| 0.3441 | 2.0 | 1070 | 0.5276 | 0.4519 |
| 0.2263 | 3.0 | 1605 | 0.6166 | 0.4820 |
| 0.1825 | 4.0 | 2140 | 0.7947 | 0.5189 |
| 0.1226 | 5.0 | 2675 | 0.8508 | 0.5222 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0.post100
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["matthews_correlation"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": []}]} | text-classification | sebastiansson/distilbert-base-uncased-finetuned-cola | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T17:14:25+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8508
* Matthews Correlation: 0.5222
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0.post100
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
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"### Training results",
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0.post100\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
72,
98,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0.post100\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | null |
# Wiedervereinigung-7b-dpo
This is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.
It is a merge of the best german 7B models with 7b parameters as a dare_ties merge.
Since the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model.
Therefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo
using our german fork of [LLaMA-Factory](https://github.com/mayflower/LLaMA-Factory-de).
## mt-bench-de
Is the merged model good? Well, of course. But it is even better with the help of some dpo tuning.
```json
{
"first_turn": 7.3,
"second_turn": 6.925,
"categories": {
"writing": 8.425,
"roleplay": 8.6,
"reasoning": 5.4,
"math": 4.35,
"coding": 4.3,
"extraction": 7.975,
"stem": 8.5,
"humanities": 9.35
},
"average": 7.1125
}
```
Wiedervereinigung-7b itself is a [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing) merge of:
* [DiscoResearch/DiscoLM_German_7b_v1](https://huggingface.co/DiscoResearch/DiscoLM_German_7b_v1)
* [DRXD1000/Phoenix](https://huggingface.co/DRXD1000/Phoenix)
* [VAGOsolutions/SauerkrautLM-7b-v1-mistral](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1-mistral)
* [malteos/hermeo-7b](https://huggingface.co/malteos/hermeo-7b)
All the actual heavylifting has been done by the creators of these models.
## 🧩 Configuration
```yaml
models:
- model: LeoLM/leo-mistral-hessianai-7b
# No parameters necessary for base model
- model: DiscoResearch/DiscoLM_German_7b_v1
parameters:
density: 0.6
weight: 0.25
- model: DRXD1000/Phoenix
parameters:
density: 0.6
weight: 0.25
- model: VAGOsolutions/SauerkrautLM-7b-v1-mistral
parameters:
density: 0.6
weight: 0.25
- model: malteos/hermeo-7b
parameters:
density: 0.6
weight: 0.25
merge_method: dare_ties
base_model: LeoLM/leo-mistral-hessianai-7b
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mayflowergmbh/Wiedervereinigung-7b-dpo"
messages = [{"role": "user", "content": "Was ist ein deutsches large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"language": ["de", "en"], "license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "DiscoResearch/DiscoLM_German_7b_v1", "DRXD1000/Phoenix", "VAGOsolutions/SauerkrautLM-7b-v1-mistral", "malteos/hermeo-7b"], "base_model": ["DiscoResearch/DiscoLM_German_7b_v1", "DRXD1000/Phoenix", "VAGOsolutions/SauerkrautLM-7b-v1-mistral", "malteos/hermeo-7b"]} | null | LoneStriker/Wiedervereinigung-7b-dpo-GGUF | [
"gguf",
"merge",
"mergekit",
"lazymergekit",
"DiscoResearch/DiscoLM_German_7b_v1",
"DRXD1000/Phoenix",
"VAGOsolutions/SauerkrautLM-7b-v1-mistral",
"malteos/hermeo-7b",
"de",
"en",
"base_model:DiscoResearch/DiscoLM_German_7b_v1",
"base_model:DRXD1000/Phoenix",
"base_model:VAGOsolutions/SauerkrautLM-7b-v1-mistral",
"base_model:malteos/hermeo-7b",
"license:apache-2.0",
"region:us"
] | 2024-02-08T17:17:23+00:00 | [] | [
"de",
"en"
] | TAGS
#gguf #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #region-us
|
# Wiedervereinigung-7b-dpo
!image/png
This is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.
It is a merge of the best german 7B models with 7b parameters as a dare_ties merge.
Since the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model.
Therefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo
using our german fork of LLaMA-Factory.
## mt-bench-de
Is the merged model good? Well, of course. But it is even better with the help of some dpo tuning.
Wiedervereinigung-7b itself is a LazyMergekit merge of:
* DiscoResearch/DiscoLM_German_7b_v1
* DRXD1000/Phoenix
* VAGOsolutions/SauerkrautLM-7b-v1-mistral
* malteos/hermeo-7b
All the actual heavylifting has been done by the creators of these models.
## Configuration
## Usage
| [
"# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.",
"## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.",
"## Configuration",
"## Usage"
] | [
"TAGS\n#gguf #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #region-us \n",
"# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.",
"## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.",
"## Configuration",
"## Usage"
] | [
156,
148,
120,
4,
3
] | [
"passage: TAGS\n#gguf #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #region-us \n# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.## Configuration## Usage"
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null | null | diffusers |
# SDXL LoRA DreamBooth - linoyts/ad_huggy
<Gallery />
## Model description
### These are linoyts/ad_huggy LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`ad_huggy.safetensors` here 💾](/linoyts/ad_huggy/blob/main/ad_huggy.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:ad_huggy:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`ad_huggy_emb.safetensors` here 💾](/linoyts/ad_huggy/blob/main/ad_huggy_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `ad_huggy_emb` to your prompt. For example, `a ad_huggy_emb ad for lego with a emoji`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('linoyts/ad_huggy', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='linoyts/ad_huggy', filename='ad_huggy_emb.safetensors', repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>", "<s2>", "<s3>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>", "<s2>", "<s3>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('a <s0><s1> ad for lego with a <s2><s3> emoji').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
to trigger concept `T2K` → use `<s2><s3>` in your prompt
## Details
All [Files & versions](/linoyts/ad_huggy/tree/main).
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
| {"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "widget": [{"text": "a <s0><s1> ad for lego with a <s2><s3> emoji", "output": {"url": "image_0.png"}}, {"text": "a <s0><s1> ad for lego with a <s2><s3> emoji", "output": {"url": "image_1.png"}}, {"text": "a <s0><s1> ad for lego with a <s2><s3> emoji", "output": {"url": "image_2.png"}}, {"text": "a <s0><s1> ad for lego with a <s2><s3> emoji", "output": {"url": "image_3.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a <s0><s1> ad for lego with a <s2><s3> emoji"} | text-to-image | linoyts/ad_huggy | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | 2024-02-08T17:18:55+00:00 | [] | [] | TAGS
#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us
|
# SDXL LoRA DreamBooth - linoyts/ad_huggy
<Gallery />
## Model description
### These are linoyts/ad_huggy LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- LoRA: download 'ad_huggy.safetensors' here .
- Place it on your 'models/Lora' folder.
- On AUTOMATIC1111, load the LoRA by adding '<lora:ad_huggy:1>' to your prompt. On ComfyUI just load it as a regular LoRA.
- *Embeddings*: download 'ad_huggy_emb.safetensors' here .
- Place it on it on your 'embeddings' folder
- Use it by adding 'ad_huggy_emb' to your prompt. For example, 'a ad_huggy_emb ad for lego with a emoji'
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the diffusers library
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept 'TOK' → use '<s0><s1>' in your prompt
to trigger concept 'T2K' → use '<s2><s3>' in your prompt
## Details
All Files & versions.
The weights were trained using diffusers Advanced Dreambooth Training Script.
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
| [
"# SDXL LoRA DreamBooth - linoyts/ad_huggy\n\n<Gallery />",
"## Model description",
"### These are linoyts/ad_huggy LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.",
"## Download model",
"### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke\n\n- LoRA: download 'ad_huggy.safetensors' here .\n - Place it on your 'models/Lora' folder.\n - On AUTOMATIC1111, load the LoRA by adding '<lora:ad_huggy:1>' to your prompt. On ComfyUI just load it as a regular LoRA.\n- *Embeddings*: download 'ad_huggy_emb.safetensors' here .\n - Place it on it on your 'embeddings' folder\n - Use it by adding 'ad_huggy_emb' to your prompt. For example, 'a ad_huggy_emb ad for lego with a emoji'\n (you need both the LoRA and the embeddings as they were trained together for this LoRA)",
"## Use it with the diffusers library\n\n\n\nFor more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers",
"## Trigger words\n\nTo trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:\n\nto trigger concept 'TOK' → use '<s0><s1>' in your prompt \n\n\nto trigger concept 'T2K' → use '<s2><s3>' in your prompt",
"## Details\nAll Files & versions.\n\nThe weights were trained using diffusers Advanced Dreambooth Training Script.\n\nLoRA for the text encoder was enabled. False.\n\nPivotal tuning was enabled: True.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix."
] | [
"TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us \n",
"# SDXL LoRA DreamBooth - linoyts/ad_huggy\n\n<Gallery />",
"## Model description",
"### These are linoyts/ad_huggy LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.",
"## Download model",
"### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke\n\n- LoRA: download 'ad_huggy.safetensors' here .\n - Place it on your 'models/Lora' folder.\n - On AUTOMATIC1111, load the LoRA by adding '<lora:ad_huggy:1>' to your prompt. On ComfyUI just load it as a regular LoRA.\n- *Embeddings*: download 'ad_huggy_emb.safetensors' here .\n - Place it on it on your 'embeddings' folder\n - Use it by adding 'ad_huggy_emb' to your prompt. For example, 'a ad_huggy_emb ad for lego with a emoji'\n (you need both the LoRA and the embeddings as they were trained together for this LoRA)",
"## Use it with the diffusers library\n\n\n\nFor more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers",
"## Trigger words\n\nTo trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:\n\nto trigger concept 'TOK' → use '<s0><s1>' in your prompt \n\n\nto trigger concept 'T2K' → use '<s2><s3>' in your prompt",
"## Details\nAll Files & versions.\n\nThe weights were trained using diffusers Advanced Dreambooth Training Script.\n\nLoRA for the text encoder was enabled. False.\n\nPivotal tuning was enabled: True.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix."
] | [
78,
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] | [
"passage: TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us \n# SDXL LoRA DreamBooth - linoyts/ad_huggy\n\n<Gallery />## Model description### These are linoyts/ad_huggy LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.## Download model### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke\n\n- LoRA: download 'ad_huggy.safetensors' here .\n - Place it on your 'models/Lora' folder.\n - On AUTOMATIC1111, load the LoRA by adding '<lora:ad_huggy:1>' to your prompt. On ComfyUI just load it as a regular LoRA.\n- *Embeddings*: download 'ad_huggy_emb.safetensors' here .\n - Place it on it on your 'embeddings' folder\n - Use it by adding 'ad_huggy_emb' to your prompt. For example, 'a ad_huggy_emb ad for lego with a emoji'\n (you need both the LoRA and the embeddings as they were trained together for this LoRA)## Use it with the diffusers library\n\n\n\nFor more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers## Trigger words\n\nTo trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:\n\nto trigger concept 'TOK' → use '<s0><s1>' in your prompt \n\n\nto trigger concept 'T2K' → use '<s2><s3>' in your prompt"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-lora-1.57M-squad-model3
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 54
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "roberta-large", "model-index": [{"name": "roberta-large-lora-1.57M-squad-model3", "results": []}]} | question-answering | varun-v-rao/roberta-large-lora-1.57M-squad-model3 | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:roberta-large",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-08T17:23:38+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-large #license-mit #endpoints_compatible #region-us
|
# roberta-large-lora-1.57M-squad-model3
This model is a fine-tuned version of roberta-large on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 54
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"# roberta-large-lora-1.57M-squad-model3\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 54\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-large #license-mit #endpoints_compatible #region-us \n",
"# roberta-large-lora-1.57M-squad-model3\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 54\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
70,
41,
6,
12,
8,
3,
90,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-large #license-mit #endpoints_compatible #region-us \n# roberta-large-lora-1.57M-squad-model3\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 54\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
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null | null | null | ### My-Pet-RABBIT-XZG Dreambooth model trained by saisindhu24 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 218U1AO407
Sample pictures of this concept:
.jpg)
| {"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]} | text-to-image | saisindhu24/my-pet-rabbit-xzg | [
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
] | 2024-02-08T17:25:56+00:00 | [] | [] | TAGS
#NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #region-us
| ### My-Pet-RABBIT-XZG Dreambooth model trained by saisindhu24 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 218U1AO407
Sample pictures of this concept:
!0.jpg)
| [
"### My-Pet-RABBIT-XZG Dreambooth model trained by saisindhu24 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 218U1AO407\n\nSample pictures of this concept:\n\n !0.jpg)"
] | [
"TAGS\n#NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #region-us \n",
"### My-Pet-RABBIT-XZG Dreambooth model trained by saisindhu24 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 218U1AO407\n\nSample pictures of this concept:\n\n !0.jpg)"
] | [
43,
64
] | [
"passage: TAGS\n#NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #region-us \n### My-Pet-RABBIT-XZG Dreambooth model trained by saisindhu24 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 218U1AO407\n\nSample pictures of this concept:\n\n !0.jpg)"
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null | null | null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Poliuszko/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Poliuszko/q-FrozenLake-v1-4x4-noSlippery | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-08T17:27:53+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
40,
39
] | [
"passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# SMIDS_3x_beit_large_Adamax_lr00001_fold5
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8314
- Accuracy: 0.905
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1537 | 1.0 | 450 | 0.2709 | 0.9067 |
| 0.1471 | 2.0 | 900 | 0.3716 | 0.8967 |
| 0.1235 | 3.0 | 1350 | 0.3515 | 0.91 |
| 0.0665 | 4.0 | 1800 | 0.4266 | 0.9117 |
| 0.0022 | 5.0 | 2250 | 0.5065 | 0.9033 |
| 0.0447 | 6.0 | 2700 | 0.5322 | 0.9133 |
| 0.0008 | 7.0 | 3150 | 0.6263 | 0.9083 |
| 0.02 | 8.0 | 3600 | 0.6755 | 0.9133 |
| 0.0006 | 9.0 | 4050 | 0.8080 | 0.905 |
| 0.0347 | 10.0 | 4500 | 0.9745 | 0.8917 |
| 0.0 | 11.0 | 4950 | 0.7945 | 0.9067 |
| 0.0087 | 12.0 | 5400 | 0.7652 | 0.9067 |
| 0.053 | 13.0 | 5850 | 0.7058 | 0.9117 |
| 0.0001 | 14.0 | 6300 | 0.6925 | 0.9117 |
| 0.0698 | 15.0 | 6750 | 0.8058 | 0.905 |
| 0.0098 | 16.0 | 7200 | 0.7520 | 0.9167 |
| 0.0074 | 17.0 | 7650 | 0.7782 | 0.905 |
| 0.0 | 18.0 | 8100 | 0.7799 | 0.9133 |
| 0.0003 | 19.0 | 8550 | 0.8447 | 0.9067 |
| 0.0 | 20.0 | 9000 | 0.8202 | 0.9117 |
| 0.0 | 21.0 | 9450 | 0.8251 | 0.9067 |
| 0.0004 | 22.0 | 9900 | 0.7880 | 0.9083 |
| 0.0356 | 23.0 | 10350 | 0.8288 | 0.905 |
| 0.0 | 24.0 | 10800 | 0.8379 | 0.9033 |
| 0.0 | 25.0 | 11250 | 0.7968 | 0.9083 |
| 0.0128 | 26.0 | 11700 | 0.8170 | 0.91 |
| 0.0 | 27.0 | 12150 | 0.8852 | 0.9033 |
| 0.0002 | 28.0 | 12600 | 0.8989 | 0.9067 |
| 0.0 | 29.0 | 13050 | 0.8791 | 0.905 |
| 0.0 | 30.0 | 13500 | 0.8663 | 0.9067 |
| 0.0 | 31.0 | 13950 | 0.8564 | 0.9083 |
| 0.0 | 32.0 | 14400 | 0.8462 | 0.9067 |
| 0.0 | 33.0 | 14850 | 0.8516 | 0.905 |
| 0.0007 | 34.0 | 15300 | 0.8682 | 0.9067 |
| 0.0 | 35.0 | 15750 | 0.8117 | 0.9067 |
| 0.0 | 36.0 | 16200 | 0.8616 | 0.9083 |
| 0.0 | 37.0 | 16650 | 0.8586 | 0.9083 |
| 0.0 | 38.0 | 17100 | 0.8326 | 0.9083 |
| 0.0 | 39.0 | 17550 | 0.8172 | 0.9083 |
| 0.0 | 40.0 | 18000 | 0.8481 | 0.9033 |
| 0.0 | 41.0 | 18450 | 0.8645 | 0.9067 |
| 0.0 | 42.0 | 18900 | 0.8596 | 0.9083 |
| 0.0 | 43.0 | 19350 | 0.8769 | 0.91 |
| 0.0 | 44.0 | 19800 | 0.8690 | 0.91 |
| 0.0001 | 45.0 | 20250 | 0.8430 | 0.905 |
| 0.0 | 46.0 | 20700 | 0.8411 | 0.905 |
| 0.0 | 47.0 | 21150 | 0.8399 | 0.9033 |
| 0.0 | 48.0 | 21600 | 0.8330 | 0.9067 |
| 0.0 | 49.0 | 22050 | 0.8319 | 0.905 |
| 0.0 | 50.0 | 22500 | 0.8314 | 0.905 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_3x_beit_large_Adamax_lr00001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.905, "name": "Accuracy"}]}]}]} | image-classification | onizukal/SMIDS_3x_beit_large_Adamax_lr00001_fold5 | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T17:27:55+00:00 | [] | [] | TAGS
#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| SMIDS\_3x\_beit\_large\_Adamax\_lr00001\_fold5
==============================================
This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8314
* Accuracy: 0.905
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 50
### Training results
### Framework versions
* Transformers 4.32.1
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2"
] | [
81,
116,
4,
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"passage: TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2"
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null | null | sentence-transformers |
For more details please refer to our github repo: https://github.com/FlagOpen/FlagEmbedding
# BGE-M3
In this project, we introduce BGE-M3, which is distinguished for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity.
- Multi-Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval.
- Multi-Linguality: It can support more than 100 working languages.
- Multi-Granularity: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to 8192 tokens.
**Some suggestions for retrieval pipeline in RAG:**
We recommend to use following pipeline: hybrid retrieval + re-ranking.
- Hybrid retrieval leverages the strengths of various methods, offering higher accuracy and stronger generalization capabilities.
A classic example: using both embedding retrieval and the BM25 algorithm.
Now, you can try to use BGE-M3, which supports both embedding and sparse retrieval.
This allows you to obtain token weights (similar to the BM25) without any additional cost when generate dense embeddings.
- As cross-encoder models, re-ranker demonstrates higher accuracy than bi-encoder embedding model.
Utilizing the re-ranking model (e.g., [bge-reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker), [cohere-reranker](https://txt.cohere.com/rerank/)) after retrieval can further filter the selected text.
## News:
- 2/1/2024: **Thanks for the excellent tool from Vespa.** You can easily use multiple modes of BGE-M3 following this [notebook](https://github.com/vespa-engine/pyvespa/blob/master/docs/sphinx/source/examples/mother-of-all-embedding-models-cloud.ipynb)
## Model Specs
| Model Name | Dimension | Sequence Length |
|:----:|:---:|:---:|
| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | 1024 | 8192 |
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 |
## FAQ
**1. Introduction for different retrieval methods**
- Dense retrieval: map the text into a single embedding, e.g., [DPR](https://arxiv.org/abs/2004.04906), [BGE-v1.5](https://github.com/FlagOpen/FlagEmbedding)
- Sparse retrieval (lexical matching): a vector of size equal to the vocabulary, with the majority of positions set to zero, calculating a weight only for tokens present in the text. e.g., BM25, [unicoil](https://arxiv.org/pdf/2106.14807.pdf), and [splade](https://arxiv.org/abs/2107.05720)
- Multi-vector retrieval: use multiple vectors to represent a text, e.g., [ColBERT](https://arxiv.org/abs/2004.12832).
**2. Comparison with BGE-v1.5 and other monolingual models**
BGE-M3 is a multilingual model, and its ability in monolingual embedding retrieval may not surpass models specifically designed for single languages.
However, we still recommend trying BGE-M3 because of its versatility (support for multiple languages and long texts).
Moreover, it can simultaneously generate multiple representations, and using them together can enhance accuracy and generalization,
unlike most existing models that can only perform dense retrieval.
In the open-source community, there are many excellent models (e.g., jina-embedding, colbert, e5, etc),
and users can choose a model that suits their specific needs based on practical considerations,
such as whether to require multilingual or cross-language support, and whether to process long texts.
**3. How to use BGE-M3 in other projects?**
For embedding retrieval, you can employ the BGE-M3 model using the same approach as BGE.
The only difference is that the BGE-M3 model no longer requires adding instructions to the queries.
For sparse retrieval methods, most open-source libraries currently do not support direct utilization of the BGE-M3 model.
Contributions from the community are welcome.
**4. How to fine-tune bge-M3 model?**
You can follow the common in this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)
to fine-tune the dense embedding.
Our code and data for unified fine-tuning (dense, sparse, and multi-vectors) will be released.
## Usage
Install:
```
git clone https://github.com/FlagOpen/FlagEmbedding.git
cd FlagEmbedding
pip install -e .
```
or:
```
pip install -U FlagEmbedding
```
### Generate Embedding for text
- Dense Embedding
```python
from FlagEmbedding import BGEM3FlagModel
model = BGEM3FlagModel('BAAI/bge-m3',
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
sentences_1 = ["What is BGE M3?", "Defination of BM25"]
sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.",
"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"]
embeddings_1 = model.encode(sentences_1,
batch_size=12,
max_length=8192, # If you don't need such a long length, you can set a smaller value to speed up the encoding process.
)['dense_vecs']
embeddings_2 = model.encode(sentences_2)['dense_vecs']
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# [[0.6265, 0.3477], [0.3499, 0.678 ]]
```
You also can use sentence-transformers and huggingface transformers to generate dense embeddings.
Refer to [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding#usage) for details.
- Sparse Embedding (Lexical Weight)
```python
from FlagEmbedding import BGEM3FlagModel
model = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
sentences_1 = ["What is BGE M3?", "Defination of BM25"]
sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.",
"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"]
output_1 = model.encode(sentences_1, return_dense=True, return_sparse=True, return_colbert_vecs=False)
output_2 = model.encode(sentences_2, return_dense=True, return_sparse=True, return_colbert_vecs=False)
# you can see the weight for each token:
print(model.convert_id_to_token(output_1['lexical_weights']))
# [{'What': 0.08356, 'is': 0.0814, 'B': 0.1296, 'GE': 0.252, 'M': 0.1702, '3': 0.2695, '?': 0.04092},
# {'De': 0.05005, 'fin': 0.1368, 'ation': 0.04498, 'of': 0.0633, 'BM': 0.2515, '25': 0.3335}]
# compute the scores via lexical mathcing
lexical_scores = model.compute_lexical_matching_score(output_1['lexical_weights'][0], output_2['lexical_weights'][0])
print(lexical_scores)
# 0.19554901123046875
print(model.compute_lexical_matching_score(output_1['lexical_weights'][0], output_1['lexical_weights'][1]))
# 0.0
```
- Multi-Vector (ColBERT)
```python
from FlagEmbedding import BGEM3FlagModel
model = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True)
sentences_1 = ["What is BGE M3?", "Defination of BM25"]
sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.",
"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"]
output_1 = model.encode(sentences_1, return_dense=True, return_sparse=True, return_colbert_vecs=True)
output_2 = model.encode(sentences_2, return_dense=True, return_sparse=True, return_colbert_vecs=True)
print(model.colbert_score(output_1['colbert_vecs'][0], output_2['colbert_vecs'][0]))
print(model.colbert_score(output_1['colbert_vecs'][0], output_2['colbert_vecs'][1]))
# 0.7797
# 0.4620
```
### Compute score for text pairs
Input a list of text pairs, you can get the scores computed by different methods.
```python
from FlagEmbedding import BGEM3FlagModel
model = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True)
sentences_1 = ["What is BGE M3?", "Defination of BM25"]
sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.",
"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"]
sentence_pairs = [[i,j] for i in sentences_1 for j in sentences_2]
print(model.compute_score(sentence_pairs,
max_passage_length=128, # a smaller max length leads to a lower latency
weights_for_different_modes=[0.4, 0.2, 0.4])) # weights_for_different_modes(w) is used to do weighted sum: w[0]*dense_score + w[1]*sparse_score + w[2]*colbert_score
# {
# 'colbert': [0.7796499729156494, 0.4621465802192688, 0.4523794651031494, 0.7898575067520142],
# 'sparse': [0.195556640625, 0.00879669189453125, 0.0, 0.1802978515625],
# 'dense': [0.6259765625, 0.347412109375, 0.349853515625, 0.67822265625],
# 'sparse+dense': [0.482503205537796, 0.23454029858112335, 0.2332356721162796, 0.5122477412223816],
# 'colbert+sparse+dense': [0.6013619303703308, 0.3255828022956848, 0.32089319825172424, 0.6232916116714478]
# }
```
## Evaluation
- Multilingual (Miracl dataset)
- Cross-lingual (MKQA dataset)
- Long Document Retrieval
- MLDR:
Please note that MLDR is a document retrieval dataset we constructed via LLM,
covering 13 languages, including test set, validation set, and training set.
We utilized the training set from MLDR to enhance the model's long document retrieval capabilities.
Therefore, comparing baseline with `Dense w.o.long`(fine-tuning without long document dataset) is more equitable.
Additionally, this long document retrieval dataset will be open-sourced to address the current lack of open-source multilingual long text retrieval datasets.
We believe that this data will be helpful for the open-source community in training document retrieval models.
- NarritiveQA:
## Training
- Self-knowledge Distillation: combining multiple outputs from different
retrieval modes as reward signal to enhance the performance of single mode(especially for sparse retrieval and multi-vec(colbert) retrival)
- Efficient Batching: Improve the efficiency when fine-tuning on long text.
The small-batch strategy is simple but effective, which also can used to fine-tune large embedding model.
- MCLS: A simple method to improve the performance on long text without fine-tuning.
If you have no enough resource to fine-tuning model with long text, the method is useful.
Refer to our [report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) for more details.
**The fine-tuning codes and datasets will be open-sourced in the near future.**
## Models
We release two versions:
- BAAI/bge-m3-unsupervised: the model after contrastive learning in a large-scale dataset
- BAAI/bge-m3: the final model fine-tuned from BAAI/bge-m3-unsupervised
## Acknowledgement
Thanks the authors of open-sourced datasets, including Miracl, MKQA, NarritiveQA, etc.
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
``` | {"license": "mit", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | Enno-Ai/bge-m3 | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"arxiv:2004.04906",
"arxiv:2106.14807",
"arxiv:2107.05720",
"arxiv:2004.12832",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-08T17:32:22+00:00 | [
"2004.04906",
"2106.14807",
"2107.05720",
"2004.12832"
] | [] | TAGS
#sentence-transformers #safetensors #xlm-roberta #feature-extraction #sentence-similarity #arxiv-2004.04906 #arxiv-2106.14807 #arxiv-2107.05720 #arxiv-2004.12832 #license-mit #endpoints_compatible #region-us
| For more details please refer to our github repo: URL
BGE-M3
======
In this project, we introduce BGE-M3, which is distinguished for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity.
* Multi-Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval.
* Multi-Linguality: It can support more than 100 working languages.
* Multi-Granularity: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to 8192 tokens.
Some suggestions for retrieval pipeline in RAG:
We recommend to use following pipeline: hybrid retrieval + re-ranking.
* Hybrid retrieval leverages the strengths of various methods, offering higher accuracy and stronger generalization capabilities.
A classic example: using both embedding retrieval and the BM25 algorithm.
Now, you can try to use BGE-M3, which supports both embedding and sparse retrieval.
This allows you to obtain token weights (similar to the BM25) without any additional cost when generate dense embeddings.
* As cross-encoder models, re-ranker demonstrates higher accuracy than bi-encoder embedding model.
Utilizing the re-ranking model (e.g., bge-reranker, cohere-reranker) after retrieval can further filter the selected text.
News:
-----
* 2/1/2024: Thanks for the excellent tool from Vespa. You can easily use multiple modes of BGE-M3 following this notebook
Model Specs
-----------
FAQ
---
1. Introduction for different retrieval methods
* Dense retrieval: map the text into a single embedding, e.g., DPR, BGE-v1.5
* Sparse retrieval (lexical matching): a vector of size equal to the vocabulary, with the majority of positions set to zero, calculating a weight only for tokens present in the text. e.g., BM25, unicoil, and splade
* Multi-vector retrieval: use multiple vectors to represent a text, e.g., ColBERT.
2. Comparison with BGE-v1.5 and other monolingual models
BGE-M3 is a multilingual model, and its ability in monolingual embedding retrieval may not surpass models specifically designed for single languages.
However, we still recommend trying BGE-M3 because of its versatility (support for multiple languages and long texts).
Moreover, it can simultaneously generate multiple representations, and using them together can enhance accuracy and generalization,
unlike most existing models that can only perform dense retrieval.
In the open-source community, there are many excellent models (e.g., jina-embedding, colbert, e5, etc),
and users can choose a model that suits their specific needs based on practical considerations,
such as whether to require multilingual or cross-language support, and whether to process long texts.
3. How to use BGE-M3 in other projects?
For embedding retrieval, you can employ the BGE-M3 model using the same approach as BGE.
The only difference is that the BGE-M3 model no longer requires adding instructions to the queries.
For sparse retrieval methods, most open-source libraries currently do not support direct utilization of the BGE-M3 model.
Contributions from the community are welcome.
4. How to fine-tune bge-M3 model?
You can follow the common in this example
to fine-tune the dense embedding.
Our code and data for unified fine-tuning (dense, sparse, and multi-vectors) will be released.
Usage
-----
Install:
or:
### Generate Embedding for text
* Dense Embedding
You also can use sentence-transformers and huggingface transformers to generate dense embeddings.
Refer to baai\_general\_embedding for details.
* Sparse Embedding (Lexical Weight)
* Multi-Vector (ColBERT)
### Compute score for text pairs
Input a list of text pairs, you can get the scores computed by different methods.
Evaluation
----------
* Multilingual (Miracl dataset)
!avatar
* Cross-lingual (MKQA dataset)
!avatar
* Long Document Retrieval
+ MLDR:
!avatar
Please note that MLDR is a document retrieval dataset we constructed via LLM,
covering 13 languages, including test set, validation set, and training set.
We utilized the training set from MLDR to enhance the model's long document retrieval capabilities.
Therefore, comparing baseline with 'Dense w.o.long'(fine-tuning without long document dataset) is more equitable.
Additionally, this long document retrieval dataset will be open-sourced to address the current lack of open-source multilingual long text retrieval datasets.
We believe that this data will be helpful for the open-source community in training document retrieval models.
+ NarritiveQA:
!avatar
Training
--------
* Self-knowledge Distillation: combining multiple outputs from different
retrieval modes as reward signal to enhance the performance of single mode(especially for sparse retrieval and multi-vec(colbert) retrival)
* Efficient Batching: Improve the efficiency when fine-tuning on long text.
The small-batch strategy is simple but effective, which also can used to fine-tune large embedding model.
* MCLS: A simple method to improve the performance on long text without fine-tuning.
If you have no enough resource to fine-tuning model with long text, the method is useful.
Refer to our report for more details.
The fine-tuning codes and datasets will be open-sourced in the near future.
Models
------
We release two versions:
* BAAI/bge-m3-unsupervised: the model after contrastive learning in a large-scale dataset
* BAAI/bge-m3: the final model fine-tuned from BAAI/bge-m3-unsupervised
Acknowledgement
---------------
Thanks the authors of open-sourced datasets, including Miracl, MKQA, NarritiveQA, etc.
If you find this repository useful, please consider giving a star :star: and citation
| [
"### Generate Embedding for text\n\n\n* Dense Embedding\n\n\nYou also can use sentence-transformers and huggingface transformers to generate dense embeddings.\nRefer to baai\\_general\\_embedding for details.\n\n\n* Sparse Embedding (Lexical Weight)\n* Multi-Vector (ColBERT)",
"### Compute score for text pairs\n\n\nInput a list of text pairs, you can get the scores computed by different methods.\n\n\nEvaluation\n----------\n\n\n* Multilingual (Miracl dataset)\n\n\n!avatar\n\n\n* Cross-lingual (MKQA dataset)\n\n\n!avatar\n\n\n* Long Document Retrieval\n\t+ MLDR: \n\t\n\t!avatar\n\tPlease note that MLDR is a document retrieval dataset we constructed via LLM,\n\tcovering 13 languages, including test set, validation set, and training set.\n\tWe utilized the training set from MLDR to enhance the model's long document retrieval capabilities.\n\tTherefore, comparing baseline with 'Dense w.o.long'(fine-tuning without long document dataset) is more equitable.\n\tAdditionally, this long document retrieval dataset will be open-sourced to address the current lack of open-source multilingual long text retrieval datasets.\n\tWe believe that this data will be helpful for the open-source community in training document retrieval models.\n\t+ NarritiveQA: \n\t\n\t!avatar\n\n\nTraining\n--------\n\n\n* Self-knowledge Distillation: combining multiple outputs from different\nretrieval modes as reward signal to enhance the performance of single mode(especially for sparse retrieval and multi-vec(colbert) retrival)\n* Efficient Batching: Improve the efficiency when fine-tuning on long text.\nThe small-batch strategy is simple but effective, which also can used to fine-tune large embedding model.\n* MCLS: A simple method to improve the performance on long text without fine-tuning.\nIf you have no enough resource to fine-tuning model with long text, the method is useful.\n\n\nRefer to our report for more details.\n\n\nThe fine-tuning codes and datasets will be open-sourced in the near future.\n\n\nModels\n------\n\n\nWe release two versions:\n\n\n* BAAI/bge-m3-unsupervised: the model after contrastive learning in a large-scale dataset\n* BAAI/bge-m3: the final model fine-tuned from BAAI/bge-m3-unsupervised\n\n\nAcknowledgement\n---------------\n\n\nThanks the authors of open-sourced datasets, including Miracl, MKQA, NarritiveQA, etc.\n\n\nIf you find this repository useful, please consider giving a star :star: and citation"
] | [
"TAGS\n#sentence-transformers #safetensors #xlm-roberta #feature-extraction #sentence-similarity #arxiv-2004.04906 #arxiv-2106.14807 #arxiv-2107.05720 #arxiv-2004.12832 #license-mit #endpoints_compatible #region-us \n",
"### Generate Embedding for text\n\n\n* Dense Embedding\n\n\nYou also can use sentence-transformers and huggingface transformers to generate dense embeddings.\nRefer to baai\\_general\\_embedding for details.\n\n\n* Sparse Embedding (Lexical Weight)\n* Multi-Vector (ColBERT)",
"### Compute score for text pairs\n\n\nInput a list of text pairs, you can get the scores computed by different methods.\n\n\nEvaluation\n----------\n\n\n* Multilingual (Miracl dataset)\n\n\n!avatar\n\n\n* Cross-lingual (MKQA dataset)\n\n\n!avatar\n\n\n* Long Document Retrieval\n\t+ MLDR: \n\t\n\t!avatar\n\tPlease note that MLDR is a document retrieval dataset we constructed via LLM,\n\tcovering 13 languages, including test set, validation set, and training set.\n\tWe utilized the training set from MLDR to enhance the model's long document retrieval capabilities.\n\tTherefore, comparing baseline with 'Dense w.o.long'(fine-tuning without long document dataset) is more equitable.\n\tAdditionally, this long document retrieval dataset will be open-sourced to address the current lack of open-source multilingual long text retrieval datasets.\n\tWe believe that this data will be helpful for the open-source community in training document retrieval models.\n\t+ NarritiveQA: \n\t\n\t!avatar\n\n\nTraining\n--------\n\n\n* Self-knowledge Distillation: combining multiple outputs from different\nretrieval modes as reward signal to enhance the performance of single mode(especially for sparse retrieval and multi-vec(colbert) retrival)\n* Efficient Batching: Improve the efficiency when fine-tuning on long text.\nThe small-batch strategy is simple but effective, which also can used to fine-tune large embedding model.\n* MCLS: A simple method to improve the performance on long text without fine-tuning.\nIf you have no enough resource to fine-tuning model with long text, the method is useful.\n\n\nRefer to our report for more details.\n\n\nThe fine-tuning codes and datasets will be open-sourced in the near future.\n\n\nModels\n------\n\n\nWe release two versions:\n\n\n* BAAI/bge-m3-unsupervised: the model after contrastive learning in a large-scale dataset\n* BAAI/bge-m3: the final model fine-tuned from BAAI/bge-m3-unsupervised\n\n\nAcknowledgement\n---------------\n\n\nThanks the authors of open-sourced datasets, including Miracl, MKQA, NarritiveQA, etc.\n\n\nIf you find this repository useful, please consider giving a star :star: and citation"
] | [
82,
74,
537
] | [
"passage: TAGS\n#sentence-transformers #safetensors #xlm-roberta #feature-extraction #sentence-similarity #arxiv-2004.04906 #arxiv-2106.14807 #arxiv-2107.05720 #arxiv-2004.12832 #license-mit #endpoints_compatible #region-us \n### Generate Embedding for text\n\n\n* Dense Embedding\n\n\nYou also can use sentence-transformers and huggingface transformers to generate dense embeddings.\nRefer to baai\\_general\\_embedding for details.\n\n\n* Sparse Embedding (Lexical Weight)\n* Multi-Vector (ColBERT)"
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null | null | transformers |
<br>
<br>
Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-72B-v1.5b was trained on the Qwen-72B base.
# Prompt Format:
```
SYSTEM: <ANY SYSTEM CONTEXT>
USER:
ASSISTANT:
``` | {"license": "other", "license_name": "qwen-72b-licence", "license_link": "https://huggingface.co/Qwen/Qwen-72B/blob/main/LICENSE"} | text-generation | migtissera/Tess-72B-v1.5b | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T17:34:38+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<br>
!Tesoro
<br>
Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-72B-v1.5b was trained on the Qwen-72B base.
# Prompt Format:
| [
"# Prompt Format:"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Prompt Format:"
] | [
52,
6
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Prompt Format:"
] | [
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] |
null | null | transformers |
# Wiedervereinigung-7b-dpo
This is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.
It is a merge of the best german 7B models with 7b parameters as a dare_ties merge.
Since the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model.
Therefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo
using our german fork of [LLaMA-Factory](https://github.com/mayflower/LLaMA-Factory-de).
## mt-bench-de
Is the merged model good? Well, of course. But it is even better with the help of some dpo tuning.
```json
{
"first_turn": 7.3,
"second_turn": 6.925,
"categories": {
"writing": 8.425,
"roleplay": 8.6,
"reasoning": 5.4,
"math": 4.35,
"coding": 4.3,
"extraction": 7.975,
"stem": 8.5,
"humanities": 9.35
},
"average": 7.1125
}
```
Wiedervereinigung-7b itself is a [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing) merge of:
* [DiscoResearch/DiscoLM_German_7b_v1](https://huggingface.co/DiscoResearch/DiscoLM_German_7b_v1)
* [DRXD1000/Phoenix](https://huggingface.co/DRXD1000/Phoenix)
* [VAGOsolutions/SauerkrautLM-7b-v1-mistral](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1-mistral)
* [malteos/hermeo-7b](https://huggingface.co/malteos/hermeo-7b)
All the actual heavylifting has been done by the creators of these models.
## 🧩 Configuration
```yaml
models:
- model: LeoLM/leo-mistral-hessianai-7b
# No parameters necessary for base model
- model: DiscoResearch/DiscoLM_German_7b_v1
parameters:
density: 0.6
weight: 0.25
- model: DRXD1000/Phoenix
parameters:
density: 0.6
weight: 0.25
- model: VAGOsolutions/SauerkrautLM-7b-v1-mistral
parameters:
density: 0.6
weight: 0.25
- model: malteos/hermeo-7b
parameters:
density: 0.6
weight: 0.25
merge_method: dare_ties
base_model: LeoLM/leo-mistral-hessianai-7b
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mayflowergmbh/Wiedervereinigung-7b-dpo"
messages = [{"role": "user", "content": "Was ist ein deutsches large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"language": ["de", "en"], "license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "DiscoResearch/DiscoLM_German_7b_v1", "DRXD1000/Phoenix", "VAGOsolutions/SauerkrautLM-7b-v1-mistral", "malteos/hermeo-7b"], "base_model": ["DiscoResearch/DiscoLM_German_7b_v1", "DRXD1000/Phoenix", "VAGOsolutions/SauerkrautLM-7b-v1-mistral", "malteos/hermeo-7b"]} | text-generation | LoneStriker/Wiedervereinigung-7b-dpo-3.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"DiscoResearch/DiscoLM_German_7b_v1",
"DRXD1000/Phoenix",
"VAGOsolutions/SauerkrautLM-7b-v1-mistral",
"malteos/hermeo-7b",
"de",
"en",
"base_model:DiscoResearch/DiscoLM_German_7b_v1",
"base_model:DRXD1000/Phoenix",
"base_model:VAGOsolutions/SauerkrautLM-7b-v1-mistral",
"base_model:malteos/hermeo-7b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T17:40:27+00:00 | [] | [
"de",
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Wiedervereinigung-7b-dpo
!image/png
This is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.
It is a merge of the best german 7B models with 7b parameters as a dare_ties merge.
Since the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model.
Therefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo
using our german fork of LLaMA-Factory.
## mt-bench-de
Is the merged model good? Well, of course. But it is even better with the help of some dpo tuning.
Wiedervereinigung-7b itself is a LazyMergekit merge of:
* DiscoResearch/DiscoLM_German_7b_v1
* DRXD1000/Phoenix
* VAGOsolutions/SauerkrautLM-7b-v1-mistral
* malteos/hermeo-7b
All the actual heavylifting has been done by the creators of these models.
## Configuration
## Usage
| [
"# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.",
"## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.",
"## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.",
"## Configuration",
"## Usage"
] | [
194,
148,
120,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.## Configuration## Usage"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_TwoWayLoss_25K_bs64_P4_N1
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 8.8668
- Accuracy: 0.5430
- Precision: 0.0114
- Recall: 0.5359
- F1: 0.0224
- Hamming: 0.4570
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Hamming |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 8.9097 | 0.0 | 5 | 8.8974 | 0.5287 | 0.0107 | 0.5189 | 0.0210 | 0.4713 |
| 8.1387 | 0.0 | 10 | 8.8668 | 0.5430 | 0.0114 | 0.5359 | 0.0224 | 0.4570 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.14.1
| {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "allenai/scibert_scivocab_uncased", "model-index": [{"name": "test_TwoWayLoss_25K_bs64_P4_N1", "results": []}]} | text-classification | bdpc/test_TwoWayLoss_25K_bs64_P4_N1 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:allenai/scibert_scivocab_uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T17:41:32+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us
| test\_TwoWayLoss\_25K\_bs64\_P4\_N1
===================================
This model is a fine-tuned version of allenai/scibert\_scivocab\_uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 8.8668
* Accuracy: 0.5430
* Precision: 0.0114
* Recall: 0.5359
* F1: 0.0224
* Hamming: 0.4570
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 10
### Training results
### Framework versions
* Transformers 4.35.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.7.1
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.7.1\n* Tokenizers 0.14.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.7.1\n* Tokenizers 0.14.1"
] | [
61,
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"passage: TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 10### Training results### Framework versions\n\n\n* Transformers 4.35.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.7.1\n* Tokenizers 0.14.1"
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] |
null | null | transformers |
# Wiedervereinigung-7b-dpo
This is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.
It is a merge of the best german 7B models with 7b parameters as a dare_ties merge.
Since the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model.
Therefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo
using our german fork of [LLaMA-Factory](https://github.com/mayflower/LLaMA-Factory-de).
## mt-bench-de
Is the merged model good? Well, of course. But it is even better with the help of some dpo tuning.
```json
{
"first_turn": 7.3,
"second_turn": 6.925,
"categories": {
"writing": 8.425,
"roleplay": 8.6,
"reasoning": 5.4,
"math": 4.35,
"coding": 4.3,
"extraction": 7.975,
"stem": 8.5,
"humanities": 9.35
},
"average": 7.1125
}
```
Wiedervereinigung-7b itself is a [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing) merge of:
* [DiscoResearch/DiscoLM_German_7b_v1](https://huggingface.co/DiscoResearch/DiscoLM_German_7b_v1)
* [DRXD1000/Phoenix](https://huggingface.co/DRXD1000/Phoenix)
* [VAGOsolutions/SauerkrautLM-7b-v1-mistral](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1-mistral)
* [malteos/hermeo-7b](https://huggingface.co/malteos/hermeo-7b)
All the actual heavylifting has been done by the creators of these models.
## 🧩 Configuration
```yaml
models:
- model: LeoLM/leo-mistral-hessianai-7b
# No parameters necessary for base model
- model: DiscoResearch/DiscoLM_German_7b_v1
parameters:
density: 0.6
weight: 0.25
- model: DRXD1000/Phoenix
parameters:
density: 0.6
weight: 0.25
- model: VAGOsolutions/SauerkrautLM-7b-v1-mistral
parameters:
density: 0.6
weight: 0.25
- model: malteos/hermeo-7b
parameters:
density: 0.6
weight: 0.25
merge_method: dare_ties
base_model: LeoLM/leo-mistral-hessianai-7b
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mayflowergmbh/Wiedervereinigung-7b-dpo"
messages = [{"role": "user", "content": "Was ist ein deutsches large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"language": ["de", "en"], "license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "DiscoResearch/DiscoLM_German_7b_v1", "DRXD1000/Phoenix", "VAGOsolutions/SauerkrautLM-7b-v1-mistral", "malteos/hermeo-7b"], "base_model": ["DiscoResearch/DiscoLM_German_7b_v1", "DRXD1000/Phoenix", "VAGOsolutions/SauerkrautLM-7b-v1-mistral", "malteos/hermeo-7b"]} | text-generation | LoneStriker/Wiedervereinigung-7b-dpo-4.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"DiscoResearch/DiscoLM_German_7b_v1",
"DRXD1000/Phoenix",
"VAGOsolutions/SauerkrautLM-7b-v1-mistral",
"malteos/hermeo-7b",
"de",
"en",
"base_model:DiscoResearch/DiscoLM_German_7b_v1",
"base_model:DRXD1000/Phoenix",
"base_model:VAGOsolutions/SauerkrautLM-7b-v1-mistral",
"base_model:malteos/hermeo-7b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T17:41:54+00:00 | [] | [
"de",
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Wiedervereinigung-7b-dpo
!image/png
This is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.
It is a merge of the best german 7B models with 7b parameters as a dare_ties merge.
Since the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model.
Therefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo
using our german fork of LLaMA-Factory.
## mt-bench-de
Is the merged model good? Well, of course. But it is even better with the help of some dpo tuning.
Wiedervereinigung-7b itself is a LazyMergekit merge of:
* DiscoResearch/DiscoLM_German_7b_v1
* DRXD1000/Phoenix
* VAGOsolutions/SauerkrautLM-7b-v1-mistral
* malteos/hermeo-7b
All the actual heavylifting has been done by the creators of these models.
## Configuration
## Usage
| [
"# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.",
"## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.",
"## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.",
"## Configuration",
"## Usage"
] | [
194,
148,
120,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.## Configuration## Usage"
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null | null | sentence-transformers |
# celik-muhammed/multi-qa-mpnet-base-cos-v1-finetuned-dtc-zoomcamp
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('celik-muhammed/multi-qa-mpnet-base-cos-v1-finetuned-dtc-zoomcamp')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=celik-muhammed/multi-qa-mpnet-base-cos-v1-finetuned-dtc-zoomcamp)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 794 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 989 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2.43e-06
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 99.0,
"weight_decay": 0.1
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': True, 'pooling_mode_mean_sqrt_len_tokens': True, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
(2): Dense({'in_features': 3072, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(3): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | celik-muhammed/multi-qa-mpnet-base-cos-v1-finetuned-dtc-zoomcamp | [
"sentence-transformers",
"pytorch",
"tflite",
"safetensors",
"mpnet",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | 2024-02-08T17:42:12+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #tflite #safetensors #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# celik-muhammed/multi-qa-mpnet-base-cos-v1-finetuned-dtc-zoomcamp
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 794 with parameters:
Loss:
'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 989 with parameters:
Loss:
'sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss'
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
| [
"# celik-muhammed/multi-qa-mpnet-base-cos-v1-finetuned-dtc-zoomcamp\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 794 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 989 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
"TAGS\n#sentence-transformers #pytorch #tflite #safetensors #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# celik-muhammed/multi-qa-mpnet-base-cos-v1-finetuned-dtc-zoomcamp\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 794 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 989 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
49,
74,
38,
29,
136,
5,
6
] | [
"passage: TAGS\n#sentence-transformers #pytorch #tflite #safetensors #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n# celik-muhammed/multi-qa-mpnet-base-cos-v1-finetuned-dtc-zoomcamp\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 794 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 989 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors"
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null | null | diffusers | # fcdsf
<Gallery />
## Model description
fgsdg
## Trigger words
You should use `dsfds` to trigger the image generation.
## Download model
[Download](/zz001/llll/tree/main) them in the Files & versions tab.
| {"license": "bigscience-openrail-m", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "wewe", "parameters": {"negative_prompt": "eretr"}, "output": {"url": "images/test.png"}}], "base_model": "InstantX/InstantID", "instance_prompt": "dsfds"} | text-to-image | zz001/llll | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:InstantX/InstantID",
"license:bigscience-openrail-m",
"region:us"
] | 2024-02-08T17:42:44+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-InstantX/InstantID #license-bigscience-openrail-m #region-us
| # fcdsf
<Gallery />
## Model description
fgsdg
!URL
## Trigger words
You should use 'dsfds' to trigger the image generation.
## Download model
Download them in the Files & versions tab.
| [
"# fcdsf\n\n<Gallery />",
"## Model description \n\nfgsdg\n!URL",
"## Trigger words\n\nYou should use 'dsfds' to trigger the image generation.",
"## Download model\n\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-InstantX/InstantID #license-bigscience-openrail-m #region-us \n",
"# fcdsf\n\n<Gallery />",
"## Model description \n\nfgsdg\n!URL",
"## Trigger words\n\nYou should use 'dsfds' to trigger the image generation.",
"## Download model\n\n\nDownload them in the Files & versions tab."
] | [
58,
10,
9,
18,
14
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-InstantX/InstantID #license-bigscience-openrail-m #region-us \n# fcdsf\n\n<Gallery />## Model description \n\nfgsdg\n!URL## Trigger words\n\nYou should use 'dsfds' to trigger the image generation.## Download model\n\n\nDownload them in the Files & versions tab."
] | [
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null | null | transformers |
# Wiedervereinigung-7b-dpo
This is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.
It is a merge of the best german 7B models with 7b parameters as a dare_ties merge.
Since the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model.
Therefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo
using our german fork of [LLaMA-Factory](https://github.com/mayflower/LLaMA-Factory-de).
## mt-bench-de
Is the merged model good? Well, of course. But it is even better with the help of some dpo tuning.
```json
{
"first_turn": 7.3,
"second_turn": 6.925,
"categories": {
"writing": 8.425,
"roleplay": 8.6,
"reasoning": 5.4,
"math": 4.35,
"coding": 4.3,
"extraction": 7.975,
"stem": 8.5,
"humanities": 9.35
},
"average": 7.1125
}
```
Wiedervereinigung-7b itself is a [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing) merge of:
* [DiscoResearch/DiscoLM_German_7b_v1](https://huggingface.co/DiscoResearch/DiscoLM_German_7b_v1)
* [DRXD1000/Phoenix](https://huggingface.co/DRXD1000/Phoenix)
* [VAGOsolutions/SauerkrautLM-7b-v1-mistral](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1-mistral)
* [malteos/hermeo-7b](https://huggingface.co/malteos/hermeo-7b)
All the actual heavylifting has been done by the creators of these models.
## 🧩 Configuration
```yaml
models:
- model: LeoLM/leo-mistral-hessianai-7b
# No parameters necessary for base model
- model: DiscoResearch/DiscoLM_German_7b_v1
parameters:
density: 0.6
weight: 0.25
- model: DRXD1000/Phoenix
parameters:
density: 0.6
weight: 0.25
- model: VAGOsolutions/SauerkrautLM-7b-v1-mistral
parameters:
density: 0.6
weight: 0.25
- model: malteos/hermeo-7b
parameters:
density: 0.6
weight: 0.25
merge_method: dare_ties
base_model: LeoLM/leo-mistral-hessianai-7b
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mayflowergmbh/Wiedervereinigung-7b-dpo"
messages = [{"role": "user", "content": "Was ist ein deutsches large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"language": ["de", "en"], "license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "DiscoResearch/DiscoLM_German_7b_v1", "DRXD1000/Phoenix", "VAGOsolutions/SauerkrautLM-7b-v1-mistral", "malteos/hermeo-7b"], "base_model": ["DiscoResearch/DiscoLM_German_7b_v1", "DRXD1000/Phoenix", "VAGOsolutions/SauerkrautLM-7b-v1-mistral", "malteos/hermeo-7b"]} | text-generation | LoneStriker/Wiedervereinigung-7b-dpo-5.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"DiscoResearch/DiscoLM_German_7b_v1",
"DRXD1000/Phoenix",
"VAGOsolutions/SauerkrautLM-7b-v1-mistral",
"malteos/hermeo-7b",
"de",
"en",
"base_model:DiscoResearch/DiscoLM_German_7b_v1",
"base_model:DRXD1000/Phoenix",
"base_model:VAGOsolutions/SauerkrautLM-7b-v1-mistral",
"base_model:malteos/hermeo-7b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T17:43:44+00:00 | [] | [
"de",
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Wiedervereinigung-7b-dpo
!image/png
This is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.
It is a merge of the best german 7B models with 7b parameters as a dare_ties merge.
Since the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model.
Therefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo
using our german fork of LLaMA-Factory.
## mt-bench-de
Is the merged model good? Well, of course. But it is even better with the help of some dpo tuning.
Wiedervereinigung-7b itself is a LazyMergekit merge of:
* DiscoResearch/DiscoLM_German_7b_v1
* DRXD1000/Phoenix
* VAGOsolutions/SauerkrautLM-7b-v1-mistral
* malteos/hermeo-7b
All the actual heavylifting has been done by the creators of these models.
## Configuration
## Usage
| [
"# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.",
"## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.",
"## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.",
"## Configuration",
"## Usage"
] | [
194,
148,
120,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.## Configuration## Usage"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | tavalenzuelag/mistral-7b-e2e-mod-5 | [
"transformers",
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#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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### Model Sources [optional]
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- Demo [optional]:
## Uses
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### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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null | null | transformers |
# Wiedervereinigung-7b-dpo
This is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.
It is a merge of the best german 7B models with 7b parameters as a dare_ties merge.
Since the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model.
Therefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo
using our german fork of [LLaMA-Factory](https://github.com/mayflower/LLaMA-Factory-de).
## mt-bench-de
Is the merged model good? Well, of course. But it is even better with the help of some dpo tuning.
```json
{
"first_turn": 7.3,
"second_turn": 6.925,
"categories": {
"writing": 8.425,
"roleplay": 8.6,
"reasoning": 5.4,
"math": 4.35,
"coding": 4.3,
"extraction": 7.975,
"stem": 8.5,
"humanities": 9.35
},
"average": 7.1125
}
```
Wiedervereinigung-7b itself is a [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing) merge of:
* [DiscoResearch/DiscoLM_German_7b_v1](https://huggingface.co/DiscoResearch/DiscoLM_German_7b_v1)
* [DRXD1000/Phoenix](https://huggingface.co/DRXD1000/Phoenix)
* [VAGOsolutions/SauerkrautLM-7b-v1-mistral](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1-mistral)
* [malteos/hermeo-7b](https://huggingface.co/malteos/hermeo-7b)
All the actual heavylifting has been done by the creators of these models.
## 🧩 Configuration
```yaml
models:
- model: LeoLM/leo-mistral-hessianai-7b
# No parameters necessary for base model
- model: DiscoResearch/DiscoLM_German_7b_v1
parameters:
density: 0.6
weight: 0.25
- model: DRXD1000/Phoenix
parameters:
density: 0.6
weight: 0.25
- model: VAGOsolutions/SauerkrautLM-7b-v1-mistral
parameters:
density: 0.6
weight: 0.25
- model: malteos/hermeo-7b
parameters:
density: 0.6
weight: 0.25
merge_method: dare_ties
base_model: LeoLM/leo-mistral-hessianai-7b
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mayflowergmbh/Wiedervereinigung-7b-dpo"
messages = [{"role": "user", "content": "Was ist ein deutsches large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"language": ["de", "en"], "license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "DiscoResearch/DiscoLM_German_7b_v1", "DRXD1000/Phoenix", "VAGOsolutions/SauerkrautLM-7b-v1-mistral", "malteos/hermeo-7b"], "base_model": ["DiscoResearch/DiscoLM_German_7b_v1", "DRXD1000/Phoenix", "VAGOsolutions/SauerkrautLM-7b-v1-mistral", "malteos/hermeo-7b"]} | text-generation | LoneStriker/Wiedervereinigung-7b-dpo-6.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"DiscoResearch/DiscoLM_German_7b_v1",
"DRXD1000/Phoenix",
"VAGOsolutions/SauerkrautLM-7b-v1-mistral",
"malteos/hermeo-7b",
"de",
"en",
"base_model:DiscoResearch/DiscoLM_German_7b_v1",
"base_model:DRXD1000/Phoenix",
"base_model:VAGOsolutions/SauerkrautLM-7b-v1-mistral",
"base_model:malteos/hermeo-7b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T17:45:54+00:00 | [] | [
"de",
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Wiedervereinigung-7b-dpo
!image/png
This is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.
It is a merge of the best german 7B models with 7b parameters as a dare_ties merge.
Since the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model.
Therefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo
using our german fork of LLaMA-Factory.
## mt-bench-de
Is the merged model good? Well, of course. But it is even better with the help of some dpo tuning.
Wiedervereinigung-7b itself is a LazyMergekit merge of:
* DiscoResearch/DiscoLM_German_7b_v1
* DRXD1000/Phoenix
* VAGOsolutions/SauerkrautLM-7b-v1-mistral
* malteos/hermeo-7b
All the actual heavylifting has been done by the creators of these models.
## Configuration
## Usage
| [
"# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.",
"## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.",
"## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.",
"## Configuration",
"## Usage"
] | [
194,
148,
120,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.## Configuration## Usage"
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# output-7b-26k-lora
This model is a fine-tuned version of [deepseek-ai/deepseek-coder-7b-instruct-v1.5](https://huggingface.co/deepseek-ai/deepseek-coder-7b-instruct-v1.5) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"license": "other", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "deepseek-ai/deepseek-coder-7b-instruct-v1.5", "model-index": [{"name": "output-7b-26k-lora", "results": []}]} | null | zzz99/output-7b-26k-lora | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:deepseek-ai/deepseek-coder-7b-instruct-v1.5",
"license:other",
"region:us"
] | 2024-02-08T17:46:36+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-deepseek-ai/deepseek-coder-7b-instruct-v1.5 #license-other #region-us
|
# output-7b-26k-lora
This model is a fine-tuned version of deepseek-ai/deepseek-coder-7b-instruct-v1.5 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | [
"# output-7b-26k-lora\n\nThis model is a fine-tuned version of deepseek-ai/deepseek-coder-7b-instruct-v1.5 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 2",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
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"# output-7b-26k-lora\n\nThis model is a fine-tuned version of deepseek-ai/deepseek-coder-7b-instruct-v1.5 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 2",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-deepseek-ai/deepseek-coder-7b-instruct-v1.5 #license-other #region-us \n# output-7b-26k-lora\n\nThis model is a fine-tuned version of deepseek-ai/deepseek-coder-7b-instruct-v1.5 on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 2### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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] |
null | null | null |
# RWKV-6 World
Use rwkv pip package 0.8.24+ for RWKV-6 inference: https://pypi.org/project/rwkv/ (pipeline = PIPELINE(model, "rwkv_vocab_v20230424") for rwkv-world models)
Trained on same world-2 dataset as https://huggingface.co/BlinkDL/rwkv-5-world.
| {"language": ["en", "zh", "fr", "es", "de", "pt", "ru", "it", "ja", "ko", "vi", "ar"], "license": "apache-2.0", "tags": ["pytorch", "text-generation", "causal-lm", "rwkv"], "datasets": ["cerebras/SlimPajama-627B", "EleutherAI/pile", "bigcode/starcoderdata", "oscar-corpus/OSCAR-2301"]} | text-generation | BlinkDL/rwkv-6-world | [
"pytorch",
"text-generation",
"causal-lm",
"rwkv",
"en",
"zh",
"fr",
"es",
"de",
"pt",
"ru",
"it",
"ja",
"ko",
"vi",
"ar",
"dataset:cerebras/SlimPajama-627B",
"dataset:EleutherAI/pile",
"dataset:bigcode/starcoderdata",
"dataset:oscar-corpus/OSCAR-2301",
"license:apache-2.0",
"has_space",
"region:us"
] | 2024-02-08T17:47:54+00:00 | [] | [
"en",
"zh",
"fr",
"es",
"de",
"pt",
"ru",
"it",
"ja",
"ko",
"vi",
"ar"
] | TAGS
#pytorch #text-generation #causal-lm #rwkv #en #zh #fr #es #de #pt #ru #it #ja #ko #vi #ar #dataset-cerebras/SlimPajama-627B #dataset-EleutherAI/pile #dataset-bigcode/starcoderdata #dataset-oscar-corpus/OSCAR-2301 #license-apache-2.0 #has_space #region-us
|
# RWKV-6 World
Use rwkv pip package 0.8.24+ for RWKV-6 inference: URL (pipeline = PIPELINE(model, "rwkv_vocab_v20230424") for rwkv-world models)
Trained on same world-2 dataset as URL
| [
"# RWKV-6 World\n\nUse rwkv pip package 0.8.24+ for RWKV-6 inference: URL (pipeline = PIPELINE(model, \"rwkv_vocab_v20230424\") for rwkv-world models)\n\nTrained on same world-2 dataset as URL"
] | [
"TAGS\n#pytorch #text-generation #causal-lm #rwkv #en #zh #fr #es #de #pt #ru #it #ja #ko #vi #ar #dataset-cerebras/SlimPajama-627B #dataset-EleutherAI/pile #dataset-bigcode/starcoderdata #dataset-oscar-corpus/OSCAR-2301 #license-apache-2.0 #has_space #region-us \n",
"# RWKV-6 World\n\nUse rwkv pip package 0.8.24+ for RWKV-6 inference: URL (pipeline = PIPELINE(model, \"rwkv_vocab_v20230424\") for rwkv-world models)\n\nTrained on same world-2 dataset as URL"
] | [
111,
69
] | [
"passage: TAGS\n#pytorch #text-generation #causal-lm #rwkv #en #zh #fr #es #de #pt #ru #it #ja #ko #vi #ar #dataset-cerebras/SlimPajama-627B #dataset-EleutherAI/pile #dataset-bigcode/starcoderdata #dataset-oscar-corpus/OSCAR-2301 #license-apache-2.0 #has_space #region-us \n# RWKV-6 World\n\nUse rwkv pip package 0.8.24+ for RWKV-6 inference: URL (pipeline = PIPELINE(model, \"rwkv_vocab_v20230424\") for rwkv-world models)\n\nTrained on same world-2 dataset as URL"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-squad-model2
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "roberta-large", "model-index": [{"name": "roberta-large-squad-model2", "results": []}]} | question-answering | varun-v-rao/roberta-large-squad-model2 | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:roberta-large",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-08T17:48:02+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-large #license-mit #endpoints_compatible #region-us
|
# roberta-large-squad-model2
This model is a fine-tuned version of roberta-large on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
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"# roberta-large-squad-model2\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
70,
34,
6,
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90,
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"passage: TAGS\n#transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-large #license-mit #endpoints_compatible #region-us \n# roberta-large-squad-model2\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
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null | null | transformers |
# Wiedervereinigung-7b-dpo
This is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.
It is a merge of the best german 7B models with 7b parameters as a dare_ties merge.
Since the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model.
Therefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo
using our german fork of [LLaMA-Factory](https://github.com/mayflower/LLaMA-Factory-de).
## mt-bench-de
Is the merged model good? Well, of course. But it is even better with the help of some dpo tuning.
```json
{
"first_turn": 7.3,
"second_turn": 6.925,
"categories": {
"writing": 8.425,
"roleplay": 8.6,
"reasoning": 5.4,
"math": 4.35,
"coding": 4.3,
"extraction": 7.975,
"stem": 8.5,
"humanities": 9.35
},
"average": 7.1125
}
```
Wiedervereinigung-7b itself is a [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing) merge of:
* [DiscoResearch/DiscoLM_German_7b_v1](https://huggingface.co/DiscoResearch/DiscoLM_German_7b_v1)
* [DRXD1000/Phoenix](https://huggingface.co/DRXD1000/Phoenix)
* [VAGOsolutions/SauerkrautLM-7b-v1-mistral](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1-mistral)
* [malteos/hermeo-7b](https://huggingface.co/malteos/hermeo-7b)
All the actual heavylifting has been done by the creators of these models.
## 🧩 Configuration
```yaml
models:
- model: LeoLM/leo-mistral-hessianai-7b
# No parameters necessary for base model
- model: DiscoResearch/DiscoLM_German_7b_v1
parameters:
density: 0.6
weight: 0.25
- model: DRXD1000/Phoenix
parameters:
density: 0.6
weight: 0.25
- model: VAGOsolutions/SauerkrautLM-7b-v1-mistral
parameters:
density: 0.6
weight: 0.25
- model: malteos/hermeo-7b
parameters:
density: 0.6
weight: 0.25
merge_method: dare_ties
base_model: LeoLM/leo-mistral-hessianai-7b
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mayflowergmbh/Wiedervereinigung-7b-dpo"
messages = [{"role": "user", "content": "Was ist ein deutsches large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"language": ["de", "en"], "license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "DiscoResearch/DiscoLM_German_7b_v1", "DRXD1000/Phoenix", "VAGOsolutions/SauerkrautLM-7b-v1-mistral", "malteos/hermeo-7b"], "base_model": ["DiscoResearch/DiscoLM_German_7b_v1", "DRXD1000/Phoenix", "VAGOsolutions/SauerkrautLM-7b-v1-mistral", "malteos/hermeo-7b"]} | text-generation | LoneStriker/Wiedervereinigung-7b-dpo-8.0bpw-h8-exl2 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"DiscoResearch/DiscoLM_German_7b_v1",
"DRXD1000/Phoenix",
"VAGOsolutions/SauerkrautLM-7b-v1-mistral",
"malteos/hermeo-7b",
"de",
"en",
"base_model:DiscoResearch/DiscoLM_German_7b_v1",
"base_model:DRXD1000/Phoenix",
"base_model:VAGOsolutions/SauerkrautLM-7b-v1-mistral",
"base_model:malteos/hermeo-7b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T17:48:30+00:00 | [] | [
"de",
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Wiedervereinigung-7b-dpo
!image/png
This is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.
It is a merge of the best german 7B models with 7b parameters as a dare_ties merge.
Since the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model.
Therefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo
using our german fork of LLaMA-Factory.
## mt-bench-de
Is the merged model good? Well, of course. But it is even better with the help of some dpo tuning.
Wiedervereinigung-7b itself is a LazyMergekit merge of:
* DiscoResearch/DiscoLM_German_7b_v1
* DRXD1000/Phoenix
* VAGOsolutions/SauerkrautLM-7b-v1-mistral
* malteos/hermeo-7b
All the actual heavylifting has been done by the creators of these models.
## Configuration
## Usage
| [
"# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.",
"## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.",
"## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.",
"## Configuration",
"## Usage"
] | [
194,
148,
120,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.## Configuration## Usage"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# working
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.16.0
- Tokenizers 0.15.0
| {"license": "mit", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "microsoft/phi-1_5", "model-index": [{"name": "working", "results": []}]} | text-generation | ManthanCisco/phi_Text2SQL_v1 | [
"transformers",
"tensorboard",
"safetensors",
"phi",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/phi-1_5",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T17:48:47+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #phi #text-generation #trl #sft #generated_from_trainer #custom_code #base_model-microsoft/phi-1_5 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# working
This model is a fine-tuned version of microsoft/phi-1_5 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.16.0
- Tokenizers 0.15.0
| [
"# working\n\nThis model is a fine-tuned version of microsoft/phi-1_5 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.05\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.36.0\n- Pytorch 2.0.0\n- Datasets 2.16.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #phi #text-generation #trl #sft #generated_from_trainer #custom_code #base_model-microsoft/phi-1_5 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# working\n\nThis model is a fine-tuned version of microsoft/phi-1_5 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.05\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.36.0\n- Pytorch 2.0.0\n- Datasets 2.16.0\n- Tokenizers 0.15.0"
] | [
75,
26,
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129,
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34
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"passage: TAGS\n#transformers #tensorboard #safetensors #phi #text-generation #trl #sft #generated_from_trainer #custom_code #base_model-microsoft/phi-1_5 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# working\n\nThis model is a fine-tuned version of microsoft/phi-1_5 on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.05\n- num_epochs: 1### Training results### Framework versions\n\n- Transformers 4.36.0\n- Pytorch 2.0.0\n- Datasets 2.16.0\n- Tokenizers 0.15.0"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-classification | selink/citation-classifier-roberta-base | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T17:51:23+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #roberta #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Uses",
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"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #roberta #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #roberta #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | token-classification | sahillihas/G2-finetuned-ner | [
"transformers",
"safetensors",
"distilbert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #distilbert #token-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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#### Hardware
#### Software
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BibTeX:
APA:
## Glossary [optional]
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## Model Card Contact
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] |
null | null | transformers |
# Wiedervereinigung-7b-dpo
This is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.
It is a merge of the best german 7B models with 7b parameters as a dare_ties merge.
Since the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model.
Therefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo
using our german fork of [LLaMA-Factory](https://github.com/mayflower/LLaMA-Factory-de).
## mt-bench-de
Is the merged model good? Well, of course. But it is even better with the help of some dpo tuning.
```json
{
"first_turn": 7.3,
"second_turn": 6.925,
"categories": {
"writing": 8.425,
"roleplay": 8.6,
"reasoning": 5.4,
"math": 4.35,
"coding": 4.3,
"extraction": 7.975,
"stem": 8.5,
"humanities": 9.35
},
"average": 7.1125
}
```
Wiedervereinigung-7b itself is a [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing) merge of:
* [DiscoResearch/DiscoLM_German_7b_v1](https://huggingface.co/DiscoResearch/DiscoLM_German_7b_v1)
* [DRXD1000/Phoenix](https://huggingface.co/DRXD1000/Phoenix)
* [VAGOsolutions/SauerkrautLM-7b-v1-mistral](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1-mistral)
* [malteos/hermeo-7b](https://huggingface.co/malteos/hermeo-7b)
All the actual heavylifting has been done by the creators of these models.
## 🧩 Configuration
```yaml
models:
- model: LeoLM/leo-mistral-hessianai-7b
# No parameters necessary for base model
- model: DiscoResearch/DiscoLM_German_7b_v1
parameters:
density: 0.6
weight: 0.25
- model: DRXD1000/Phoenix
parameters:
density: 0.6
weight: 0.25
- model: VAGOsolutions/SauerkrautLM-7b-v1-mistral
parameters:
density: 0.6
weight: 0.25
- model: malteos/hermeo-7b
parameters:
density: 0.6
weight: 0.25
merge_method: dare_ties
base_model: LeoLM/leo-mistral-hessianai-7b
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mayflowergmbh/Wiedervereinigung-7b-dpo"
messages = [{"role": "user", "content": "Was ist ein deutsches large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"language": ["de", "en"], "license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "DiscoResearch/DiscoLM_German_7b_v1", "DRXD1000/Phoenix", "VAGOsolutions/SauerkrautLM-7b-v1-mistral", "malteos/hermeo-7b"], "base_model": ["DiscoResearch/DiscoLM_German_7b_v1", "DRXD1000/Phoenix", "VAGOsolutions/SauerkrautLM-7b-v1-mistral", "malteos/hermeo-7b"]} | text-generation | LoneStriker/Wiedervereinigung-7b-dpo-AWQ | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"DiscoResearch/DiscoLM_German_7b_v1",
"DRXD1000/Phoenix",
"VAGOsolutions/SauerkrautLM-7b-v1-mistral",
"malteos/hermeo-7b",
"de",
"en",
"base_model:DiscoResearch/DiscoLM_German_7b_v1",
"base_model:DRXD1000/Phoenix",
"base_model:VAGOsolutions/SauerkrautLM-7b-v1-mistral",
"base_model:malteos/hermeo-7b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2024-02-08T18:01:25+00:00 | [] | [
"de",
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# Wiedervereinigung-7b-dpo
!image/png
This is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.
It is a merge of the best german 7B models with 7b parameters as a dare_ties merge.
Since the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model.
Therefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo
using our german fork of LLaMA-Factory.
## mt-bench-de
Is the merged model good? Well, of course. But it is even better with the help of some dpo tuning.
Wiedervereinigung-7b itself is a LazyMergekit merge of:
* DiscoResearch/DiscoLM_German_7b_v1
* DRXD1000/Phoenix
* VAGOsolutions/SauerkrautLM-7b-v1-mistral
* malteos/hermeo-7b
All the actual heavylifting has been done by the creators of these models.
## Configuration
## Usage
| [
"# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.",
"## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
"# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.",
"## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.",
"## Configuration",
"## Usage"
] | [
197,
148,
120,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.## Configuration## Usage"
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# zephyr-7b-beta-es-2000-es-agent
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"license": "mit", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "HuggingFaceH4/zephyr-7b-beta", "model-index": [{"name": "zephyr-7b-beta-es-2000-es-agent", "results": []}]} | null | Yaxin1992/zephyr-7b-beta-es-2000-es-agent | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"license:mit",
"region:us"
] | 2024-02-08T18:03:54+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-HuggingFaceH4/zephyr-7b-beta #license-mit #region-us
|
# zephyr-7b-beta-es-2000-es-agent
This model is a fine-tuned version of HuggingFaceH4/zephyr-7b-beta on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | [
"# zephyr-7b-beta-es-2000-es-agent\n\nThis model is a fine-tuned version of HuggingFaceH4/zephyr-7b-beta on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 2000\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-HuggingFaceH4/zephyr-7b-beta #license-mit #region-us \n",
"# zephyr-7b-beta-es-2000-es-agent\n\nThis model is a fine-tuned version of HuggingFaceH4/zephyr-7b-beta on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 2000\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-HuggingFaceH4/zephyr-7b-beta #license-mit #region-us \n# zephyr-7b-beta-es-2000-es-agent\n\nThis model is a fine-tuned version of HuggingFaceH4/zephyr-7b-beta on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 2000\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | token-classification | sahillihas/G3-finetuned-ner | [
"transformers",
"safetensors",
"distilbert",
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# Model Card for Model ID
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## Uses
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### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
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- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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null | null | transformers |
# Model Card for Model ID
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#Mahna_Mahna, Fine Tuning Mistral 7b in Google Colab with QLoRA(tutorial_reproduction).ipynb
#Tutorial by Ali Mobarekati, 2024
#https://medium.com/@codersama/fine-tuning-mistral-7b-in-google-colab-with-qlora-complete-guide-60e12d437cca
## Model Details
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| {"library_name": "transformers", "tags": []} | null | KJTDHISAW/Enlighten_Instruct | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-08T18:07:31+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
#Mahna_Mahna, Fine Tuning Mistral 7b in Google Colab with QLoRA(tutorial_reproduction).ipynb
#Tutorial by Ali Mobarekati, 2024
#URL
## Model Details
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## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"### Model Architecture and Objective",
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"## Training Details",
"### Training Data",
"### Training Procedure",
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"### Compute Infrastructure",
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"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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null | null | transformers |
<img src="aya-fig1.png" alt="Aya model summary image" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Model Card for Aya 101
## Model Summary
> The Aya model is a massively multilingual generative language model that follows instructions in 101 languages.
> Aya outperforms [mT0](https://huggingface.co/bigscience/mt0-xxl) and [BLOOMZ](https://huggingface.co/bigscience/bloomz) a wide variety of automatic and human evaluations despite covering double the number of languages.
> The Aya model is trained using [xP3x](https://huggingface.co/datasets/CohereForAI/xP3x), [Aya Dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset), [Aya Collection](https://huggingface.co/datasets/CohereForAI/aya_collection), a subset of [DataProvenance collection](https://huggingface.co/datasets/DataProvenanceInitiative/Commercially-Verified-Licenses) and ShareGPT-Command.
> We release the checkpoints under a Apache-2.0 license to further our mission of multilingual technologies empowering a
> multilingual world.
- **Developed by:** [Cohere For AI]((https://cohere.for.ai))
- **Model type:** a Transformer style autoregressive massively multilingual language model.
- **Paper**: [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://arxiv.org/abs/2402.07827)
- **Point of Contact**: Cohere For AI: [cohere.for.ai](https://cohere.for.ai)
- **Languages**: Refer to the list of languages in the `language` section of this model card.
- **License**: Apache-2.0
- **Model**: [Aya-101](https://huggingface.co/CohereForAI/aya-101)
- **Model Size**: 13 billion parameters
- **Datasets**: [xP3x](https://huggingface.co/datasets/CohereForAI/xP3x), [Aya Dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset), [Aya Collection](https://huggingface.co/datasets/CohereForAI/aya_collection), [DataProvenance collection](https://huggingface.co/datasets/DataProvenanceInitiative/Commercially-Verified-Licenses), ShareGPT-Command.
## Use
```bash
# pip install -q transformers
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
checkpoint = "CohereForAI/aya-101"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
aya_model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
# Turkish to English translation
tur_inputs = tokenizer.encode("Translate to English: Aya cok dilli bir dil modelidir.", return_tensors="pt")
tur_outputs = aya_model.generate(tur_inputs, max_new_tokens=128)
print(tokenizer.decode(tur_outputs[0]))
# Aya is a multi-lingual language model
# Q: Why are there so many languages in India?
hin_inputs = tokenizer.encode("भारत में इतनी सारी भाषाएँ क्यों हैं?", return_tensors="pt")
hin_outputs = aya_model.generate(hin_inputs, max_new_tokens=128)
print(tokenizer.decode(hin_outputs[0]))
# Expected output: भारत में कई भाषाएँ हैं और विभिन्न भाषाओं के बोली जाने वाले लोग हैं। यह विभिन्नता भाषाई विविधता और सांस्कृतिक विविधता का परिणाम है। Translates to "India has many languages and people speaking different languages. This diversity is the result of linguistic diversity and cultural diversity."
```
## Model Details
### Finetuning
- Architecture: Same as [mt5-xxl](https://huggingface.co/google/mt5-xxl)
- Number of Samples seen during Finetuning: 25M
- Batch size: 256
- Hardware: TPUv4-128
- Software: T5X, Jax
### Data Sources
The Aya model is trained on the following datasets:
- [xP3x](https://huggingface.co/datasets/CohereForAI/xP3x)
- [Aya Dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset)
- [Aya Collection](https://huggingface.co/datasets/CohereForAI/aya_collection)
- [DataProvenance collection](https://huggingface.co/datasets/DataProvenanceInitiative/Commercially-Verified-Licenses)
- ShareGPT-Command
All datasets are subset to the 101 languages supported by [mT5](https://huggingface.co/google/mt5-xxl). See the [paper](https://arxiv.org/abs/2402.07827) for details about filtering and pruning.
## Evaluation
We refer to Section 5 from our paper for multilingual eval across 99 languages – including discriminative and generative tasks, human evaluation, and simulated win rates that cover both held-out tasks and in-distribution performance.
## Bias, Risks, and Limitations
For a detailed overview of our effort at safety mitigation and benchmarking toxicity and bias across multiple languages, we refer to Sections 6 and 7 of our paper: [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://arxiv.org/abs/2402.07827).
We hope that the release of the Aya model will make community-based redteaming efforts possible, by exposing an open-source massively-multilingual model for community research.
## Citation
**BibTeX:**
```
@article{üstün2024aya,
title={Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model},
author={Ahmet Üstün and Viraat Aryabumi and Zheng-Xin Yong and Wei-Yin Ko and Daniel D'souza and Gbemileke Onilude and Neel Bhandari and Shivalika Singh and Hui-Lee Ooi and Amr Kayid and Freddie Vargus and Phil Blunsom and Shayne Longpre and Niklas Muennighoff and Marzieh Fadaee and Julia Kreutzer and Sara Hooker},
journal={arXiv preprint arXiv:2402.07827},
year={2024}
}
```
## Languages Covered
Below is the list of languages used in finetuning the Aya Model. We group languages into higher-, mid-, and lower-resourcedness based on a language classification by [Joshi et. al, 2020](https://microsoft.github.io/linguisticdiversity/). For further details, we refer to our [paper](https://arxiv.org/abs/2402.07827)
| ISO Code | Language Name | Script | Family | Subgrouping | Resourcedness |
| :------- | :-------------- | :----------: | :-------------: | :---------------: | :-----------: |
| afr | Afrikaans | Latin | Indo-European | Germanic | Mid |
| amh | Amharic | Ge'ez | Afro-Asiatic | Semitic | Low |
| ara | Arabic | Arabic | Afro-Asiatic | Semitic | High |
| aze | Azerbaijani | Arabic/Latin | Turkic | Common Turkic | Low |
| bel | Belarusian | Cyrillic | Indo-European | Balto-Slavic | Mid |
| ben | Bengali | Bengali | Indo-European | Indo-Aryan | Mid |
| bul | Bulgarian | Cyrillic | Indo-European | Balto-Slavic | Mid |
| cat | Catalan | Latin | Indo-European | Italic | High |
| ceb | Cebuano | Latin | Austronesian | Malayo-Polynesian | Mid |
| ces | Czech | Latin | Indo-European | Balto-Slavic | High |
| cym | Welsh | Latin | Indo-European | Celtic | Low |
| dan | Danish | Latin | Indo-European | Germanic | Mid |
| deu | German | Latin | Indo-European | Germanic | High |
| ell | Greek | Greek | Indo-European | Graeco-Phrygian | Mid |
| eng | English | Latin | Indo-European | Germanic | High |
| epo | Esperanto | Latin | Constructed | Esperantic | Low |
| est | Estonian | Latin | Uralic | Finnic | Mid |
| eus | Basque | Latin | Basque | - | High |
| fin | Finnish | Latin | Uralic | Finnic | High |
| fil | Tagalog | Latin | Austronesian | Malayo-Polynesian | Mid |
| fra | French | Latin | Indo-European | Italic | High |
| fry | Western Frisian | Latin | Indo-European | Germanic | Low |
| gla | Scottish Gaelic | Latin | Indo-European | Celtic | Low |
| gle | Irish | Latin | Indo-European | Celtic | Low |
| glg | Galician | Latin | Indo-European | Italic | Mid |
| guj | Gujarati | Gujarati | Indo-European | Indo-Aryan | Low |
| hat | Haitian Creole | Latin | Indo-European | Italic | Low |
| hau | Hausa | Latin | Afro-Asiatic | Chadic | Low |
| heb | Hebrew | Hebrew | Afro-Asiatic | Semitic | Mid |
| hin | Hindi | Devanagari | Indo-European | Indo-Aryan | High |
| hun | Hungarian | Latin | Uralic | - | High |
| hye | Armenian | Armenian | Indo-European | Armenic | Low |
| ibo | Igbo | Latin | Atlantic-Congo | Benue-Congo | Low |
| ind | Indonesian | Latin | Austronesian | Malayo-Polynesian | Mid |
| isl | Icelandic | Latin | Indo-European | Germanic | Low |
| ita | Italian | Latin | Indo-European | Italic | High |
| jav | Javanese | Latin | Austronesian | Malayo-Polynesian | Low |
| jpn | Japanese | Japanese | Japonic | Japanesic | High |
| kan | Kannada | Kannada | Dravidian | South Dravidian | Low |
| kat | Georgian | Georgian | Kartvelian | Georgian-Zan | Mid |
| kaz | Kazakh | Cyrillic | Turkic | Common Turkic | Mid |
| khm | Khmer | Khmer | Austroasiatic | Khmeric | Low |
| kir | Kyrgyz | Cyrillic | Turkic | Common Turkic | Low |
| kor | Korean | Hangul | Koreanic | Korean | High |
| kur | Kurdish | Latin | Indo-European | Iranian | Low |
| lao | Lao | Lao | Tai-Kadai | Kam-Tai | Low |
| lav | Latvian | Latin | Indo-European | Balto-Slavic | Mid |
| lat | Latin | Latin | Indo-European | Italic | Mid |
| lit | Lithuanian | Latin | Indo-European | Balto-Slavic | Mid |
| ltz | Luxembourgish | Latin | Indo-European | Germanic | Low |
| mal | Malayalam | Malayalam | Dravidian | South Dravidian | Low |
| mar | Marathi | Devanagari | Indo-European | Indo-Aryan | Low |
| mkd | Macedonian | Cyrillic | Indo-European | Balto-Slavic | Low |
| mlg | Malagasy | Latin | Austronesian | Malayo-Polynesian | Low |
| mlt | Maltese | Latin | Afro-Asiatic | Semitic | Low |
| mon | Mongolian | Cyrillic | Mongolic-Khitan | Mongolic | Low |
| mri | Maori | Latin | Austronesian | Malayo-Polynesian | Low |
| msa | Malay | Latin | Austronesian | Malayo-Polynesian | Mid |
| mya | Burmese | Myanmar | Sino-Tibetan | Burmo-Qiangic | Low |
| nep | Nepali | Devanagari | Indo-European | Indo-Aryan | Low |
| nld | Dutch | Latin | Indo-European | Germanic | High |
| nor | Norwegian | Latin | Indo-European | Germanic | Low |
| nso | Northern Sotho | Latin | Atlantic-Congo | Benue-Congo | Low |
| nya | Chichewa | Latin | Atlantic-Congo | Benue-Congo | Low |
| ory | Oriya | Oriya | Indo-European | Indo-Aryan | Low |
| pan | Punjabi | Gurmukhi | Indo-European | Indo-Aryan | Low |
| pes | Persian | Arabic | Indo-European | Iranian | High |
| pol | Polish | Latin | Indo-European | Balto-Slavic | High |
| por | Portuguese | Latin | Indo-European | Italic | High |
| pus | Pashto | Arabic | Indo-European | Iranian | Low |
| ron | Romanian | Latin | Indo-European | Italic | Mid |
| rus | Russian | Cyrillic | Indo-European | Balto-Slavic | High |
| sin | Sinhala | Sinhala | Indo-European | Indo-Aryan | Low |
| slk | Slovak | Latin | Indo-European | Balto-Slavic | Mid |
| slv | Slovenian | Latin | Indo-European | Balto-Slavic | Mid |
| smo | Samoan | Latin | Austronesian | Malayo-Polynesian | Low |
| sna | Shona | Latin | Indo-European | Indo-Aryan | Low |
| snd | Sindhi | Arabic | Indo-European | Indo-Aryan | Low |
| som | Somali | Latin | Afro-Asiatic | Cushitic | Low |
| sot | Southern Sotho | Latin | Atlantic-Congo | Benue-Congo | Low |
| spa | Spanish | Latin | Indo-European | Italic | High |
| sqi | Albanian | Latin | Indo-European | Albanian | Low |
| srp | Serbian | Cyrillic | Indo-European | Balto-Slavic | High |
| sun | Sundanese | Latin | Austronesian | Malayo-Polynesian | Low |
| swa | Swahili | Latin | Atlantic-Congo | Benue-Congo | Low |
| swe | Swedish | Latin | Indo-European | Germanic | High |
| tam | Tamil | Tamil | Dravidian | South Dravidian | Mid |
| tel | Telugu | Telugu | Dravidian | South Dravidian | Low |
| tgk | Tajik | Cyrillic | Indo-European | Iranian | Low |
| tha | Thai | Thai | Tai-Kadai | Kam-Tai | Mid |
| tur | Turkish | Latin | Turkic | Common Turkic | High |
| twi | Twi | Latin | Atlantic-Congo | Niger-Congo | Low |
| ukr | Ukrainian | Cyrillic | Indo-European | Balto-Slavic | Mid |
| urd | Urdu | Arabic | Indo-European | Indo-Aryan | Mid |
| uzb | Uzbek | Latin | Turkic | Common Turkic | Mid |
| vie | Vietnamese | Latin | Austroasiatic | Vietic | High |
| xho | Xhosa | Latin | Atlantic-Congo | Benue-Congo | Low |
| yid | Yiddish | Hebrew | Indo-European | Germanic | Low |
| yor | Yoruba | Latin | Atlantic-Congo | Benue-Congo | Low |
| zho | Chinese | Han | Sino-Tibetan | Sinitic | High |
| zul | Zulu | Latin | Atlantic-Congo | Benue-Congo | Low |
## Model Card Contact
For errors in this model card, contact Ahmet or Viraat, `{ahmet, viraat} at cohere dot com`.
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#transformers #safetensors #t5 #text2text-generation #afr #amh #ara #aze #bel #ben #bul #cat #ceb #ces #cym #dan #deu #ell #eng #epo #est #eus #fin #fil #fra #fry #gla #gle #glg #guj #hat #hau #heb #hin #hun #hye #ibo #ind #isl #ita #jav #jpn #kan #kat #kaz #khm #kir #kor #kur #lao #lav #lat #lit #ltz #mal #mar #mkd #mlg #mlt #mon #mri #msa #mya #nep #nld #nor #nso #nya #ory #pan #pes #pol #por #pus #ron #rus #sin #slk #slv #smo #sna #snd #som #sot #spa #sqi #srp #sun #swa #swe #tam #tel #tgk #tha #tur #twi #ukr #urd #uzb #vie #xho #yid #yor #zho #zul #dataset-CohereForAI/xP3x #dataset-CohereForAI/aya_dataset #dataset-CohereForAI/aya_collection #dataset-DataProvenanceInitiative/Commercially-Verified-Licenses #dataset-CohereForAI/aya_evaluation_suite #arxiv-2402.07827 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| 
Model Card for Aya 101
======================
Model Summary
-------------
>
> The Aya model is a massively multilingual generative language model that follows instructions in 101 languages.
> Aya outperforms mT0 and BLOOMZ a wide variety of automatic and human evaluations despite covering double the number of languages.
> The Aya model is trained using xP3x, Aya Dataset, Aya Collection, a subset of DataProvenance collection and ShareGPT-Command.
> We release the checkpoints under a Apache-2.0 license to further our mission of multilingual technologies empowering a
> multilingual world.
>
>
>
* Developed by: Cohere For AI)
* Model type: a Transformer style autoregressive massively multilingual language model.
* Paper: Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model
* Point of Contact: Cohere For AI: URL
* Languages: Refer to the list of languages in the 'language' section of this model card.
* License: Apache-2.0
* Model: Aya-101
* Model Size: 13 billion parameters
* Datasets: xP3x, Aya Dataset, Aya Collection, DataProvenance collection, ShareGPT-Command.
Use
---
Model Details
-------------
### Finetuning
* Architecture: Same as mt5-xxl
* Number of Samples seen during Finetuning: 25M
* Batch size: 256
* Hardware: TPUv4-128
* Software: T5X, Jax
### Data Sources
The Aya model is trained on the following datasets:
* xP3x
* Aya Dataset
* Aya Collection
* DataProvenance collection
* ShareGPT-Command
All datasets are subset to the 101 languages supported by mT5. See the paper for details about filtering and pruning.
Evaluation
----------
We refer to Section 5 from our paper for multilingual eval across 99 languages – including discriminative and generative tasks, human evaluation, and simulated win rates that cover both held-out tasks and in-distribution performance.
Bias, Risks, and Limitations
----------------------------
For a detailed overview of our effort at safety mitigation and benchmarking toxicity and bias across multiple languages, we refer to Sections 6 and 7 of our paper: Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model.
We hope that the release of the Aya model will make community-based redteaming efforts possible, by exposing an open-source massively-multilingual model for community research.
BibTeX:
Languages Covered
-----------------
Below is the list of languages used in finetuning the Aya Model. We group languages into higher-, mid-, and lower-resourcedness based on a language classification by Joshi et. al, 2020. For further details, we refer to our paper
Model Card Contact
------------------
For errors in this model card, contact Ahmet or Viraat, '{ahmet, viraat} at cohere dot com'.
| [
"### Finetuning\n\n\n* Architecture: Same as mt5-xxl\n* Number of Samples seen during Finetuning: 25M\n* Batch size: 256\n* Hardware: TPUv4-128\n* Software: T5X, Jax",
"### Data Sources\n\n\nThe Aya model is trained on the following datasets:\n\n\n* xP3x\n* Aya Dataset\n* Aya Collection\n* DataProvenance collection\n* ShareGPT-Command\n\n\nAll datasets are subset to the 101 languages supported by mT5. See the paper for details about filtering and pruning.\n\n\nEvaluation\n----------\n\n\nWe refer to Section 5 from our paper for multilingual eval across 99 languages – including discriminative and generative tasks, human evaluation, and simulated win rates that cover both held-out tasks and in-distribution performance.\n\n\nBias, Risks, and Limitations\n----------------------------\n\n\nFor a detailed overview of our effort at safety mitigation and benchmarking toxicity and bias across multiple languages, we refer to Sections 6 and 7 of our paper: Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model.\n\n\nWe hope that the release of the Aya model will make community-based redteaming efforts possible, by exposing an open-source massively-multilingual model for community research.\n\n\nBibTeX:\n\n\nLanguages Covered\n-----------------\n\n\nBelow is the list of languages used in finetuning the Aya Model. We group languages into higher-, mid-, and lower-resourcedness based on a language classification by Joshi et. al, 2020. For further details, we refer to our paper\n\n\n\nModel Card Contact\n------------------\n\n\nFor errors in this model card, contact Ahmet or Viraat, '{ahmet, viraat} at cohere dot com'."
] | [
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"### Data Sources\n\n\nThe Aya model is trained on the following datasets:\n\n\n* xP3x\n* Aya Dataset\n* Aya Collection\n* DataProvenance collection\n* ShareGPT-Command\n\n\nAll datasets are subset to the 101 languages supported by mT5. See the paper for details about filtering and pruning.\n\n\nEvaluation\n----------\n\n\nWe refer to Section 5 from our paper for multilingual eval across 99 languages – including discriminative and generative tasks, human evaluation, and simulated win rates that cover both held-out tasks and in-distribution performance.\n\n\nBias, Risks, and Limitations\n----------------------------\n\n\nFor a detailed overview of our effort at safety mitigation and benchmarking toxicity and bias across multiple languages, we refer to Sections 6 and 7 of our paper: Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model.\n\n\nWe hope that the release of the Aya model will make community-based redteaming efforts possible, by exposing an open-source massively-multilingual model for community research.\n\n\nBibTeX:\n\n\nLanguages Covered\n-----------------\n\n\nBelow is the list of languages used in finetuning the Aya Model. We group languages into higher-, mid-, and lower-resourcedness based on a language classification by Joshi et. al, 2020. For further details, we refer to our paper\n\n\n\nModel Card Contact\n------------------\n\n\nFor errors in this model card, contact Ahmet or Viraat, '{ahmet, viraat} at cohere dot com'."
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] |
null | null | transformers |
# Wiedervereinigung-7b-dpo
This is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.
It is a merge of the best german 7B models with 7b parameters as a dare_ties merge.
Since the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model.
Therefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo
using our german fork of [LLaMA-Factory](https://github.com/mayflower/LLaMA-Factory-de).
## mt-bench-de
Is the merged model good? Well, of course. But it is even better with the help of some dpo tuning.
```json
{
"first_turn": 7.3,
"second_turn": 6.925,
"categories": {
"writing": 8.425,
"roleplay": 8.6,
"reasoning": 5.4,
"math": 4.35,
"coding": 4.3,
"extraction": 7.975,
"stem": 8.5,
"humanities": 9.35
},
"average": 7.1125
}
```
Wiedervereinigung-7b itself is a [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing) merge of:
* [DiscoResearch/DiscoLM_German_7b_v1](https://huggingface.co/DiscoResearch/DiscoLM_German_7b_v1)
* [DRXD1000/Phoenix](https://huggingface.co/DRXD1000/Phoenix)
* [VAGOsolutions/SauerkrautLM-7b-v1-mistral](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1-mistral)
* [malteos/hermeo-7b](https://huggingface.co/malteos/hermeo-7b)
All the actual heavylifting has been done by the creators of these models.
## 🧩 Configuration
```yaml
models:
- model: LeoLM/leo-mistral-hessianai-7b
# No parameters necessary for base model
- model: DiscoResearch/DiscoLM_German_7b_v1
parameters:
density: 0.6
weight: 0.25
- model: DRXD1000/Phoenix
parameters:
density: 0.6
weight: 0.25
- model: VAGOsolutions/SauerkrautLM-7b-v1-mistral
parameters:
density: 0.6
weight: 0.25
- model: malteos/hermeo-7b
parameters:
density: 0.6
weight: 0.25
merge_method: dare_ties
base_model: LeoLM/leo-mistral-hessianai-7b
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mayflowergmbh/Wiedervereinigung-7b-dpo"
messages = [{"role": "user", "content": "Was ist ein deutsches large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"language": ["de", "en"], "license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "DiscoResearch/DiscoLM_German_7b_v1", "DRXD1000/Phoenix", "VAGOsolutions/SauerkrautLM-7b-v1-mistral", "malteos/hermeo-7b"], "base_model": ["DiscoResearch/DiscoLM_German_7b_v1", "DRXD1000/Phoenix", "VAGOsolutions/SauerkrautLM-7b-v1-mistral", "malteos/hermeo-7b"]} | text-generation | LoneStriker/Wiedervereinigung-7b-dpo-GPTQ | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"DiscoResearch/DiscoLM_German_7b_v1",
"DRXD1000/Phoenix",
"VAGOsolutions/SauerkrautLM-7b-v1-mistral",
"malteos/hermeo-7b",
"de",
"en",
"base_model:DiscoResearch/DiscoLM_German_7b_v1",
"base_model:DRXD1000/Phoenix",
"base_model:VAGOsolutions/SauerkrautLM-7b-v1-mistral",
"base_model:malteos/hermeo-7b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T18:15:39+00:00 | [] | [
"de",
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Wiedervereinigung-7b-dpo
!image/png
This is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.
It is a merge of the best german 7B models with 7b parameters as a dare_ties merge.
Since the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model.
Therefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo
using our german fork of LLaMA-Factory.
## mt-bench-de
Is the merged model good? Well, of course. But it is even better with the help of some dpo tuning.
Wiedervereinigung-7b itself is a LazyMergekit merge of:
* DiscoResearch/DiscoLM_German_7b_v1
* DRXD1000/Phoenix
* VAGOsolutions/SauerkrautLM-7b-v1-mistral
* malteos/hermeo-7b
All the actual heavylifting has been done by the creators of these models.
## Configuration
## Usage
| [
"# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.",
"## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.",
"## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.",
"## Configuration",
"## Usage"
] | [
194,
148,
120,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #DiscoResearch/DiscoLM_German_7b_v1 #DRXD1000/Phoenix #VAGOsolutions/SauerkrautLM-7b-v1-mistral #malteos/hermeo-7b #de #en #base_model-DiscoResearch/DiscoLM_German_7b_v1 #base_model-DRXD1000/Phoenix #base_model-VAGOsolutions/SauerkrautLM-7b-v1-mistral #base_model-malteos/hermeo-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Wiedervereinigung-7b-dpo\n\n!image/png\n\nThis is a dpo aligned merge of multiple german models scoring 7.1 on the mt-bench-de average.\nIt is a merge of the best german 7B models with 7b parameters as a dare_ties merge. \nSince the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. \nTherefore the name, no nationalist ideas involved. To improve result quality they are dpo-trained with a german translation of intel-orca-dpo \nusing our german fork of LLaMA-Factory.## mt-bench-de\n\nIs the merged model good? Well, of course. But it is even better with the help of some dpo tuning.\n\n\n\n\nWiedervereinigung-7b itself is a LazyMergekit merge of:\n* DiscoResearch/DiscoLM_German_7b_v1\n* DRXD1000/Phoenix\n* VAGOsolutions/SauerkrautLM-7b-v1-mistral\n* malteos/hermeo-7b\n\nAll the actual heavylifting has been done by the creators of these models.## Configuration## Usage"
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null | null | null |
# **Llama 2**
| {"license": "llama2", "extra_gated_prompt": "### LLAMA 2 COMMUNITY LICENSE AGREEMENT", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Date of birth": "date_picker", "Country": "country", "Affiliation": "text", "I accept the terms and conditions": "checkbox", "Test request?": "checkbox", "geo": "ip_location"}, "extra_gated_description": "Llama 2 License and Acceptable Use Policy", "extra_gated_button_content": "I Accept Llama 2 License and AUP"} | null | huggingface-test1/test-model-1 | [
"license:llama2",
"region:us"
] | 2024-02-08T18:17:47+00:00 | [] | [] | TAGS
#license-llama2 #region-us
|
# Llama 2
| [
"# Llama 2"
] | [
"TAGS\n#license-llama2 #region-us \n",
"# Llama 2"
] | [
13,
4
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"passage: TAGS\n#license-llama2 #region-us \n# Llama 2"
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null | null | transformers |
# **Llama 2 7b Spanish Version**
Orale amigos hispanohablantes, esta es una de las tantas pruebas que voy a seguir haciendo para tener un modelo en español que no alucine y empiece
a hablar en idioma "gringo" de la nada. Aportes, ayuda, condolencias...son bienvenidas!!! Gracias Zuck por querer tirar el monopolio de OpenAi (envidia ?)
regalando tu modelo al mundo!
Dataset usado para el entrenamiento: bertin-project/alpaca-spanish
Tiempo de entrenamiento: 4hs
Cantidad de registros procesados del dataset: 10k
# **Llama 2**
Llama 2 es una colección de modelos de texto generativo preentrenados y afinados que varían en escala desde 7 mil millones hasta 70 mil millones de parámetros. Este es el repositorio del modelo afinado de 7B, optimizado para casos de uso de diálogo y convertido al formato de Hugging Face Transformers. Los enlaces a otros modelos se pueden encontrar en el índice al final.
## Detalles del Modelo
*Nota: El uso de este modelo está regido por la licencia Meta. Para descargar los pesos del modelo y el tokenizador, por favor visita el [sitio web](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) y acepta nuestra Licencia antes de solicitar acceso aquí.*
Meta desarrolló y lanzó públicamente la familia de modelos de lenguaje grande Llama 2 (LLMs), una colección de modelos de texto generativo preentrenados y afinados que varían en escala desde 7 mil millones hasta 70 mil millones de parámetros. Nuestros LLMs afinados, llamados Llama-2-Chat, están optimizados para casos de uso de diálogo. Los modelos Llama-2-Chat superan a los modelos de chat de código abierto en la mayoría de los benchmarks que probamos, y en nuestras evaluaciones humanas de utilidad y seguridad, están a la par con algunos modelos cerrados populares como ChatGPT y PaLM.
**Desarrolladores del Modelo:** Meta
**Variaciones:** Llama 2 viene en una variedad de tamaños de parámetros, incluyendo 7B, 13B y 70B, así como variaciones preentrenadas y afinadas.
**Entrada:** Los modelos solo reciben texto de entrada.
**Salida:** Los modelos generan texto únicamente.
**Arquitectura del Modelo:** Llama 2 es un modelo de lenguaje auto-regresivo que utiliza una arquitectura de transformer optimizada. Las versiones ajustadas utilizan ajuste fino supervisado (SFT) y aprendizaje por refuerzo con retroalimentación humana (RLHF) para alinearse con las preferencias humanas en cuanto a utilidad y seguridad.
||Datos de Entrenamiento|Parámetros|Longitud del Contenido|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*Una nueva combinación de datos disponibles públicamente en línea*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*Una nueva combinación de datos disponibles públicamente en línea*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*Una nueva combinación de datos disponibles públicamente en línea*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Familia de modelos Llama 2.* Los recuentos de tokens se refieren solo a los datos de preentrenamiento. Todos los modelos se entrenan con un tamaño de lote global de 4 millones de tokens. Los modelos más grandes - 70B - utilizan Atención de Consulta Agrupada (GQA) para mejorar la escalabilidad de la inferencia.
**Fechas del Modelo:** Llama 2 fue entrenado entre enero de 2023 y julio de 2023.
**Estado:** Este es un modelo estático entrenado en un conjunto de datos sin conexión. Se lanzarán futuras versiones de los modelos ajustados a medida que mejoremos la seguridad del modelo con la retroalimentación de la comunidad.
**Licencia:** Una licencia comercial personalizada está disponible en: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Artículo de Investigación:** ["Llama-2: Fundación Abierta y Modelos de Chat Afinados"](arxiv.org/abs/2307.09288)
## Uso Previsto
**Casos de Uso Previstos:** Llama 2 está destinado para uso comercial e investigación en inglés. Los modelos ajustados están destinados para chat similar a un asistente, mientras que los modelos preentrenados pueden adaptarse para una variedad de tareas de generación de lenguaje natural.
Para obtener las características y el rendimiento esperados para las versiones de chat, se debe seguir un formato específico, que incluye las etiquetas `INST` y `<<SYS>>`, los tokens `BOS` y `EOS`, y los espacios en blanco y saltos de línea entre ellos (recomendamos llamar a `strip()` en las entradas para evitar espacios dobles). Consulta nuestro código de referencia en github para más detalles: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**Usos Fuera de Alcance:** Uso de cualquier manera que viole leyes o regulaciones aplicables (incluyendo leyes de cumplimiento comercial). Uso en idiomas que no sean inglés. Uso de cualquier otra manera que esté prohibida por la Política de Uso Aceptable y el Acuerdo de Licencia para Llama 2.
## Hardware y Software
**Factores de Entrenamiento:** Utilizamos bibliotecas de entrenamiento personalizadas, el Super Cluster de Investigación de Meta y clústeres de producción para el preentrenamiento. El ajuste fino, la anotación y la evaluación también se realizaron en cómputo en la nube de terceros.
**Huella de Carbono:** El preentrenamiento utilizó un total acumulado de 3.3 millones de horas de GPU de cómputo en hardware de tipo A100-80GB (TDP de 350-400W). Las emisiones totales estimadas fueron de 539 tCO2eq, el 100% de las cuales fueron compensadas por el programa de sostenibilidad de Meta.
||Tiempo (horas de GPU)|Consumo de Energía (W)|Carbono Emitido (tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**Emisiones de CO<sub>2</sub> durante el preentrenamiento.** Tiempo: tiempo total de GPU necesario para entrenar cada modelo. Consumo de Energía: capacidad de energía pico por dispositivo GPU para las GPU utilizadas ajustadas para la eficiencia del uso de energía. El 100% de las emisiones se compensan directamente mediante el programa de sostenibilidad de Meta, y dado que estamos lanzando estos modelos abiertamente, los costos de preentrenamiento no deben ser asumidos por otros.
## Datos de Entrenamiento
**Descripción General:** Llama 2 fue preentrenado en 2 billones de tokens de datos de fuentes disponibles públicamente. Los datos de ajuste fino incluyen conjuntos de datos de instrucciones disponibles públicamente, así como más de un millón de nuevos ejemplos anotados por humanos. Ni los datos de preentrenamiento ni los datos de ajuste fino incluyen datos de usuarios de Meta.
**Actualidad de los Datos:** Los datos de preentrenamiento tienen un corte hasta septiembre de 2022, pero algunos datos de ajuste son más recientes, hasta julio de 2023.
## Resultados de Evaluación
En esta sección, reportamos los resultados de los modelos Llama 1 y Llama 2 en benchmarks académicos estándar. Para todas las evaluaciones, utilizamos nuestra biblioteca de evaluaciones internas.
|Modelo|Tamaño|Código|Razonamiento con Sentido Común|Conocimiento del Mundo|Comprensión de Lectura|Matemáticas|MMLU|BBH|Evaluación AGI|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Rendimiento general en benchmarks académicos agrupados.** *Código:* Reportamos el promedio de pase@1 de nuestros modelos en HumanEval y MBPP. *Razonamiento con Sentido Común:* Reportamos el promedio de PIQA, SIQA, HellaSwag, WinoGrande, ARC fácil y desafío, OpenBookQA y CommonsenseQA. Reportamos resultados de 7 disparos para CommonSenseQA y resultados de 0 disparos para todos los demás benchmarks. *Conocimiento del Mundo:* Evaluamos el rendimiento de 5 disparos en NaturalQuestions y TriviaQA y reportamos el promedio. *Comprensión de Lectura:* Para comprensión de lectura, reportamos el promedio de 0 disparos en SQuAD, QuAC y BoolQ. *Matemáticas:* Reportamos el promedio de los benchmarks GSM8K (8 disparos) y MATH (4 disparos) en el top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluación de LLMs preentrenados en benchmarks automáticos de seguridad.** Para TruthfulQA, presentamos el porcentaje de generaciones que son tanto verídicas como informativas (cuanto mayor, mejor). Para ToxiGen, presentamos el porcentaje de generaciones tóxicas (cuanto menor, mejor).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluación de LLMs afinados en diferentes conjuntos de datos de seguridad.** Mismas definiciones de métricas que arriba.
## Consideraciones Éticas y Limitaciones
Llama 2 es una nueva tecnología que conlleva riesgos con su uso. Las pruebas realizadas hasta la fecha han sido en inglés, y no han cubierto, ni podrían cubrir, todos los escenarios. Por estas razones, como con todos los LLMs, las salidas potenciales de Llama 2 no pueden predecirse de antemano, y el modelo puede en algunos casos producir respuestas inexactas, sesgadas u otras respuestas objetables a las solicitudes de los usuarios. Por lo tanto, antes de implementar cualquier aplicación de Llama 2, los desarrolladores deben realizar pruebas de seguridad y ajuste adaptadas a sus aplicaciones específicas del modelo.
Consulta la Guía de Uso Responsable disponible en [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reportar Problemas
Por favor, reporta cualquier "error" de software u otros problemas con los modelos a través de alguno de los siguientes medios:
- Reportar problemas con el modelo: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reportar contenido problemático generado por el modelo: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reportar errores y problemas de seguridad: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Índice de Modelos Llama
|Modelo|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Enlace](https://huggingface.co/llamaste/Llama-2-7b) | [Enlace](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Enlace](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Enlace](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
|13B| [Enlace](https://huggingface.co/llamaste/Llama-2-13b) | [Enlace](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Enlace](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Enlace](https://huggingface.co/llamaste/Llama-2-13b-hf)|
|70B| [Enlace](https://huggingface.co/llamaste/Llama-2-70b) | [Enlace](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Enlace](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Enlace](https://huggingface.co/llamaste/Llama-2-70b-hf)| | {"language": ["es"], "license": "llama2"} | text-generation | Kukedlc/Llama-7b-spanish | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"es",
"arxiv:2307.09288",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T18:18:32+00:00 | [
"2307.09288"
] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #llama #text-generation #es #arxiv-2307.09288 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Llama 2 7b Spanish Version
==========================
Orale amigos hispanohablantes, esta es una de las tantas pruebas que voy a seguir haciendo para tener un modelo en español que no alucine y empiece
a hablar en idioma "gringo" de la nada. Aportes, ayuda, condolencias...son bienvenidas!!! Gracias Zuck por querer tirar el monopolio de OpenAi (envidia ?)
regalando tu modelo al mundo!
Dataset usado para el entrenamiento: bertin-project/alpaca-spanish
Tiempo de entrenamiento: 4hs
Cantidad de registros procesados del dataset: 10k
Llama 2
=======
Llama 2 es una colección de modelos de texto generativo preentrenados y afinados que varían en escala desde 7 mil millones hasta 70 mil millones de parámetros. Este es el repositorio del modelo afinado de 7B, optimizado para casos de uso de diálogo y convertido al formato de Hugging Face Transformers. Los enlaces a otros modelos se pueden encontrar en el índice al final.
Detalles del Modelo
-------------------
*Nota: El uso de este modelo está regido por la licencia Meta. Para descargar los pesos del modelo y el tokenizador, por favor visita el sitio web y acepta nuestra Licencia antes de solicitar acceso aquí.*
Meta desarrolló y lanzó públicamente la familia de modelos de lenguaje grande Llama 2 (LLMs), una colección de modelos de texto generativo preentrenados y afinados que varían en escala desde 7 mil millones hasta 70 mil millones de parámetros. Nuestros LLMs afinados, llamados Llama-2-Chat, están optimizados para casos de uso de diálogo. Los modelos Llama-2-Chat superan a los modelos de chat de código abierto en la mayoría de los benchmarks que probamos, y en nuestras evaluaciones humanas de utilidad y seguridad, están a la par con algunos modelos cerrados populares como ChatGPT y PaLM.
Desarrolladores del Modelo: Meta
Variaciones: Llama 2 viene en una variedad de tamaños de parámetros, incluyendo 7B, 13B y 70B, así como variaciones preentrenadas y afinadas.
Entrada: Los modelos solo reciben texto de entrada.
Salida: Los modelos generan texto únicamente.
Arquitectura del Modelo: Llama 2 es un modelo de lenguaje auto-regresivo que utiliza una arquitectura de transformer optimizada. Las versiones ajustadas utilizan ajuste fino supervisado (SFT) y aprendizaje por refuerzo con retroalimentación humana (RLHF) para alinearse con las preferencias humanas en cuanto a utilidad y seguridad.
*Familia de modelos Llama 2.* Los recuentos de tokens se refieren solo a los datos de preentrenamiento. Todos los modelos se entrenan con un tamaño de lote global de 4 millones de tokens. Los modelos más grandes - 70B - utilizan Atención de Consulta Agrupada (GQA) para mejorar la escalabilidad de la inferencia.
Fechas del Modelo: Llama 2 fue entrenado entre enero de 2023 y julio de 2023.
Estado: Este es un modelo estático entrenado en un conjunto de datos sin conexión. Se lanzarán futuras versiones de los modelos ajustados a medida que mejoremos la seguridad del modelo con la retroalimentación de la comunidad.
Licencia: Una licencia comercial personalizada está disponible en: URL
Artículo de Investigación: "Llama-2: Fundación Abierta y Modelos de Chat Afinados"
Uso Previsto
------------
Casos de Uso Previstos: Llama 2 está destinado para uso comercial e investigación en inglés. Los modelos ajustados están destinados para chat similar a un asistente, mientras que los modelos preentrenados pueden adaptarse para una variedad de tareas de generación de lenguaje natural.
Para obtener las características y el rendimiento esperados para las versiones de chat, se debe seguir un formato específico, que incluye las etiquetas 'INST' y '<>', los tokens 'BOS' y 'EOS', y los espacios en blanco y saltos de línea entre ellos (recomendamos llamar a 'strip()' en las entradas para evitar espacios dobles). Consulta nuestro código de referencia en github para más detalles: 'chat\_completion'.
Usos Fuera de Alcance: Uso de cualquier manera que viole leyes o regulaciones aplicables (incluyendo leyes de cumplimiento comercial). Uso en idiomas que no sean inglés. Uso de cualquier otra manera que esté prohibida por la Política de Uso Aceptable y el Acuerdo de Licencia para Llama 2.
Hardware y Software
-------------------
Factores de Entrenamiento: Utilizamos bibliotecas de entrenamiento personalizadas, el Super Cluster de Investigación de Meta y clústeres de producción para el preentrenamiento. El ajuste fino, la anotación y la evaluación también se realizaron en cómputo en la nube de terceros.
Huella de Carbono: El preentrenamiento utilizó un total acumulado de 3.3 millones de horas de GPU de cómputo en hardware de tipo A100-80GB (TDP de 350-400W). Las emisiones totales estimadas fueron de 539 tCO2eq, el 100% de las cuales fueron compensadas por el programa de sostenibilidad de Meta.
Emisiones de CO2 durante el preentrenamiento. Tiempo: tiempo total de GPU necesario para entrenar cada modelo. Consumo de Energía: capacidad de energía pico por dispositivo GPU para las GPU utilizadas ajustadas para la eficiencia del uso de energía. El 100% de las emisiones se compensan directamente mediante el programa de sostenibilidad de Meta, y dado que estamos lanzando estos modelos abiertamente, los costos de preentrenamiento no deben ser asumidos por otros.
Datos de Entrenamiento
----------------------
Descripción General: Llama 2 fue preentrenado en 2 billones de tokens de datos de fuentes disponibles públicamente. Los datos de ajuste fino incluyen conjuntos de datos de instrucciones disponibles públicamente, así como más de un millón de nuevos ejemplos anotados por humanos. Ni los datos de preentrenamiento ni los datos de ajuste fino incluyen datos de usuarios de Meta.
Actualidad de los Datos: Los datos de preentrenamiento tienen un corte hasta septiembre de 2022, pero algunos datos de ajuste son más recientes, hasta julio de 2023.
Resultados de Evaluación
------------------------
En esta sección, reportamos los resultados de los modelos Llama 1 y Llama 2 en benchmarks académicos estándar. Para todas las evaluaciones, utilizamos nuestra biblioteca de evaluaciones internas.
Rendimiento general en benchmarks académicos agrupados. *Código:* Reportamos el promedio de pase@1 de nuestros modelos en HumanEval y MBPP. *Razonamiento con Sentido Común:* Reportamos el promedio de PIQA, SIQA, HellaSwag, WinoGrande, ARC fácil y desafío, OpenBookQA y CommonsenseQA. Reportamos resultados de 7 disparos para CommonSenseQA y resultados de 0 disparos para todos los demás benchmarks. *Conocimiento del Mundo:* Evaluamos el rendimiento de 5 disparos en NaturalQuestions y TriviaQA y reportamos el promedio. *Comprensión de Lectura:* Para comprensión de lectura, reportamos el promedio de 0 disparos en SQuAD, QuAC y BoolQ. *Matemáticas:* Reportamos el promedio de los benchmarks GSM8K (8 disparos) y MATH (4 disparos) en el top 1.
Evaluación de LLMs preentrenados en benchmarks automáticos de seguridad. Para TruthfulQA, presentamos el porcentaje de generaciones que son tanto verídicas como informativas (cuanto mayor, mejor). Para ToxiGen, presentamos el porcentaje de generaciones tóxicas (cuanto menor, mejor).
Evaluación de LLMs afinados en diferentes conjuntos de datos de seguridad. Mismas definiciones de métricas que arriba.
Consideraciones Éticas y Limitaciones
-------------------------------------
Llama 2 es una nueva tecnología que conlleva riesgos con su uso. Las pruebas realizadas hasta la fecha han sido en inglés, y no han cubierto, ni podrían cubrir, todos los escenarios. Por estas razones, como con todos los LLMs, las salidas potenciales de Llama 2 no pueden predecirse de antemano, y el modelo puede en algunos casos producir respuestas inexactas, sesgadas u otras respuestas objetables a las solicitudes de los usuarios. Por lo tanto, antes de implementar cualquier aplicación de Llama 2, los desarrolladores deben realizar pruebas de seguridad y ajuste adaptadas a sus aplicaciones específicas del modelo.
Consulta la Guía de Uso Responsable disponible en URL
Reportar Problemas
------------------
Por favor, reporta cualquier "error" de software u otros problemas con los modelos a través de alguno de los siguientes medios:
* Reportar problemas con el modelo: URL
* Reportar contenido problemático generado por el modelo: URL
* Reportar errores y problemas de seguridad: URL
Índice de Modelos Llama
-----------------------
| [] | [
"TAGS\n#transformers #pytorch #safetensors #llama #text-generation #es #arxiv-2307.09288 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
68
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #llama #text-generation #es #arxiv-2307.09288 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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null | null | peft |
# Model Card for Model ID
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- PEFT 0.7.2.dev0 | {"library_name": "peft", "base_model": "google/flan-t5-base"} | null | HeydarS/flan-t5-base_peft_v1 | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/flan-t5-base",
"region:us"
] | 2024-02-08T18:21:44+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-base #region-us
|
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"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.2.dev0"
] | [
"TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-base #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.2.dev0"
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"passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-base #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.7.2.dev0"
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null | null | null |
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3NvZnQgU0NEIENsYWltYW50cyBSU0EgQ0EwggIiMA0GCSqGSIb3DQEBAQUAA4ICDwAwggIKAoICAQDXJeLb8443bNoExaat+SmT0DUBXhFfqUaWfR6k7SCD6AEWd5EAq0w3Ue+rrNM0kanwhMIOjG41cUIdInoLdbgaHVBeBccEvAJNgir6KJpZo/H8VqNbFi+cQ9h4JLGZHNGFAKZ/rGaQ4kSzZ+zecVwYl9Z0w+ipTO/B412lryhgfZhaJdbifP89TEAaEqE1LDUVMPlISMjvDPoHyA6i/CMw0L+QB0u+ziGT5H7dhbBUXWf7vN369iZS0+Dxt7FfXowzHmnNG5ll9wZinCP6EUyVDdBX3iZMHYCa66kKQMICuyplN2jmRfjy0kV3nGVJC/sUvV262AdgZ1t914LzPeAsrCQRQid5M2aIc6wv/fX24tjor+OVpHxrqraMglsxWXQPuOui1x8HmMaeMLB/TOaHVc4iMrVLikoE9wcYGb0V9mnCpBFaD0S9/pzo6sx9xpl3g1O1L0WU0kIx/SxdnMQ9epxGDtJcjxCgZHHM56MdlmclOybk2DQO4cOO8lp0Dj+FfK2eZ7Bd+t/ZXC0C1RgIGKK3kWotVyv0nUtDPXOu0vwTcd/ckeOtRuBjgLHXPjJM/7rTXF2BN7HnwOtPIG5yniRaxfOUvrtmOfhOO61cWvylcMLNaYnHzwTsbtL2sFxIOV0gSWGJAlbmv2FQSgWXK1cWohnLQiktiGWBNVX1aQIDAQABo4IBjjCCAYowDgYDVR0PAQH/BAQDAgGGMBAGCSsGAQQBgjcVAQQDAgEAMB0GA1UdDgQWBBSLrZr8j3XNzg2Naa18TKRgVtm0RDARBgNVHSAECjAIMAYGBFUdIAAwGQYJKwYBBAGCNxQCBAweCgBTAHUAYgBDAEEwDwYDVR0TAQH/BAUwAwEB/zAfBgNVHSMEGDAWgBQLs2g7r9qv7nCldtkh98xEFgfQ+DBsBgNVHR8EZTBjMGGgX6BdhltodHRwOi8vd3d3Lm1pY3Jvc29mdC5jb20vcGtpb3BzL2NybC9NaWNyb3NvZnQlMjBTdXBwbHklMjBDaGFpbiUyMFJTQSUyMFJvb3QlMjBDQSUyMDIwMjIuY3JsMHkGCCsGAQUFBwEBBG0wazBpBggrBgEFBQcwAoZdaHR0cDovL3d3dy5taWNyb3NvZnQuY29tL3BraW9wcy9jZXJ0cy9NaWNyb3NvZnQlMjBTdXBwbHklMjBDaGFpbiUyMFJTQSUyMFJvb3QlMjBDQSUyMDIwMjIuY3J0MA0GCSqGSIb3DQEBDAUAA4ICAQBpxEcsFAQ9oJ+nX90jxdm0zaRrpzJVb7sSue3McYCfUjf3mnVTtU/AsWpQAWg6C/z65YH0AEeBV8qlDb+QoGDWjAc5Q9JKHzoIdJxJA07Q0GwJHAA8mPCkG9OJDMATcdXHe5Rr3gyWFWADZfcBEPdExeaQrAlXycwDlRtYpI86tjoqzb/9OKs4hl8FfK8kjm+9XeWCdiwlmPBMl6GdH/otPRkHzxtWuFv2ZPfVsIzsA04/QwhmUmY8OrCeKMTD4rY4aOrmgjR7MghQRfDoDNAueUDs5yYVdfkb5z6u3kpXPP5H/AsGrY5U3hvjmTQjGKvqc9vaSTsb9tHXk5g+6EQRK+OC8UE5K/lE+bhBiExQTCfwJeWgahGxTeXy807rrE3KZUg4j80lnaMt0DNbMSxhlPF9zLzMK8edCPctFwKvfWMOoE9mTf9giYJ3V2g45mQKOZfk93VcUkcNazLd+iiUzFlYTB8NLmu4Sc1Lqgr507Wtip3UEANCuVZCt/KyK3xupM40vubUyWHk3QxwPwaXy5/3kGxDtKzy7hVTCc5ILiHBnNQyvjiNYU8zOt2Fs/JkIMpPy7sqAvurpoGsSxv/0od0ns4p3Zg2ZIskuaI66ccB/6qLeAp1AAwTbV7lOgtiqhl9vKS9CyFLH4Sd44M+ZiGKGlunx26nOprkYnZSxVkFszCCBa8wggOXoAMCAQICEGgo1Ux+XNq9QzmuDMFaKjUwDQYJKoZIhvcNAQEMBQAwXzELMAkGA1UEBhMCVVMxHjAcBgNVBAoTFU1pY3Jvc29mdCBDb3Jwb3JhdGlvbjEwMC4GA1UEAxMnTWljcm9zb2Z0IFN1cHBseSBDaGFpbiBSU0EgUm9vdCBDQSAyMDIyMB4XDTIyMDIxNzAwMTIzNloXDTQ3MDIxNzAwMjEwOVowXzELMAkGA1UEBhMCVVMxHjAcBgNVBAoTFU1pY3Jvc29mdCBDb3Jwb3JhdGlvbjEwMC4GA1UEAxMnTWljcm9zb2Z0IFN1cHBseSBDaGFpbiBSU0EgUm9vdCBDQSAyMDIyMIICIjANBgkqhkiG9w0BAQEFAAOCAg8AMIICCgKCAgEAniUBZhkfZDTBnTkjYh1xi1bqJdKbH+8nAYK/d9iUM4MYSJtQnnuWZMLQw19F/zKc6BcXvXjtdZhfOgYIKxw3m0ZKkAqwr0aSPjOJKvq45zJj8yPHbtIU+yZY7v4GhFT6wR83qtvU7FYqv0m9zOsC7cZO/KwZtRI1aRWJF02jaOpsHimaCfPOeiHGCdEZ6o8wRmk7aAQrfIot1mNd6m3WOZ69Bj5b7i8RWyhrp1KkaF5MpOquziO/TDZx2oFFUI7Khs7/U8O4Q7Mk7gd6orT6xwode8ZSNTHsCB+EgJJb+LHaOdbJ5+WJBH5Rf/TmamRHSer47Kb2oENT/trDIyTYJdoTLCq3P5TedxxMeBxq+ZqP62oVd3etSYTOEEDHmUgP1ZYegJxzoTihA2/TTSDQtUPk9y54D073vL9l2m2QC1u/3uonJ5lk+Dl8cz3WIdLu1vNTES5Vw9zq8SlX3lGheHOQCy/1yXU2643SbY55XboaOP/fGQGo0sjR1vLrivUu0cyTE5uckHhlY3kExPGen4w682QM/pgdk+KPVqVjUyO4bnMWRRq293sPzaQy/1r+lo3hh3jbcIOoJIVpIMJtEg3lefYqWc/Wq+eB5qCxiC0IjAuxz9dsNq+e+QNn2UFzqatFuHFgWBjUFixlutEF3pLFUBARkM5HzPuvvyPAnwUCAwEAAaNnMGUwDgYDVR0PAQH/BAQDAgGGMA8GA1UdEwEB/wQFMAMBAf8wHQYDVR0OBBYEFAuzaDuv2q/ucKV22SH3zEQWB9D4MBAGCSsGAQQBgjcVAQQDAgEAMBEGA1UdIAQKMAgwBgYEVR0gADANBgkqhkiG9w0BAQwFAAOCAgEASMc3///BaFfXi0NmRjomay/o+t5ooY9H8T00lXraVTH0ldI4Xyy6j6WNUTFqiVVobCtCZGqFJKBRA8fd0XJY7WwejNiRxedJEZ0ZejdYHndE+8IImELETeObig7PQEVPG4BwWYyTgegP1cgmlan3H3cGuMrvnPvoZtrlOeDS0mLDp9S2GJonmyZQSnKX1bNbKqT9Xy9+5mKjJ2YM+tkZzBEdMagBUgjmVAyZYyvq2ITUtAgW775KW4hY9AYWoOt6XeHHRNa7L1VWJfCeDOQPEtvAf69WXcaJDnGpVhLkuZyoZB61R5WSrtBwyJN9fFpY8QXxSrhschiprh9XmSZ0ZvUdD99d8Oc3W1+68LTv5GMHfh8yGGmpcFqS+XmcWNR+v3JdU0YrbqOZYNaFjGZ3Fnav4sUYW+JdCDbWZjcXZfAuz6HlvOaNDWW0VlNdn8ivTm5Rz4i+kuow+yzndT9CYMRx55efc8efytG4bCPqUCgdDkPM9akbQOummOXlD8WSL6WWx9f6PBjuHRthA/2G5yRBM73Y87ZgfPMcggPVYK/f9CCk5IEGIlrMhTN9ZPjkuL+AF9T7IT9jruePtxdE7HIuNckL0IEd6XIDCUHZ3wlI5s23shxgJRlS8z0SSe2dlCKOcSj4wQdUc904CLSFjxRsqgCvQKu1h862OVxz+ZBmc2lnVHN0oWl0c3RUb2tlbnOBoWN2YWxZFzQwghcwMAMCAQAwghcnBgkqhkiG9w0BBwKgghcYMIIXFAIBAzEPMA0GCWCGSAFlAwQCAQUAMHcGCyqGSIb3DQEJEAEEoGgEZjBkAgEBBglghkgBhv1sBwEwMTANBglghkgBZQMEAgEFAAQgtbgZ8oIWmS5XENkuY1VBNvFYGj1GMTUpNuIyT0vMtCACECZn80XjVP6AQSxxVKEpiNkYDzIwMjQwMjA4MDgxNDMxWqCCEwkwggbCMIIEqqADAgECAhAFRK/zlJ0IOaa/2z9f5WEWMA0GCSqGSIb3DQEBCwUAMGMxCzAJBgNVBAYTAlVTMRcwFQYDVQQKEw5EaWdpQ2VydCwgSW5jLjE7MDkGA1UEAxMyRGlnaUNlcnQgVHJ1c3RlZCBHNCBSU0E0MDk2IFNIQTI1NiBUaW1lU3RhbXBpbmcgQ0EwHhcNMjMwNzE0MDAwMDAwWhcNMzQxMDEzMjM1OTU5WjBIMQswCQYDVQQGEwJVUzEXMBUGA1UEChMORGlnaUNlcnQsIEluYy4xIDAeBgNVBAMTF0RpZ2lDZXJ0IFRpbWVzdGFtcCAyMDIzMIICIjANBgkqhkiG9w0BAQEFAAOCAg8AMIICCgKCAgEAo1NFhx2DjlusPlSzI+DPn9fl0uddoQ4J3C9Io5d6OyqcZ9xiFVjBqZMRp82qsmrdECmKHmJjadNYnDVxvzqX65RQjxwg6seaOy+WZuNp52n+W8PWKyAcwZeUtKVQgfLPywemMGjKg0La/H8JJJSkghraarrYO8pd3hkYhftF6g1hbJ3+cV7EBpo88MUueQ8bZlLjyNY+X9pD04T10Mf2SC1eRXWWdf7dEKEbg8G45lKVtUfXeCk5a+B4WZfjRCtK1ZXO7wgX6oJkTf8j48qG7rSkIWRw69XloNpjsy7pBe6q9iT1HbybHLK3X9/w7nZ9MZllR1WdSiQvrCuXvp/k/XtzPjLuUjT71Lvr1KAsNJvj3m5kGQc3AZEPHLVRzapMZoOIaGK7vEEbeBlt5NkP4FhB+9ixLOFRr7StFQYU6mIIE9NpHnxkTZ0P387RXoyqq1AVybPKvNfEO2hEo6U7Qv1zfe7dCv95NBB+plwKWEwAPoVpdceDZNZ1zY8SdlalJPrXxGshuugfNJgvOuprAbD3+yqG7HtSOKmYCaFxsmxxrz64b5bV4RAT/mFHCoz+8LbH1cfebCTwv0KCyqBxPZySkwS0aXAnDU+3tTbRyV8IpHCj7ArxES5k4MsiK8rxKBMhSVF+BmbTO77665E42FEHypS34lCh8zrTioPLQHsCAwEAAaOCAYswggGHMA4GA1UdDwEB/wQEAwIHgDAMBgNVHRMBAf8EAjAAMBYGA1UdJQEB/wQMMAoGCCsGAQUFBwMIMCAGA1UdIAQZMBcwCAYGZ4EMAQQCMAsGCWCGSAGG/WwHATAfBgNVHSMEGDAWgBS6FtltTYUvcyl2mi91jGogj57IbzAdBgNVHQ4EFgQUpbbvE+fvzdBkodVWqWUxo97V40kwWgYDVR0fBFMwUTBPoE2gS4ZJaHR0cDovL2NybDMuZGlnaWNlcnQuY29tL0RpZ2lDZXJ0VHJ1c3RlZEc0UlNBNDA5NlNIQTI1NlRpbWVTdGFtcGluZ0NBLmNybDCBkAYIKwYBBQUHAQEEgYMwgYAwJAYIKwYBBQUHMAGGGGh0dHA6Ly9vY3NwLmRpZ2ljZXJ0LmNvbTBYBggrBgEFBQcwAoZMaHR0cDovL2NhY2VydHMuZGlnaWNlcnQuY29tL0RpZ2lDZXJ0VHJ1c3RlZEc0UlNBNDA5NlNIQTI1NlRpbWVTdGFtcGluZ0NBLmNydDANBgkqhkiG9w0BAQsFAAOCAgEAgRrW3qCptZgXvHCNT4o8aJzYJf/LLOTN6l0ikuyMIgKpuM+AqNnn48XtJoKKcS8Y3U623mzX4WCcK+3tPUiOuGu6fF29wmE3aEl3o+uQqhLXJ4Xzjh6S2sJAOJ9dyKAuJXglnSoFeoQpmLZXeY/bJlYrsPOnvTcM2Jh2T1a5UsK2nTipgedtQVyMadG5K8TGe8+c+njikxp2oml101DkRBK+IA2eqUTQ+OVJdwhaIcW0z5iVGlS6ubzBaRm6zxbygzc0brBBJt3eWpdPM43UjXd9dUWhpVgmagNF3tlQtVCMr1a9TMXhRsUo063nQwBw3syYnhmJA+rUkTfvTVLzyWAhxFZH7doRS4wyw4jmWOK22z75X7BC1o/jF5HRqsBV44a/rCcsQdCaM0qoNtS5cpZ+l3k4SF/Kwtw9Mt911jZnWon49qfH5U81PAC9vpwqbHkB3NpE5jreODsHXjlY9HxzMVWggBHLFAx+rrz+pOt5Zapo1iLKO+uagjVXKBbLafIymrLS2Dq4sUaGa7oX/cR3bBVsrquvczroSUa31X/MtjjA2Owc9bahuEMs305MfR5ocMB3CtQC4Fxguyj/OOVSWtasFyIjTvTs0xf7UGv/B3cfcZdEQcm4RtNsMnxYL2dHZeUbc7aZ+WssBkbvQR7w8F/g29mtkIBEr4AQQYowggauMIIElqADAgECAhAHNje3JFR82Ees/ShmKl5bMA0GCSqGSIb3DQEBCwUAMGIxCzAJBgNVBAYTAlVTMRUwEwYDVQQKEwxEaWdpQ2VydCBJbmMxGTAXBgNVBAsTEHd3dy5kaWdpY2VydC5jb20xITAfBgNVBAMTGERpZ2lDZXJ0IFRydXN0ZWQgUm9vdCBHNDAeFw0yMjAzMjMwMDAwMDBaFw0zNzAzMjIyMzU5NTlaMGMxCzAJBgNVBAYTAlVTMRcwFQYDVQQKEw5EaWdpQ2VydCwgSW5jLjE7MDkGA1UEAxMyRGlnaUNlcnQgVHJ1c3RlZCBHNCBSU0E0MDk2IFNIQTI1NiBUaW1lU3RhbXBpbmcgQ0EwggIiMA0GCSqGSIb3DQEBAQUAA4ICDwAwggIKAoICAQDGhjUGSbPBPXJJUVXHJQPE8pE3qZdRodbSg9GeTKJtoLDMg/la9hGhRBVCX6SI82j6ffOciQt/nR+eDzMfUBMLJnOWbfhXqAJ9/UO0hNoR8XOxs+4rgISKIhjf69o9xBd/qxkrPkLcZ47qUT3w1lbU5ygt69OxtXXnHwZljZQp09nsad/ZkIdGAHvbREGJ3HxqV3rwN3mfXazL6IRktFLydkf3YYMZ3V+0VAshaG43IbtArF+y3kp9zvU5EmfvDqVjbOSmxR3NNg1c1eYbqMFkdECnwHLFuk4fsbVYTXn+149zk6wsOeKlSNbwsDETqVcplicu9Yemj052FVUmcJgmf6AaRyBD40NjgHt1biclkJg6OBGz9vae5jtb7IHeIhTZgirHkr+g3uM+onP65x9abJTyUpURK1h0QCirc0PO30qhHGs4xSnzyqqWc0Jon7ZGs506o9UD4L/wojzKQtwYSH8UNM/STKvvmz3+DrhkKvp1KCRB7UK/BZxmSVJQ9FHzNklNiyDSLFc1eSuo80VgvCONWPfcYd6T/jnA+bIwpUzX6ZhKWD7TA4j+s4/TXkt2ElGTyYwMO1uKIqjBJgj5FBASA31fI7tk42PgpuE+9sJ0sj8eCXbsq11GdeJgo1gJASgADoRU7s7pXcheMBK9Rp6103a50g5rmQzSM7TNsQIDAQABo4IBXTCCAVkwEgYDVR0TAQH/BAgwBgEB/wIBADAdBgNVHQ4EFgQUuhbZbU2FL3MpdpovdYxqII+eyG8wHwYDVR0jBBgwFoAU7NfjgtJxXWRM3y5nP+e6mK4cD08wDgYDVR0PAQH/BAQDAgGGMBMGA1UdJQQMMAoGCCsGAQUFBwMIMHcGCCsGAQUFBwEBBGswaTAkBggrBgEFBQcwAYYYaHR0cDovL29jc3AuZGlnaWNlcnQuY29tMEEGCCsGAQUFBzAChjVodHRwOi8vY2FjZXJ0cy5kaWdpY2VydC5jb20vRGlnaUNlcnRUcnVzdGVkUm9vdEc0LmNydDBDBgNVHR8EPDA6MDigNqA0hjJodHRwOi8vY3JsMy5kaWdpY2VydC5jb20vRGlnaUNlcnRUcnVzdGVkUm9vdEc0LmNybDAgBgNVHSAEGTAXMAgGBmeBDAEEAjALBglghkgBhv1sBwEwDQYJKoZIhvcNAQELBQADggIBAH1ZjsCTtm+YqUQiAX5m1tghQuGwGC4QTRPPMFPOvxj7x1Bd4ksp+3CKDaopafxpwc8dB+k+YMjYC+VcW9dth/qEICU0MWfNthKWb8RQTGIdDAiCqBa9qVbPFXONASIlzpVpP0d3+3J0FNf/q0+KLHqrhc1DX+1gtqpPkWaeLJ7giqzl/Yy8ZCaHbJK9nXzQcAp876i8dU+6WvepELJd6f8oVInw1YpxdmXazPByoyP6wCeCRK6ZJxurJB4mwbfeKuv2nrF5mYGjVoarCkXJ38SNoOeY+/umnXKvxMfBwWpx2cYTgAnEtp/Nh4cku0+jSbl3ZpHxcpzpSwJSpzd+k1OsOx0ISQ+UzTl63f8lY5knLD0/a6fxZsNBzU+2QJshIUDQtxMkzdwdeDrknq3lNHGS1yZr5Dhzq6YBT70/O3itTK37xJV77QpfMzmHQXh6OOmc4d0j/R0o08f56PGYX/sr2H7yRp11LB4nLCbbbxV7HhmLNriT1ObyF5lZynDwN7+YAN8gFk8n+2BnFqFmut1VwDophrCYoCvtlUG3OtUVmDG0YgkPCr2B2RP+v6TR81fZvAT6gt4y3wSJ8ADNXcL50CN/AAvkdgIm2fBldkKmKYcJRyvmfxqkhQ/8mJb2VVQrH4D6wPIOK+XW+6kvRBVK5xMOHds3OBqhK/bt1nz8MIIFjTCCBHWgAwIBAgIQDpsYjvnQLefv21DiCEAYWjANBgkqhkiG9w0BAQwFADBlMQswCQYDVQQGEwJVUzEVMBMGA1UEChMMRGlnaUNlcnQgSW5jMRkwFwYDVQQLExB3d3cuZGlnaWNlcnQuY29tMSQwIgYDVQQDExtEaWdpQ2VydCBBc3N1cmVkIElEIFJvb3QgQ0EwHhcNMjIwODAxMDAwMDAwWhcNMzExMTA5MjM1OTU5WjBiMQswCQYDVQQGEwJVUzEVMBMGA1UEChMMRGlnaUNlcnQgSW5jMRkwFwYDVQQLExB3d3cuZGlnaWNlcnQuY29tMSEwHwYDVQQDExhEaWdpQ2VydCBUcnVzdGVkIFJvb3QgRzQwggIiMA0GCSqGSIb3DQEBAQUAA4ICDwAwggIKAoICAQC/5pBzaN675F1KPDAiMGkz7MKnJS7JIT3yithZwuEppz1Yq3aaza57G4QNxDAf8xukOBbrVsaXbR2rsnnyyhHS5F/WBTxSD1Ifxp4VpX6+n6lXFllVcq9ok3DCsrp1mWpzMpTREEQQLt+C8weE5nQ7bXHiLQwb7iDVySAdYyktzuxeTsiT+CFhmzTrBcZe7FsavOvJz82sNEBfsXpm7nfISKhmV1efVFiODCu3T6cw2Vbuyntd463JT17lNecxy9qTXtyOj4DatpGYQJB5w3jHtrHEtWoYOAMQjdjUN6QuBX2I9YI+EJFwq1WCQTLX2wRzKm6RAXwhTNS8rhsDdV14Ztk6MUSaM0C/CNdaSaTC5qmgZ92kJ7yhTzm1EVgX9yRcRo9k98FpiHaYdj1ZXUJ2h4mXaXpI8OCiEhtmmnTK3kse5w5jrubU75KSOp493ADkRSWJtppEGSt+wJS00mFt6zPZxd9LBADMfRyVw4/3IbKyEbe7f/LVjHAsQWCqsWMYRJUadmJ+9oCw++hkpjPRiQfhvbfmQ6QYuKZ3AeEPlAwhHbJUKSWJbOUOUlFHdL4mrLZBdd56rF+NP8m800ERElvlEFDrMcXKchYiCd98THU/Y+whX8QgUWtvsauGi0/C1kVfnSD8oR7FwI+isX4KJpn15GkvmB0t9dmpsh3lGwIDAQABo4IBOjCCATYwDwYDVR0TAQH/BAUwAwEB/zAdBgNVHQ4EFgQU7NfjgtJxXWRM3y5nP+e6mK4cD08wHwYDVR0jBBgwFoAUReuir/SSy4IxLVGLp6chnfNtyA8wDgYDVR0PAQH/BAQDAgGGMHkGCCsGAQUFBwEBBG0wazAkBggrBgEFBQcwAYYYaHR0cDovL29jc3AuZGlnaWNlcnQuY29tMEMGCCsGAQUFBzAChjdodHRwOi8vY2FjZXJ0cy5kaWdpY2VydC5jb20vRGlnaUNlcnRBc3N1cmVkSURSb290Q0EuY3J0MEUGA1UdHwQ+MDwwOqA4oDaGNGh0dHA6Ly9jcmwzLmRpZ2ljZXJ0LmNvbS9EaWdpQ2VydEFzc3VyZWRJRFJvb3RDQS5jcmwwEQYDVR0gBAowCDAGBgRVHSAAMA0GCSqGSIb3DQEBDAUAA4IBAQBwoL9DXFXnOF+go3QbPbYW1/e/Vwe9mqyhhyzshV6pGrsi+IcaaVQi7aSId229GhT0E0p6Ly23OO/0/4C5+KH38nLeJLxSA8hO0Cre+i1Wz/n096wwepqLsl7Uz9FDRJtDIeuWcqFItJnLnU+nBgMTdydE1Od/6Fmo8L8vC6bp8jQ87PcDx4eo0kxAGTVGamlUsLihVo7spNU96LHc/RzY9HdaXFSMb++hUD38dglohJ9vytsgjTVgHAIDyyCwrFigDkBjxZgiwbJZ9VVrzyerbHbObyMt9H5xaiNrIv8SuFQtJ37YOtnwtoeW/VvRXKwYw02fc7cBqZ9Xql4o4rmUMYIDdjCCA3ICAQEwdzBjMQswCQYDVQQGEwJVUzEXMBUGA1UEChMORGlnaUNlcnQsIEluYy4xOzA5BgNVBAMTMkRpZ2lDZXJ0IFRydXN0ZWQgRzQgUlNBNDA5NiBTSEEyNTYgVGltZVN0YW1waW5nIENBAhAFRK/zlJ0IOaa/2z9f5WEWMA0GCWCGSAFlAwQCAQUAoIHRMBoGCSqGSIb3DQEJAzENBgsqhkiG9w0BCRABBDAcBgkqhkiG9w0BCQUxDxcNMjQwMjA4MDgxNDMxWjArBgsqhkiG9w0BCRACDDEcMBowGDAWBBRm8CsywsLJD4JdzqqKycZPGZzPQDAvBgkqhkiG9w0BCQQxIgQgtjbcUw8JQ+4BJRYa3i6d02c3ponVYOJR1O+/tvEoOcIwNwYLKoZIhvcNAQkQAi8xKDAmMCQwIgQg0vbkbe10IszR1EBXaEE2b4KK2lWarjMWr00amtQMeCgwDQYJKoZIhvcNAQEBBQAEggIASj+0yT/ruWTV+BtWc7aN6dgh5DBlH8jj8pAijzf8u23eMA8nWRDXHCHEa+VMg9yq+mBSeO8dGY9y7T4CJc3C3BLG9FPrMv0a9h8QAoFBqtGak5HrkZ85VTUUbmDpjWdVBtuiY5Y6Rj979Gm0jJGr7HNtFL/kXBy1C0kmP1iKMsymeF/+0oZw1Cgj3wY+JBqdTiDj8sCyDe2bPLCUl9LOMoVOPzRWjVK8jiZGKty5RbnirqLzXtfu9dk82EkFZyvTVOXg3lJ8kyqBfpgmB7m8TM/RemZ4cHwpaiKP6pB3FjQvzC1DPsiD1N+ngD+Zw2PCRWPTaIJ9VLZkFQA/CepE6WV9qiz7lhzfXK+bG/HC737MZrwDvK2wSF6m0Cb+dRDfBVfleX0FCRKcEa8s/qqstFDCG/gm7Mzrsdns4Mw/0SKVqN1BvX9JJlOqhlzlNahdg9heLB8U4uxnvbasHNcVauiLqAU3GG9l/NkZyWXd14qaYpvNLMpg/6KIs6MxSTttNg1EMgA7ir9cv1zsdA2gyJAlf0SIMjESFEA3/di4RJ512rzQ4sCeEiu0mDW1zUgU7WSjki4QvkWpNnnYASQ6SShPr2n6azwe3rtWE12pTFyY84t+6/0aeIjjTc+0xDos491Kx8R8WqZG/Emc74+QN7s+hRmEXD9txmXz2GQbqDr2WQGAUq8+ncyKpuNTAm/GKfZBOpMH8QVsBUuEstuKIB+vUps3glqhtZ99pu3/Pxq78wHQ6/zZBxEV26sgrZ1QQ4zMAwL79Ch/1eAcIMsN6dJjz3iRprWj+yOkT6wruMU00BJvQTYRsefPJsz0p62rbYBwMWmNWwdvRj2S1+uT8E6VeZRy6NwPABXIga5yuYOK7xephG5Y+LmXTzB7JZVXVuTRmsf2kUHuOmSn6SB0US5YMsEkUxO70HHh+aWbLEALx7ZbuDdPLaflymzkB5EFEQR5BpPLPNABhJRPXalbscliue9zkwH+GkrdwZUbLSj6784QHizhcbjrN83RyBBKnD2cXcXFHtwcpU5TZH0om9JBoUG29oimvDklsR7pfxazB1dePj0lhgrPGkMOscXZMloQtxXYUXrujm2+TDSEayNN2ML/FuduuyA5xNXuzk1/hXk26bB3pjZI45eRJZsGKAHmp6l+m025WywniuyzIC6L3iEMBpLXoF8PKv9uU4OAHKKJ/+EARkV4aWYAAE1NACoAAAAIAAQBEgADAAAAAQABAABREAABAAAAAQEAAABREQAEAAAAAQAAAABREgAEAAAAAQAAAAAAAAAA/9sAQwACAQECAQECAgICAgICAgMFAwMDAwMGBAQDBQcGBwcHBgcHCAkLCQgICggHBwoNCgoLDAwMDAcJDg8NDA4LDAwM/9sAQwECAgIDAwMGAwMGDAgHCAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwM/8AAEQgBgAGAAwEiAAIRAQMRAf/EAB4AAAEEAwEBAQAAAAAAAAAAAAcEBQYIAAMJAgEK/8QAShAAAQMDAgQEBAMFBgUDAwIHAQIDEQQFIQYSAAcxQQgTIlEJMmFxFEKBFSORofAKUrHB0eEWJDNi8UNygjRjkheyGCUmNXOTov/EAB0BAAICAwEBAQAAAAAAAAAAAAUGBAcAAgMBCAn/xAA8EQABAwMCBAMFBgYBBAMAAAABAAIDBAUREiEGMUFREyJhMnGBkaEHFCOxwfAVM0JS0eFyFiRi8TRDgv/aAAwDAQACEQMRAD8A4H09NtZlaQWyRJ/KMT26cb0tq2oWN6QpJInGAJx3mD/LjzCwyNoIjoZwSIMR0nJMHhUwz+JlCEqUFTuSJ2wOhM4EGf5A8YvcLUyFNvKQoNpbLZThJ2mMds5xMxnrjjammV+8CUpCQFBRSsbSQDiZzGMA/wCPChmhIdb2lbRcbITkjaFHdux+UDMf6yLbVfwrrjbfhXNeKI670a7bam+GzJ08p8i4pIeDI+U7VObklflFMlG1fSeMG5ws2HNVIWyEMbEuEhtAQJVIbThSs4GZJkZxPSSVFutNTd7ghNI0p2qqCUttNq3uKJIThIzmQBJA6kA9tNW0k1bxQgK80TOTtE5MgSBmegwIjJ4lvh+54Xnw7859Ia5sKbeu/aPudPdKJq4MJqKdT7KgQhxCyApJMbhjqciOPCt1FqqiNnfdZqEqp3UOLQpK0H0LkDaQQNpAKusdDgTx5bbKU72kuJQopSFNq2CMgAnp1AOc578TjxDeIC++J/nxqnmFqk0SdQ6zrnblcG6GnTTsB50ZKGx8qIAx1J6knJhbCvN8tbgcPlkDdv6AiDJESIImft9OPVim3LPlzo2/aR1pVag1kzpi6WO0Jq7BQrtjtWdRV34hCBSJWjDCvLWpe907BsIycCGsraQ5tB3ISZCUyhKgkzujoTG7qMAHjxT0riqcpHnOBI3uKbTvAgnJIOIKhnHQdONrLKW9ydrZbQN4I3FOU9szmInBET9eMWYPNamWi+ny0oWFYwFbiI6CTO5JI3YmJEYMcOFDaFqrEoBaSdqgSJM/3s98T8pO3qCOPlv/ABDlS5TtpO5agNskFw4BTAMHvJOYERmOLH+KjxBVHjk1fo5Vj5caR5dI0zpqk085T6cpvLTdXGpH4l0CNzy5jaJOACVEDjXBcdLdypcMIx4smzRzQKoBQ2J5MNF6sc9IQhCZKjgJ2wQkTJEeomJAnglctOVGtOeV/boqChqairq2lQxRslyrqPLSVqXgblbUBSiTGASRHSVWfwqXDl3oBOpbtY7oxa6x1dC3cH6Yhj8UgJK2ypQCdwCkyO24GAFZQp17qOgq1P2S5XDTLCWnGjU0rymXglSdriSUkH1IJlMDCyOnzT/uIhAdU8+y2huclQDHQnDRzPT5pAzqjT/LTSt9tLdgtN8vt2NOmmu9Qp41VpdbUpbpp9iocU7OwlY9KUgpAUTxD3bDWXVhutvtQaOmQmWmHCAsY2wEgbUKJR7SYJMYlydqqHSr6gyyHKlTeHHEhbpJ6EJMmTP0iJ7glirkVV/qvNW6tJWYC1EzI6wR1HUykfrkDiO+WR5wB8OgUiOlhaNUjs/mT6LRdtUt20KZtqPKUpBJf+eqWDiCuJiPykRj6cR9GmlXSqU68A6+4ZWpaip1RJIlUmIx1JOD2McTOl0s1SHaUbAoztgAoIiMAwf/AJGehM9ndm2/hmYQhunQUEBWwbtogyTAHY5wcdiJ4FVdcyLYnJ9E12fheesIeW6GevNRG16FYoktufg2kK3BKlurjHulMdMHIj29jw70FgpwMArDacwkJCgATugTkZkn+MmOJDT6eLiSt8EiRKSQQn2UojGBmJ7/AK8bBTBK0bFNLJO6CQkHtMGJJgZAHf2wDnrHyeisi3cOUtJggZPc/omlmjW69KtjCQQcqIASSenWIGfaB1OOFNNRIUryxuU6QQVJEg5AGSMA5k/6cL6WhFepCZdrJXtQE7giCQBhIk5MCTOffh6p9Opo2UpuNQWkLI2UrABcBIyITIMjvk4MweIfiNbuSj7IZHZAGE1ULL7lak06lB0kLbLa9uwSBuDnpxkRtBycA8PFJpR2qQaisecWVwrc4grWfTgJ6AAAGCT1AxBI4kto0VWvUwXT0LdspSQhSn8uL6dJOQr2GBnAzDsnQrpfQlbqnnFnJdcJ3SSYjtuxI2g9sHjk6qc7ZuyltpYmfzDkqNt21q3FMBE/KVbt7y1A4BBMDM9CMZgjr7fcVUNpT+9d2gJMqJQM/NMACD1AjH/u4m9HyyLeahUpWUpIJLSGup9SiBJjMyQN2ZiQ40OlKFhgLdq2UuFtUBJLmw9d3sSZJyokkGJnjeKn1nLyskr44hpjCHtBYqmt2bKbaFdCdoySJHQ+oAGDtMEAz1hxouV9W8w2XXvMDZKClAhsH+6CSZ79ATjBzAnzVEyHHW7XQV9UtRUQ6GcmB1hUA9Ae4wJ6QfSNFXevXL7KGGkEgh55C9mdoAAmFmQJBAwczEmaeKhj3eclBZ66skOIxgKFv8urdblDzqlKXEAqaS49ucyTO4JQYA7hRn5faApGmbYwhfk2x+rLajsWphxTgUEypJKyUJAETMkbjIBMif0HL0opAk1Tz3REsJ8tKTtMoMCZkZj2PcyF9Dy1pnVpKmGycICFQSgAQlJgEgmRCQCPlPUmStPU0PJoQ6aluJ8xchqiGnNtNZaJtDw3JHnh1J2gGAYyn1HoY6SZzx8coa95tUsU7AdSFjctYWiQJ+VO8iCTKpAiBPY3WXkzdK8rNsoapllaipZaSQtwjAlUQR6R0Ix0ESOHK2+EK8XVlLtS1VO9AFLISASmPnMEGAQRHuAevBinbSybtG6GTC4M2yFW1+g8+RuqXGnipxJp2ylTm2SFDIUqRtkmEiB1kDhHU2pCH0rKa1pPVC1BIKyDG6JiSSOpPVX1ItNcPC01p1pa6tt/yvNBkVKVlZ6JnIlUScdYGO/EduPh+t1zcWilcDq/Una2tCVpEDCQkgAR1Ix1gSTJVlsiePZQievrIz5noAptX4Yuf8yd4CoKXd8wCrfKMKCYEFUDG6IIl0p6N1LDxAZVsQNgKpS3khZhRB9QM9hMkCeJ5qvk87YVLSrzXElKCsIdCWUlO0JJ+Ug5UIOcmY3cQOvVU6ceQXFuqidrqfQRClQoYkHGY9OJkxxxko3xHHRdYKqCbyzDdPNPVqZRsU2PKb8xfypSQkSreoLMBICspIO7ESrI9uimXTz5LjwS1sb8xJQreoEpSVAKkzuEJSnaVAJJJALSzqNl5lrz0hQQYS4y2VrRHdRmCcGCVKJkfYfBdKF6rU1TlttZAStRJC1n3BBMH6ghOBAIIPHPxnMGFL/hdPIOhC83+gFtJVS4QnclaPPQrcADsBTgTuVJUTuAkbk44Yfx7bdQlBU++wg7UbylCRG5KFAidhUmO4kySolW3h6dubbwSA40HGR09OFgq7iAB0ESSTmdoHEa1JRLQ2tTZSph1K1gqTvcCgDtVHclJ7jpIGJ3eGoaeaXrnYXwjXFuFovGkUB1yoYD29LimlNEq3IWVmSd6CrJIMmSIUQUgAG1/wAMf4hWhfB9S64puYnLyk1kvUFoVQUTrjLSnGnJWT8yYCF7huUnMgnuriptnuiC35Lqwwy635jZHq2gCAdzmFAzORuhEBU44W3SxLvCC+2pzzwkjCyoMkkqGVZBEA5MDeDAMgcWz6HZSbW2tk7CCN1IblcGrzcal5tX4Yu1DikNNna3TFREIiYCgCB0O336kSzlhW6KRZ9S0OrtNXG4uXO1OMWh2jqvJbt1VvTDziNqi42Etx5ZOBJyBgTW6/uUbbjdQ46H2lek7ilVQkH5ljChAIVGDKwQDuJ4mFoLFzQW14cQnaosqS2VkoSYiRPpkykCdpAJzw50NS2pb5TuqxudI6ifh/JMWvdPNsXRuo/fho5b2LEgESQUwNqe/VOUjpBmO1LJ8ko/+oS6oICSna4tQgjESidwBKTKj6RuiOCbW0jyGXaZ5FQp1n0hbidqXNgJS2qBiDIwABJMKk8RW425lFMC5tQiSFLgIM7SgkkpPp2tgZE5BABniNX0RwcIlZ7i1zgCU58tuVeqdW6Hvt8syGa+i0q2h+rXtQ4Utrc8uQlRB2iVDbIBUYUDuIDaKug1bZlvNFtxLHoqKZx1LikM99ywUlaIWkqXBJUCkAAyN7ddX6fttfSU1RV0tFXiKqnTvK9iin0FI6hJk7VAdZyDJg1nuT2ndR1IBYUyVB1TTjW5CkFRGO8jPqSesAH1AcLUjSOafGVDRgKrLISrZucSoKwB0BJAEfQfX6Hrji9fwIdLeHHVfi6LHiVuFHS6V/ZrrtuFc+ult7tcgphFQ42QUNbCrbkJKh168Und05WW610VbU09S3RVwWlh9Tag06pBG8BREKiUzGRjHGuhqPKRBUVoJCUysJ3IJ6KAk5n+BPvBFjnugQ3GFYf4n1n5QaY8cWtKXkRV/j+WdPXE2uoQ4txtYLY8wNqVClNpcKwg4JSBnM8AWludU20GEVLrlO1K1Nq9KFEp2pUUkgExPqIBgAdJ49WOxV+qq+noaSlqK2teO1qlYZUt1z/tCEg7iCCekE5zGElTRqp3nGVtIbfaWUKCkjr0IBxI/iAOnWePXHJyF61uBgr5gKVkDYZ2kj5ZJk9zETJA6jOQOPrjCFLSt5JdOyTuUPlMGDjKiD1x3zGePqArytwTMynCgZBmf1JAInHv24+fgy75yk+ajcduVEAKSEqVJIJwIzgDB6Y41Wy3lSENrRLYKN0ygnqACD+UnAgED5hMjj40ve6y+SqW1eYpRj0ztEmczg+o/SPpJeZ/J7UvI/WYsGq7FW6evKqZm4fgK5sNuhipbD7S9g6JU0tKk9ilU4HRBoi101/1HaaGquNLaWq+qZpl1j6VLZoEKWlCqhSW5UpttOTtBnarBPHhKwKZjxCUo8LCOWStF6TU9/xN/wASJ1R+HJvoQGC3+BDswaaVBZTAO8AnrxB6ChqLg80huUIQPLS4tMGN24dP0jp0mRB4k+uuX9FovmRe7RZ79R6os1srnKaivdOwunYvLKFEIqW23QHUoWmFBK8gKEgzwusNkSEpW4lxxpEQFhUKOIgD3PTsCYg540LnOOGopTUTD55dgvGjdCN1q22UIacClpbUBg5k7U4gCAVdD8pGCRJq0RdRyuVSVlpeeoq2mM079M2WngQqQUg53JiAobYGIn1JH9Jf02WnCaZpt2rCJ3YDKMgqkDA+sYjp9fNbqI0b3n1S3HnypSkJdXjptATIKoPsE9EmIyQaoZWU27Bl/foFBuERqvLL5YxyHUokXnmbqLmJam9Ou3O6V1pVU/imbOxufRU1akBHm+WSf3isJmNygAPYAX6t1LV01SaYU7tArcoN0oSUPMhJAIUoiQN0CEgeoK7weFegucGqeXvMOx6q03cqqxXnTtY3W2usYBbXQPNxtcQgzuKTHzDb3z0OrVGprnrHVFxvl7rai73661DlZWVLh/e1Dq1lS1rKY+ZaiSBiT9OINwuMbDrkOXH9/BE7HYKms8lOzTGOvRM9tsq3ypSm0sJUtSy4lUBJkEK9UdAJgndIntw60lKzSOhbaVuLlJUsY9P3MRJjH2/TVS0j1SpMoKvLMBAAhIk4wDEQegxP1PDwlhq3HzKoh12EkoC5UjBypR6CPoTjHQErNVcppfw2HS399Vatn4XoqH8WUeI/5/IL1QW0oZlKPw7LYMuBO51Ak9AqMn2xMdT2d9M2Fq73qjomBRsvXKoTRoeuDvkU7bqykSt5e0IRMkqIAGJMcRS56pLxLTQDhSTASkhIVkyAclWCZVnqRHb5QWisury5CnlLELgSEzJgnpBkjJzkfcW8MbvzTfTySvO2B2A/VPN8ffp7pUUhLVU7SPLaUttaVs7kqIJSoDaoYJkdeuZMprTb03l5CFioqVgE+RTNlWCcndnp75gfTPEn0nydqbwFOPtF1TKd/lIEhInBMfljvAHTMxM6tnLgtN+Q2hnytqVLRO0EFQOUIKirJ7qHePbjmY3lusjA9f8AakGpiiOjVv8AM/RRK0aYcUPLWGaZLYJUinT5iwDG4FZhLc4mMn744mWmtJmjUTR0rKEPN7vxMALB2/KXFSpSvqmB6fbiVW3QKqOm3PpYbT5nloCUh56okY2IHpBIHaevYZMoouVVyvAUC63Z6dZSUPVKw/UuQSYS2J2CFkDcZABH14FTV9DFvI7V7uXzXV332UYiGkeu5+X+VC3GQwz5wWCXEpQp0J8pG/cYlSyVHviY7YBgKba1VVq1U9tQupWiGgtgOBtBkR6sGOgxgkiAdsCdL03ovlwr8RdK43KtUYUKypK1Ff5gltA9MkCE4PywcCd45mvVVOpuwWGrca/6ZWG00rY6BJCjJIj6ie2M8QH3USkCBmG/L811ZRPYCZ35P77KKUHLGrrktqqm6pUFCYa/doaMCSColWYEQB8w6Azw9UHLmnpdw20TCgU5SDUFZBg+o4nBzJgwAeNdwvl+q0OKcTbaZskifMWsogTG4qCR1j5sSMRPDLc7hdKyWxXklxZCvJSC0nEk7SnpEH0jogGQJI7Nkc/YkD4rGtib6/BTb9mW2gC0KrKYkD0hcgge23APQASZ9RgYke6essbK2wblRgJSoN7ErMGJGf1UcbYgduIExpOuqXUOVT5JCSvy1O7IHRRUEdc4+/c9OFNPpOqbP7tVKzuJLBNSRIjqfQVdzB9jg54lxQU5Pnl+S3Mrx7MaMmjbHpjUSwmtvVtZS56pdKhtSMEQJET7RI6Zjgu6WsugtO04dYq7ZXKWAUCmgboJmOpkGepmR3nioCdI11SwEpuYfbwAhNQAnfOT2AjHZQz3yeG6psPkqUlC/Ic7KZqFbiR12wQNoxjAEjrJHBWntkDjmOYrhI6V3tN2VyNR+Ke3aaBZprfbggApG50AnbAMgdZj+U/Xgb618VLmoQUtOmkB9IAn0me5yYx3iO/1rk9cK+jQECreUW0eYlIeUpSQkbkxGRIJjdPQZgYQp1umld8ioShCEkqKgsuBMpEqEplRGOoUYB7DDLSB1OPKcqPJTwP2e1Ei+81K91TynEIUtzcFnapQB7Y6EHPbKsRwz1HNV3cUllaUOKUpQ3KlRAAAg5IgTtAz9ZJ4Y6S6t1ZG9R8tSUqUpLmAowAOhTgj1dQADkHjWKFis/duH0q9UISV7UeknMyJ9cYPf6AFReKiPBPJQJrBSTDDTupzauZzt42pqQthIKWQFrK0pIwQoiVR6QduSQU5PCa8aH/byEI8pDqXk7liVKEqKkgewJAgRAGDiSBmgKLTNu5e6ht1ys9bW6kqXKZdlujNeG6eiQFq/EB1qD5hWlMJ/ulJxmeJXoKjS5XooaxtUrIUyogTugBQCSYjt0kYmCQOD9HcmVAAPVJV0sM9Ll7OQQjvnLe6WBa1hlTjZMJKkgpa6CYSSAO8xkgfKQOInqKwvU4adbbWh5lYQ2EJI3yeqlHcQcntMfX1G3l8at7VCmlcp2WnmEiS7hPsIBBGCDmO5mTB4F+t9BU9wbcLZ8hodF7e4VEjMgTOZTOZmABLmtrXeZiXHXeWLyHbCAVf+/p/MZVuUVQptDcEE9JgFJMx1EY6AyOPDVM5d2ko8k1AUoMnH4iVbySZGSokkx1zOCY4m995UuwssL8t9e8nIASvaREqO04URMgg5mThjunL26aTWzV1FDVU9JVJK2lOMnynEoISqJAlMhQgHE5JieBE9A8cgitLxHFIzw5Xc1G7houronAlXmMu0z8pcUlaA0CdkZSRAASSZlISoAnHBc5DcmblzOU1T0jDrymQViEuK2FO3aACNqkztMEfQ54nPIvw/wBZzopEsW6ldqapgraUlpkykgg+YpM4MkAZIj+JtD8P6tpPAl4i6N3VlA4advzCWHkbTvM5g+kEQrb2MduPI7VO9viuadPdArlUxxsdJCQSqSc+PCNdeXVNUViqR2kS0sJqGlpU25S7p2QDlUBM+3pyJIkMaavNVaakhZLJZPljakgoVIj2JAEZTAgAeoxx0t+MD4tdLeIzmm1dLFRJom22m6Z1p5fluVK5j1pR1klIwT0H145tavs4RfatbRSX0tytyQsqbAKR0EgGRP0I9G7HHtLL9zqcA9shK91oTX0Ilc3BR2qeUl2oeSdj1y+3TfsDU9ZUW2hcarkuLQ8wE7g40DKAJQQcAwM/lIvvdtVSvuJBCgo+WlGFKxgIBCSBBUev/biCOPHKu/Oqo6hl5biUEHzSUHf0T7YAyDug4Ik5jh11NStqXUtKTKVkqKULB2naRMgfbMdiYERxYxayenDlSkTpaWqLHnkVH7NVpYuAQHUtE7mlOOGPLWVdYB65T6hmFE9AE8NvOPkVqCxcpaTmGikpxpM3T9g+Yl9LjqH1t+aELbJCtxCHDJEEglIECNrla7R3ZtbUuKCkk7AFgplRJHypQjcQCJgkZHzHj1qQp1HpN6gR5raS835jaTu8xwgHfs/KrJQkbQR6j2PCNXQhhLVa9uqTLEDnkq4aq8UWtOY/InRnLK7XyorNG6DdrKnT9uU20G6FypWFVCypI3LUqCJcJhOAIPEERUqWhtSVhuTKilUDBAnMdSBj69gOE9PSKShtwqb2qMkHqARicdJA6HEn6nhbb2UpqFlbZWoLQlQCsznI+mBgnv8AXhaJyu4GOSJng18W2qfA34jNPc0NFKtKtT6YLxo0XOlFXSFTjSmylTaiCYCgPSQpJSCkgieIjzE5h1vOHmFftVXkJevF9uD11r3GW0stuVFQ8t10oQnCQVLwlPRI9hAj7TZqKZHlILq1J2JgKyI9RB94nr2H3ixHKn4ZXNvnV4OtY88tN6a/aXLrQFQ7SXer85tp4oQG1urbbnc4GkKQVkTtmZPTjwDKwnCryw0HSinQpKglsAq3AhU5UB1G337yPoeNiKZTziCtLzyOu1YzmQMdZJmBPf3k8bHEkupR5qvMYWImTtUUjOCTOP1gdJx6dZ8wy/8AuS6lKj5gKVEESDke+T7gmJ7YsSysra3V1Y6/Vvv17qkwuoq3S44oISEJlSiTtSlITGdohIwI4c6CjZpaqAp1+rXCAEJlZO0QkADH5oz9Zzw1jz31oaTuBUCodA2kREe394SZ/meJhoHRjlVWkNFdQp9flNPBtQS8d0pCSQPVmfp+ZMSRq4KbTjfDV9abTQpClqbeqVDaNhG1CCMCB+bI9OM4MiYeaZurcbWtx5bZUnKSfS2nAACo6T1jCsATPBv5xfDj5u+HXlRZteap0fdrNp28pBo7hUM7WxuAKJJ+QGMYBVIE9OBFoLWTOhNeWq7G1W68NWWuZq1UVa159JcEtqSotVAkFSFhJSoJBGwxOQRuHBo32CntBkGIzk8vimq4XVu1JcVQpddIBS2tYS0qBt3GASpCcjOBMgEmRw3t0ZQFKqVuPVLytx9KQVEDBySVZHfsme/C7Wmr29a67u91oLZb7WLrWO1CaSlQRRW5LjhIZaSpSoaQFQgEnAA492izraPoc8tYMrdWPWgHAzMx2gdMe+B9XcSweVM1k4ZE0w8cavT/AGvlO2UwncQpyIbCoKgAcqPt1gGBjsBlwoLMlKC+75acDYlXqJ/9o/MSe+QM8bqWmaogA3tST1Uo+t0YH2SR9M4PQ8THk7yg1Bzm1O3S2CzV18dbW27Vttfuwwz5qW/Ncz6WzuA3E4BPtPC7LK4+c9eqtSKnp6ZmkkANHIbBRKtuYtiXGk+Y3vO0AuFKlnAzH+EgA+3dvaoqq+ugpGxlAMLUNqQRjI+wPUni4Pis+FZdeTqHL3o9ys1FYFPluoplNbqu3jO1S1pPrY/KVgJKQPV808C/SvLu12F5O8m41iSnzUobJRTr/wC0dSSPzEznAxxyY8HfO3cryKugnj1R7g8gOZ9/YIeaO5UvXctw044nCkkiNxjEf6kiOn3KukuTSGtodipWHAhKG07khX5RtAxJJ6GR2nrxL9L6fuN5bLVNRIaLkJcWr0jckkgpPzK9UAxJGfUI4n1s0Hb7OkG4PVlxeXDrVBRgpNUkemVrUdwbIBJmO5gCeBFZxBS0x8nnd++iJQ2+sqBpcBG305/NRSw6Odvj6qemS6psDzixTEEpn8xWfSnEzv2zuEDqOJKrRNt0VTvLr62nYKAElqleCnFKgbdzsQIJQSEJ/KfaeJZbdM3zV7H4G309NbrY0ZTT0DRCXEKxlzqqAQSUz8sSDMP1h8MlJQu+bc3ad6qWIKnxIIV0Vt3GDgmVqMz9xwk3PieSodiokwOwRiitlLS+wMnuhczqLctw6dtFUopQpAcy2FDcTlZlS8AzMTuyIzw+0HKjUurnQ7cLkqholqCiwykpFQhWAACd6iAUnOFEZCuxitejLLZyip3t1amgmXXwUM08dAlJyU4OBjGcmQ9LulNb0KdbNLTobJC6h5shTmIgDBjESo9cd8r096I/kN+J5ojoLx5QhRpvw7UVkWsUFqLy1IIU++lJedk4mcJzuI7kK/huvXLQ0aCqtdZKtgCdgS6omJGFQkCI+VI+buAeCkzcF3LezQMVtw8knaUU8JUOm3smIMHqMkGeHBnS9bQpUtyno6RtsFe0uJU6omFbiYMEdcDv3jge68VOcyOXjadjDy3QGXyzqbvUlVBZ1LdSA35y2h5aR0O5S9qZEwNoO2B068bKPw8Vcf8APahapEpAAabk7AkCQB6QfboRkZkcGq8WOsvDYapA/VEplKWMCffpAIG7MJjpkSQP9fcsdQ291zzmKSjQEpkurndgQr2MiCPT3nuOJdNdpX7B2lSGwtdtsE1WzkhpZloLXUGsUj8x2qhQJgjMSciZkwB1HC53lfp9tLpSxO+FgthtJIifyjrJnsPtGIXdbddaOoT/AM4wtwpKQVpdkKwkJKpHXpB9z0meG1l65VLqUNugKSQlLagtGyesbsAAGSRESnME8T2id24lJC6/dhyKfNZcsKByjW/TwpyJCSoSFR6QYnHQ49h9QQzrrTn7GacBQwpsp2oVEQnoenQifoR2MEknGgrnUkCtW4Gyn1OOqKkoIxuEE9fsT94HHjVXLWh1wyWgrcpUoKkqkoxBV8og9sR1M9DwQob1NTPAe4kLm+lj6hVJul6cpalQDi2ticBThSFKEAGSBjHQd8Y4Y6u8v1TyQ4QtWOu0FZAwIEkYj/CenBW5nci6zTq3D5Ydp+iXSjy1tpmNoJEQI6E4HtwM+ZNmtdDq+5LsFPd6XT4XNG3cnULq207Ukh0ohJO4Kx7Dix6K6GePUyTIUGSliDgA1JKDUlVbKplcuqStW/blKkmSn0zJ3GO3tHY8FDlq89zBp009KXqyoQkvqbSgb2xHYEEAAdenQEdOBHY6dbq0spSAhSPm2kJUqMAp74xnA+3BZ5RcwLjyXvLF9tzTSq1ttQdC6ZKkOT2OTI29egBgD6NVmnZJI2OqdhnU+iA3mmnigfNRtBeBs07AlPdJdH6GoCVoc8tBle0kAHpEHsFQSSqMj7cE3RWuKDU1OF1KG2q4He4pKSkOZEEAHpJOIg5/QB33mSrV+pauvfZha1HenCQg9jkgdlSZ+kHhzsup3KWubcQ8Q4FQFob+XBMgDqo7hkAwEkzjgu1zIpiYTludvcoLS+aACcYJAyPXqFZHVa2qhht9DmzzEwlUpJQD6SkzJyDEfXvw209GtxpLLqXHEOKSsb25QgEAbhBG0gYx7+4Ewex6se1DaU04UW3G/W3uG4KkEGOmPT2Hv1zMr5Ra1p699NtuLbb7bihtKVb/ACifckbcwO2ZBntw322t14aeSrbim0DQZmc19es7TPnE/vErAW1uJUGnEj07vVBG0wZnak4Bzw7ahv8AceaXL21WjUF0uFw03o1CqO2sOKT5dsD7kqSkH1BK1okROAcpiDOrty7apKFa1O7miCrzIEAQdoVjsfuMdAeog5hsVFlvaXG2VENhRgtDeQRknA2wDO4gDpIPDTLSZjEgGVSM1w8OfQ47o7fDo552zwv8wnn61Ka+ndSlDSSAFGQYgfTPfse3Cj4i/ihtXOHXbFfb6b8KhKdqhAbUpO7IA6lXQYOJiOpFdqYrojvSCslIBaQnzSRJg7cExMwqMp7RmIc5L7VXuyU6hLpZp1FRA3B6MlJ2gzk9zAKI6K2gVV1b44PB07nbPojlNViQblaubLBu9ubqGlOICVKR5jbiSoBIUkLlGSJT8ySBJVG6AQIbvVPPUdKtbe2pYCXG9qtn7xJIxHqSZPeDjsVYO+mtK3Gs0fQpvVBcaOjuyPMtlxqaZSW6ptHpc8pRML2qJlQ3RAk7lhKQ7zT08dJ3StblJcpHgttYmV7idxSlQAB3SATk/aRwrXCkwBUN5Hn70VtVzbK40zuab9M1oermKtCFoc/6dSEJKPKJ3H0JECTEx0CiBOczHWtLU0TLDtU09TuujzWC6VNyDEEThQO4+oAgboGdpI1UtVtuHmNhltSFkbN37pB+SQAVSoR23Yx2gFnmTzkv3N7Qen13y5GvRpi3NWShDoQHqejaJUGSlAG4BThhRkkDr6Y4ZeHq/VCYnKu+MbToqmzxjY80N77tr6CXUkqQ2CmUiUknAnEmMzntAiVHXbXx5b7a4dpnyFuEIcIlJ9XYx6VK7gACSlSZPCpbIcDW0oQEon1SsNAFKyrpuSM5GTgGZ4aKtTVnrGXXAoJfJ/dhuVIJUCpHWd6imPUSMzMDiPdoyCHKTYpsN0lVasNhe1GqlYaQ47U1bqW2QlJU6VJj0R+YkREj8wgz1eeZHKi/8or4uz6psV605eW9rzlDc6JdI8ErTLSti0gkKTkH5epB68b+SfMa5ckuZWnNYWd5K7vpW6MXOhKkB5CX2XUrRKchSSUkGRJAgd4L/wASv4kGufid87WNf8wKawW+5Ulup7YzTWemcYp2Wm/XHrUpZUpxRJKiY3CAAOEpMe6r28nc+6jepCm0kLS5CCCfftiQMRJPtwZuSfi15o6a5S3bk1ZuYN003y01/Vss3q3OOgUC0KWlJW8QgqQgAhTgQYUlJkKiOA44goW6d6QUEqhUpChIyZOO3aTOY4ymSplxCfMJbcWuF7PVEkpUmcIJgd5jrgce5XuB1T3rmztaT1Nc7Y3cLbd/2bWO0qa+hUXKSvSlZQHmlGCtDgRuSoonaoGOg4QJZU47t3gJUghG4hIj04AHfrMR9T3410sLAca2wJW4pUdQPZJ6GT0+okzxY3xI665Ocy+XnKum5W6BvGkb1pywJptbV1ZWJebvlx3JIqGEpUdiI3dkfMkFv0bjqdhldI2F7w1o5oFWKxJqXyspUW0D39SfaD1InqB1j68S7S+uqzReq7dcKdKVO26pbqUsqPoltW4JMYCZ2giZgd+GW4X1FKXGWlEMNIMrSdqRu/KCB8o9x1IEEdeNthtjlWPxFUC02YPkKASJmUqXJz2wZ/WZ45FzQCSUapoHOeIIW5J5rp78Qf8AtCdx8dfgioOWb2jmdO1DjjBvFybqt7dSWQAnyRA2BRGZMwSAB145q01ucvaEpbaU3TI3EnJW4Pm2z7ZnuTHTh0tNjeuS0lxtW8JPqJytOeojAj6gmOwxxvq69ptXk0aZUjLj5UDtHzECIH3iZzGAYFVFY+TZg2HVPto4ep6Nuag4J6dT/pa6OzMUikNJaKtoO3arKBJx09JyQTP29yueeCQaen2peMlIII3E4Kj2H3PuI7njQw0WKVCUqWFqWNxSQVbkiAkZx1z0k/QHjegpp0KZSgPVDp9SU/IgCMlXWYTknrJ/UTI4A6n7lPFLESPDpxpb1PVeyAKjaD57klSCgHcCCDCR2yfc9vaeOhvwaOV34TQertTuMJSLpcGLfTL2ypKGGytyDAkb3QDjqgfc0k5Eck7xzw5j23TNmNO5drutSGEur2NrKGy4sqVEYSgkT3A9+O1fJrk/Z+T+gbZpyxU34a12lnymEE7jJMrUpUepSlEkqP8AIQBDmmL+aE8TyRUlIKVg8z8H4DqfeU92q1+Vn5SBhQOR/X04h+uvBJy95nXN6vrtPsMXKrcL7tTRrUypxcj1LSPQs9zuSZJ4LFstEQQnJ6RiOH+isxaSmEySPaI/24jPYHDS5IUFVNA/XC4tPoqg374a9xonyrT+oaBTClSfxtMpL6Ujp60gpOcZnqYjhpR4KLvoF8OXmnfuaVKK0MWlhb6KpSgDtUojcsk59QxBxEDi9FDb5QkJBKVGQY/jw6U9tIwNyU7cgHEe3AGs4ehmaQwlpPUJrpuOLhGAJSHAfD8lRK6aS1Zp+1MOI09cbHaCAEufhl+YsEdFr6JAgH1EdMcJbToKpr6kD8MtbzagnzFOCEEACZUUgyZHQn1fbi/tXpdi60D9JVspqKeoaUy+2uSl1BBBB+/+/XPAU1d4TK7TrS1aVZo6ppCg40mseU5UJ9wkKwsjAkkE49uE288LTUzA+kGrv3TdZONIJyWVQDHdDvhA5nku8+DVVN1orfSNkOFanAoYGdogCPv79M8NqnOX+k60jz6zU9YTtAWpamlY7AYj0n2iPboYVeD28aieDuqK51tidxQHEq2T12pRICumCZH6Ykdh8P1h0mhTFDanFKAh1bqMmI9RKsn69OuI4Sah01P/APIDgfdj5lMv8apyNLJNX/HkgjQa6cuLATS2k0LAMJKleQFbQREDP+Jzg4B4S1epqK3r3uVVpZKDErVvUiAAQSowIjMiPtA4sHfOSFsv1OWLhUGibWn/AKTCRviI+b8uO2OgMGOGD/8Agw5b3bLi7gl9aRDv4hwKVGR3jBHSB3gZIMeGeBxzJ5fqfqVqb1SsG4PyVfLjzZoqB+V3aocSkb1BpHpGAQcflIgHPfocw8ac5waPuqVU1+bubS1p3qWSCW0j3HUDr2AiOszwRdXfDns1MhVRpm9KfbC1H8NWEHbJKvSomR1z1EcBzWvhKu+lt7iKMlTBDhOxO76kJOFGc4GZg8Eo30Dhp1EKRDcKWoH4bt/kVKta8g9N68sy67S+oqdwqRuU2AjAEGCjqIG3BIj37cV05haAqNJVLia5hhKmhtbW0Skox85IgwR1IBwOh68O1z0rcrBXOKoK6ooqtJkpU75clOYM4GCQIicfQ8R6/wDNu+W6GrykVrSRG9afKdSJwe4nJ9JkCR9uDVvp5mn8J+tvY80SgDmDBdq/NNNkvlTbKpLK1OVFOo7UKdSQsn5QCoHIzHZUqk5jh8qLrUWSrDKtqadtJBMqScHb6REfmEbYJntg8Qq/UlPdnRcLa4UrGNo2gq9UKCgPoOkdes9iTY9OPcytIU9Q0giqZTsUNm0pxk/5de4yOD4oHykFjc91lRVxxbyHAWV1xa1BQ7XlF1hfyArKnEiDHUAK75/7T+oc11yiYu77pp4plAFf7vKVAkR3Ek+5/wC7qBkzI0nddNIUy+y60pfpncpLaCB0HYwIPQxJmIniLuU7i9UN0ppXKvz1JbbQE/vC5JAAzOen16z34yFtRQy45ei5wzxStOgghAGn0KnT1/aU4xtSh8r8tBUVKGfTMBJnPqkqMGBnJQ50an0rcdA0LdsaaYr9sPhBIJxMRHYfxMdOPnOfQlZpCrqqV5ioaXTK8txDjcKZ9Q3NqThUgnKe3Q9+BLdbgp6nXucgIOwkkbkpIO4be8RmBAHt04f7Pe3+CRjOVpJStk0vzsord2VIqFPtuJ3QAoTIUoGSk5/mT/sr07qFbitqt6vMbgEE/MD1nKSDAHXp3HTj5cHUu0ziVktuIVtQqSpBKcCB1n9ScgCTBEXVVOWe5oKlpSkOgfMQFHbG77pyJHQkyZzw2UlSDhB62EtycIr6Q1YLZem1JcDKCEkp7kFI/ugSnKJ6EkiRmOCfp1tylvSPLLhbfh1pCSCDJIMicHMGe8/Wa82+7KqJKCElhZKJBUlUnaowARPqBkCMiAVHBl5La7/HKp6N0uKbCv3RwpSSchJM9SIkkAZMGccNNtqBE8avgku80v3iBxbzwrV6PvbV/wBKOU6iwytLCijITuISmJA9Uz7TOSDwOuY2mlVda+gstKQClxK3mk+iT6UqOBGZkwRjIOOCHyY1jbeV2ubfeLvYEaktLG9FRa1uqaTVJUgwlSo9MEz2JIP6N1XamrvVF/yy21VSsBJINNOQQTmAD83QwInvbVtnEzNB5L5S4oon085lAQqs1mbqmFNL89kKbMJUSPOhcjd0I7ExORnpPA81rS/h3ENupC8khJVsDW0EDGQPSJSSDO6Jk8Hit0s7ZKlTiUv+dTghSdp83bI9QIgTAGQfbgOc27e0ivW1CilUvoG5K5JIUDk9CYPuSTkDHGt0oGhmcILbLo4vwFGrbzi1ffqTT2mrlfLncbNpBT9DZaCpeK6O3JdXvWhoEApCl7VEKJwOogxGuel6eqb/AL0rdd/EMB1RUSCkJV6kwZASk+kqV6Sn3xxso6cP3be2PxRWlRBUoFUhRMnb8w2ySowITIA7oecjaq6ltT6Ggp9wpbwhMtlSlJBR3C4QM9YJJiSrhRroP+2c0JottTiqa/Khte2irpVJX5a2dqElM7d+B6ogbSUj5YJBSBGd3DlYrspyyuNBxCiztbcB6QqdpCR6UiRsCSCUxPvwnoGdtvC9kU6IbUzKUpabKjuABOdxLgCVGSQEmMyi0+ypj8ZbXE+aUBYTLstrG0gb9sJ3BOAE4ntBI4AWmQxTj1TFf4xNTZ7KYWDStZcNM09d5Ly6MrVvfbXtGDGzeRnO7qI3LQCe3EE5huqYfq0haULAXUSjchtxYdG9Se6lbgBCsAgYJk8F7SHPC9v8qkaLFUtdgYfVXtstUyVueYQEKSFTJG3adgmCMRuIAe18FJuKytSQAtL+9I3tLBUJ6AEpEzMKACjkDPDNcz+GCk+2sax/lKrFTVlOhp0qQlJCk7CoblQD7jvABxAyeuON7TKfJTvB3gBLbaiZiMSIGM4IyTHYicoFOvtNhanZdHmK2K/6ihO1RyQQFZ7QBjtx5UtKqTO8qSkgqCZK4MEAE9DPbrkx24Q01LYw1D+9CMwQpMlW9YPUgdZgYPcEwRgbPL3PoTKVbCUQBuUYlKQkjKhETByZ9p4etO8s7zqpFU3bqCrrSwFug0zS3g37rO0EDoO4PrxiZRLtjjdQ4y8ytuoXLat52ISEpASSCCTEEnqPl9+M6ZWNGTgL1aLYbnc0rSUiIdU4FApKSpUjGAFZj6jh3qbs20W6JshpCZUtUgKECSonPsD3mYGeE7zabJRfhmStDygC4fUpRJwN2IBzMH6HhXZ7Om2ufvQH6lR9LIIKWfYqgxu6HqYEj6ccJJA0ZKOUFI97vDh5nmegC3WWylavxNQFBLUBpgblbSBAEd1D6+0mY4mmndOrqgpRKUBhPmKPzJSeoMifUTPyz1kkCSNOjNMv6hvLTbSPxNWrLaAAW2+pEzCcA9/aCPabasqaa0WxVpt7rK0tyurqVE+Wrv5fuZ6FO0Se3EOGF1Qdb9mhOIkhtLGxRNzI75n39lFLjeFv0aqOlQtKHZU6tO390MEweyhPQTAjoRxopLcG07oStQIC90QgY6npP8z6unb2mobcaDoLjVI2r0lXzPqJyEgYPRWAPYdhC+22Z+4pQt1PoaSEIaWZDY6iYwVZnaPvMY4G19XgaWnACarFaHSSCeYZkd07LwilIeQGwHql4QiJlKTIxPRPaSBI7Ykuti0w5WutoUdwyjYlO7cRnalIkwcRI+vXh50zo5dY/wCTTbXHVr/e1a8t9wSo9FGTO0SYBJPQcEPS+lGrS6KWhaU9cFK8pVQ4klW4ylKUIwdpBEjGAeuQVuerJOlnNWJFDHTN1zHkrCfCQ5G1VXzsqtRrpkooLBbXGg44lJWqoqP3aSDMpISHQR1ycAHjpnarOEg7khPeQOn04EXgV8Ob/Inkyigr2FNXq5VK6+4blpW4hRAS22Sk7ZSgR6cAqP1HFiLRYiEhJwD2mAeNmNIHm3JVN8R3JtbWulZ7I2HwSe12fahJKOp9pPbh6o7XPtB6kHH24X0VnWHUHaVJUIWem3Aj9OHyjsmAIPtPsZ43QBNVNZsSEnd14X01oGzKCrufc/bh6pLNGdu1J9skD/fhfT2ny0+rbuH5gM/bjMLEx01qAPqTuVMRH9R/nwqZtGPUkEHJxH6jh5RbfLB9KQUwnrgfT+vfj2UNszJmDMDH8eMwvQSE1NWUAY+UCNyhJ+/+XCbUWiWb5S+WrzGHGzKXBJg/XOR/pw8O3JpslMoBPtmT9fp/twjf1K0ykAqk46dP14j1VDDUxmGZuWldYp3RuD2bEIcVPKO5W+rZbUadxl9YZ81CVHygZyqTIEex7H68aGLVdLa/tYZtgdRM+acNESDIOMmMA5mDnic1+s0JTPmJzgZgGff+fEXu98becWpLjhSvO0qhIECSB9Y7+54r+5/ZzTPYPuR0nPU9Ecj4inO024SKm1fVWupDV1slMUq6lASkgDuPv7Z6DPCq80Nh17av3SQlU7ipSANqiOhPXqTE/bhurX6a+uop3X/KdUqELB9Kcd0k46dR79OMqLDVWujU9DbK0NepaFSFdfcQR9T3PCweFX0s7WSt1DIyMcx70RZVxysL4zocEJObXhlobzQVVW9+EPlDehxtuXAOmd35R1JkwenFU+dfh6XQpqA20mDJDhBVvSRI9pge5M/YkcXpqtWIcp0h4pWogpUlYwqev/jvPAl510tNX2lPkJSUtKUElSjuyJiepGCO+McM164ZFBH97oHaWtGS3JPVGOFuL53ztpKnzajgHbZc3m7G7pTUS2Er2tOShSFwCgSI3ATOREHBj26dPvg6eF7T/NwXa53lH4iltqENfhZKfMUSYK/qAIOfpxzz5y0zNo1GZTuAWCFDASROP4Ht9cmDwW/CF459ReF+5/jLPWIZUtIbdbUmWnkgHqmOgjB4sj7PbnT6tc7g1zm4BPLJ/fNT/tMtNbW20x0IycgkZwSOoC6EfE88J2luX3Ldq/WWkaoCt3ynKdBCQqRO4E9Iif045WajokVd2Q5uSpxhYV5qfSUbT79ZyDMwZ7cHLxQfE61D4o6JukudTToaaSfLZYSUNpB+YxPviZn+PAA0ddTqCufdKpCkLV64hPpzIj3UTiMDvxt9pNTTSNi8Fwe5o3I/fRCPsvtFwoqV4rgW5OzSc4Czm5aF3a2qfecXWVTgBUtwlxxSpmCo5k9ZPYzJOeK9Uuo3uXuuF19PTUF0NMle1qsY81h0KQpG4oJHQq3BROFBJz04srrghmzxKj5be2EJ3zBBPp6YKo69x0xxWzXxpKHUyTUBLrReStxIO3zUbo7fm7CZzu6Dql8NSuLi1WtU4EOB+9lCLgVU6vLhIKkBxa8wTM9QMKIjp/GOGa6oNUNu5SS4pIKtolKicECe5x365yBwbfHDq3k7rDXNre5M2i+2WyptbCK9m6u+Y5+JKR5hEHI3dwYOCAO4PdqYRJGUmVbxEZkkpBAn9IA6GZ4sqNnhP05z6hB4qptXTCXSW56HYhb6B8voS5Li1oICtwUsDMAlU5PToR8oH04KPh/19RaG13YbtcaJu5UFtr2aurplHaKtttY3NqIwCpJAxOf0gXMsgKbKEq9BUjaQTuTGD9ZEziADM9+JHYqlaaQtwYhS29ypJXAIIVOSAQcAnJPcnhs/oY7slQNDnSRu5HIXTPxteNjQHiy1BYn+XukE6XYoraGqtK2EtB9W+EyECCEgQFfMoEiRBHEU5btoqKZhDikHcvcUKG0IEe/QSOncA56cVR5MVqG0KCgnaluAkKEqWVkAEnE5EiO4zMcWF0FeF3KvoWtzbKlwy4t9zchIWQNxJykDeM9hntxZPD8oY0Hovn3jG2sjaYGDOEQNZaYoFUS3adbXmNnaUncUKIJlW4ZTCswIzHeYrxzQs34itrCkLJaG3cpuVMkkEuJgYMJA9UAZxIgnTmO4rSGqq2yuXC23BVqql0ZqKJwO0r6knbuZUSNwMDMEEH3jgQ69T+KQXkrBcdJSC1KScfKMY6mSTmcgEGG6of4se6p9tP4T9TRhBFTBTqBC1eUvzqZIUPMQGx6oJABz6oxgkznuGXmna/J0u2ypa1qZeSoBaZDoKx8/QKSVd4zHcjiZavtLluudM9ChsWfVtlSkhUeoqGcz9fUPc8R3msy28mqpiA0j8OVFJaC9hJneAkScSCnpCRnpwq3CICN3uRy3yuMzUM7NrOsprFX2OmXTppLlUIdd8xlKn0rbUvYApMrSCFOqUEgg7M5EqakuGkujDqW1BKx5ZQkhQgk7ASpJBG0picSJ2yCR9tdsXdb8pLVMmoQMKbV+8RnqCtO0AFW4kekkJQP7o4U6r04u3VC6d9sML8z9wXlJCnE4grBAURjd06LPsJR4AfEBHRPdQ/MBDuqk3K15VZVOqJQYpyqdpUCpJncNvqEFO8FPsme4EP5oUrpRlxSUwv1bZTuKYKUmJgkbQJkYAJztleiypujefd3Nmo2OJL5AUAspJJJkJ/MqAegBMQoFi5tUaW6ZcRvDjbiWwE+YiVbSBuABIABgEYyVCPSzXEk02eqT7cP+5x0Q/wDCRc+TlqqNdL51WvWd2Yq9LVg0sqw1KWnGr6VfuVPk5LEk7jBAIgggxwIm0JStKQ6HHDElJI3wcmOkE9ExPXHt4pGfNAUVO1BVErGSAPaYxOJ7AfWOPaSHUqjylJIDRO4GCdwhAyOpMdsBXbhFTdtldwP7NL4zfDXyA5A6ysvMx/TmntW1D5d/GXlpDorqJKD+4QVAkbSVenEknA45q/EU5iaH1j4steXnlvTCk0tc7s87aW07g21TqWIUP7oKhIG3rEiegFtb/wCz6QPwAkpKPQjcFwMBJgSdsZHY/XhVb6J+uq9xQj8VA2pmQ0ACIA7kgmZ+pnEnHTt0aAOamUduc6TxM7L5R21VCoJQQ5WvKIBM+gnBjtu6/bb1I4lmldIPO1LNNTIU/WOZMK9LckE+qYn1CP8A3g9p4lHhh5Jo5085NO6QbrWqAX6uYonbi8T/AMsh1zaDu7DIJ6Hp1GDdL4qnw3NP/Cgv+n7TQa3TqZvUdtU+oMtoYq2XMp2hKVGEKPqCjmB0jIhtp/FOqQ4aMpvirYqHEMAzK7lty9feqljyOXGnnaVh2jqLpVg/jNyVfu0zBnoAnEEyZJIBHUw1brd1YLrhdXaGVKKSoQqoc6yR0AMRGYn78eq6rcuzqqioJYpVOeYy2JPmECJUokFR6dDGAB78OVqoA7TpuVcCmlQQmnp9sl1XVIgROOmJmO0QOuNyBAiiGGjZN/D/AA3JI81dWcuO+/T1W60afS8Px9yA2iW2WSYmDIKj7kwYEYBmOnE50foKp1c6l1bSk0jJO6USWxmFQr0TAPzSAYJPXj5y55cuaxuLVZWNvuU6BtQlAIQ2Py7lJ6mZyCAJV3Bggts/8TPMWqjATQtqASWk7V1piQkZMyNkn5cQO3ClXveDh53/ACHr6qxaOohYwiDlyz1PuWmio6VmgX+FCmbeB5DjyJK3HMjbuImAUjEQB1meLNfDk5WW658+bLW6hpA008l5FjoXmwgKfSjch5QOSdqCE7hJKPVAA4FY0zTaOZpkP0yKqudBZprayApAUSUpISZKldD0mAR7AWO5Acu2uVmv7LqXW1U8m+PVbTlPR07almkZ3hUwSJUBggdAoz1HC7PeGRFug4BI3PN2/QdvVcLhRPqKZ7XnzEHAHTbquglh08BtEbwYJIPzH34lFnsgcCUlIAHRUY74njfa6ZpDMgpWhQ3gjOCJBB+3fhzZrG2x+UqAiOGgbqjiO6U26zgQqNnYiZBH24cqenQygEmI6T0HDI/qpmmAIXvP/uyof1/hw21uukMNEeYj0Yge/wB/6zxsGrFMl1rVMgA4KACT3BxwlqdQs0424BTJIkZj3+nA6uXMQSRvG7JP6fbpxH7rzLAblLkkdMwD7/fjbGy11BFSt1q2klJXCTOUn6Rww3TmClswFlM9IGR3x7cCm6cygSuHCYGADk8Ru68zIKti4MzGPSOPCcLM55ItXDmL5atwWUp+UAGD/H24Yq/mOHBPmA/mJT+b2j6ffgQ1/MlInIUZnr/HiO3HmSQcukFQ9R9+PC5ejKMVfzKO6AuZO4g4A4ZbjzKkQXJV+YDgN3DmXBIDoz1B6n78R2480S0iA6TOJmT9emMY/nx5qK20o0V3M8oyHSPLGNqsgz7/AOfDTWc4Kl2nW27WVa2Vq3qQXVbSZmSOhPTMcAu7c0UAD1hHvKv4frxHLnzZTlIe94JPQ8czg9FuAcKwFbzX3A7lwlWIBwQe324i+q+Ya7mwGwv0pWmQFQIg9RBgAdzwCrpziDRVLonvnpA4R6d51UlTqdlqqfbabeKpcUjclsgE5EgdoiczwMvEb5aKWNnMgorYyyOvie/kCFA+f+o/xetLgguBpDS0gDBCIAkAzJjrt9+8TxDrfXf/AMrfddDzoCF7wk5ThXpCoIIyD/8AEe08JdWX46j1VUuugpNWohSFSC2gkTMdgIBxmD9YsH4W/A9W84qe01l/eRbdN17pcrksr/5x1tJhICFAhG+CAsGRJVA7rtupn+EyJg3GPkvoC5XCmoofGqXAN/Pbsq5aPrXHK0r8xSWgtTiiFbYgekkdJgTI/NODI4udyA+HdqJXL+qumonTZrnXoSu3W9YCvKSU7pfj5d0gQCSnM+xMvgz+FJZOXfP2t1Vqa5UV5s9rq1uabtqEbm1BRKkLqdwJJaCglKdytxTuJIwboam5et1ZUpptIKpJAIgmev8AHM8HxahLl1R12x+v+FW3EPHLQWw2s9iXfoP1+S5H8++TmotCWB165Wmop6Z0bfNCQplsD0kqIkA/+6CQZEDil3Oa4JTXKlwJW0VIWXFzt3FWM9cdCZkfQjjvvrDlukIfCmvMS8ktuoWJSpJEEK9wQYg+/Ag1jyH0/darzKzTtiqXUjalT9uYWQANoTJT/dgZ7Ace0FpbSyao3bLWm+0V2jTUxZPcFcK3f3zClIVsWleEEgqQYwY9zCgJGO0kSM8tPltnqlPpUQZCRAJI6jB7icn9OOv/ADD8EHLLWtjYoKzRFnpqajW44yLcg0LiFOABZC2ikmYSYVuTKQY4p1z9+FjqXRYqa3RVX/xNbkK8xNA6Es3JtO6QADDTxT2IKVHPpnq1wVDC8ZUui4tpKhpY7yn1/wAqp6QWal1B6lO4zCkwPmM5nM5yMdJjiWaaoQ4umaKvINSQ2pxS52ElICSVCQUx1kHOeuX7T/hD5i6vr61NDo69KNqaCqoP0qqZxSCraEth2A6tIAhKZhMmOh4M3Kv4c2vNW0VM9dlW7TlFUUxdT5xLjrUqMNKaTDiZGREp2kdd0BnbWwNb5nBQZq6njcXOePmmHUGiWOUl9atlNd6W9qShDqKilSspO8boAVkEAqBBHYjr6uCHoh9NZWI8qQS6BAcDhSmSkkGe5AIKpGI6RxG+YnhM1ByGrqBVxdpbpQVbyWU1VGlS0DbkJO4ApcOfpB+b0xxOOVGl6m/31mhpWX6urqH0tJZQkKO4kbAAT6icR1I2nAkRYFkqmSRCWM5Cpnid7Hzuwc+qcq9tFE3BISCQZQobVT0GSACFZOcAYzJ4bNI64e0fqNi5Io7TXPMsuodZuVMKinUpaSlRWhUDcATGZB2HtHC/nZp658ubm9abxRrt1wpv3JaqU7VsL9jMZJj3gexOYRaroF3Bjc4rZuUNpWpIGDiBgJSnJhI+c4JiGiOqbjDlW9VQmQ+VRm+UDd0uzaAHk07yghS/LTC4BBB3TJIKesZJyR0IPxJvB1pnw0aG5d3yxa9tGsHdc2n8VVUtIlC3KRQQmBhZKkkwgSQoqQYBIgibmdranNUVoRT0qA15KlsoAyBCjO4blZ3HYo/QADgX6quTl18tx0+agoSCWxvwT2UNoyPTGe8TuEBLnUh+Qw+9dqG2OifmRK/B5zNsfKXxD6Z1DqKzU2pbHZa1D9ZROpJTUttuJVtJJ2gKHqSI2jYDCSqOCp8VbxJ6K8W3ifOpuWumEaUtNYyhkUaUNMuPuAet0pRKQs9gAZ3JjG6a62qndXWMtrThpsLUAhKUnopslMDaQnYB77TAyTw5Xy3rbQumUlSJ2LSNiWiQoBWBGUmRMn0hJjHAOmo99fVEa2vAaYwt2lFt0mkF1LBStO3YVhG4LUASZ943ScnB6CAQ0cwqn9pW0Ogne0o7lKWgpKQQoKBUTnclRAiMx7Hh/q6lyj02/TJUmoUQh9Xlo3F1UJIicEJMAgRHpwJxEr1VecHada0OI3hKCkH0HcFg+o9CSZHUwIBgQTuB8mhBrYTrLvVCDwy8qRzx57aT0gmp05SPakubVtRU6iq101sp1uApC6l1KklCEiFkg9QgZynhBrHQL+jtfXTTlRV225P2G5P252poXRVUdWth0tqcZXAStCvL3BQGU5AzxHqFhNQWoStYIlYCU7ikqIxJIMk5k9+sdZWxaV26lRTIbK3p2LW0ncBIgNpAMZjMZEqE54QnOxsnikpTI4k8hzSTy0VVwQtxSvyobKAVKSndGBnMwCYPQjMxxJLbZF0G2mKUrqlkOKCQJQIGD/eURB+g+5HGyhtzeicpDC7wv3TvRSAgiT7rBkBJ9QKiemOPbi1Wrc0G1vVbhKlNu5Wsk+olQ64weg9RwTBGaA12l/xRmGQ+GXxjPb3pwtd8esqHWqJ9DRbAUt5vKnFAkglX5SDJET2EgwOHXVOsLrzDu37Qv9yr7s82A2hVVVLddWkKwNyp2gGY7YEATwzW22IbaQ4vaURCCIBcPuB9+8CMf3Y4frfbFXdalur2UtIkF9aUghlBAwEnBUc4OTBzngDcbmXHRHs3srN4W4W0N+8VG7z1PRebRbP2m+9WVm1umZKSRlIWegQBkqBJiPYd+045e6LqNe3lNZV/8tQ0w2NhY3EjBKQB1UEiCQJxiIPHzRehXdZ1qCGzTWplSkq3H1AnBKjgKWcjMyIGAMEporQE223g/IlJT6SKdG7KSRjE9IVmQcmeBbneENR9s8h/aO/vTdUBjhoG0Y54/qPb3L5UIXeG0WKzMeUVIC33lkJab9KQoqVlO1IJAECcR19RH0za2dB21hFG29c73cEhtASDvcKj1QPb1HoBkkz0JbND6c/CoU2zTj8Oh0B5CTCalzqSEkxt6nbugbv04PnLnSFJywZbvVyKXtQV8ONKUd5pkqP5e5WTOMCDknEVzxDeAD4Y3H1cfX0Ryhoi1okeNzyHQD/KU8teWLXI62q1BfG2LrrevSttLIBVT0G8j0JzG6CJUM5EYBHBG0jp1vSFai93tSrtqevUfwlEDu/DpUJEJwJKTG6OxIB4jCLidL1dJdq6ldrb1W+m2W/CnN68+c4kYk/KlPQbQcHIsV4e+RdVohoao1KTcNZV7fmNB8/urWkZhUzJGM5iYnpFd3C4Oa0zTO39Po0dh3Umrljpo8dT8z/gImaF19d9PWa20Wpall25vspJpmGjNIMBPmEfQZUY94GeHWt5iJLJAc75z8vvxBmUi+XGvFH5tbUxNZW7dyFO4hpA95APfA4gmpNcVNlr6imqULp6mmVsW2sFKmlDtnP+szxYnAt5lraVzKh2XtP0KpfiihZBUB0YwHD6+5Fe6czNhUfMAKjhXbhgunM2ZG8qMdjmP9OA9c+ZYQpUOR16nB/r+fEbu3MsBRhyYn6En/GeHjUloNRiuXMpIWoeZ8vUkz3mD/XfhgufM9Klql3AGM9OAzd+ZgEpLpO1Mnt9hxG7rzTk5cA7ACI/n/h9+PCSVsGoy3fmXuUR5pyeyun+3EbufNBJlKVwc9e/1/r24C135qDP73A9zG7iMXbmyEKMPfm98njXJW4wEb7nzRTuUC5GCJ3e+Y4jl25pEqJ82Tu6Dof9+AXeOcCgVISpZHTr8vv9s8R6v5jVFS56Cev5fbj3TlYjfdebkgbXEhIAyVATPcff+fEZufOAbClLwIIIJBIBA/8AHAgqNQ1dcQVLM5k9Y4TP1Li1AqcUVRPWT9+PNgvBlT6782VOqJBBj3P+H68R+s5gVVSowopHsDwO9T83tNaLB/ad6t1KtP5FvAr/AEQmVH+HA21Z46NL2hKhbWLjdXRO0hsMNz/7lZ//AOTxLjpJ5PYYfyXGWphj9t2EfXbzUVGd8Jmcnpxperyj1qego9U7un1+nFONW+OvVN2KkW2lt9pbVMKCS+4AemVemfsngZaq5tak1stRut6uVaFGdi3yGx/8BCf5cEIrFM7+Y4D6qC++QsOYwSfkrzau8TGkNAVK3q66Ww1jagpLVM4F7VT1LaAScY6iI4tr4B/iG6c5uW8N22vdQ/QQy5RPDY8wjokweqY6EdfocccRfN+mPpjh75ecyrxyq1dSXyw1rtvuNEqW3EHBHdKh0Uk9wcHiSOG6ZjD4ezu68qeLq6q0sqXamN5Dsv1U8pec6K1ppYdSUrgJIOQP9f8AXg+aP1/T3elQhawo/UiQZ78cVPh1/EdoeeFibpKp9FDfqNKRWUSl+odB5jZ6qQT+qSYPYnofyr5zpqWEFT0wAJBiZwPsc8ApInxvMb+anxSNe0PZuCra3axMXyiJSgCQZEd+BnrblztBIROSRKf14eNBczBVttodckqiBuwocTsfhtR04Uk7gR6o+ZJ9p45kLpnKrJqPSCmFKPQ5MniG3WzK3GYUPzAjPFndYct/OCihG5MzuTkA/X24EurNFLZWoLbUDJAO3BPXr0/XjxYDhCh6kDh2uAKKDIBlUfXjyEhsHAg9VJPX+PEjutiUytRIIQT3yOGh+gIWRkkGOvHh5L3UVHtWaao9ZWCsttc027T1jfluJUncB/dP0KSAoHrIGeK4WJu9cg+YjNU0tygudidRVU7qCc7SSh5JjbBgnInqO2LUOUaisEH1I+v8eIjzR5QN8wWmXGahdFXUra2UOKTuQpCh6kke05kZx9Tw1cK30UMpinP4bufoe6FXWjM8eWcwqv8AiH5u3vnTra4X/UNZU1VxuLpXUVBGxK1fQfYJAA7HvHAp1NWuWcKU82thflealKyqDuSPLKSRIHQgEmB7dOLC33wWasLo/BO2mqAdCgsVXkqg7QrCkSR83pOACYzHEc5teAbmZqu4MG31FqvC30pZU6agtFiAYSd4yhJ6lMkykBMdLE/6hoD5WSj5oLS26Rpy5pVO9T31166uOpccSuSrcU+WnccBSiQk9QIBk/LIiRwhuDQfp1KVuDlTThBdWkbtsiVKGTkEScfl9I2pBkPNvk5qLk1rlqx6otT9qrqZfn7CWy24kEypLgO1UncAQdoKyJgEcNlLalXCkpkt7nz5u11TRJ2pEQRPqAmT+X1AiMAHpGfEcC3qo9czQHPK2aJ09Uag1GiipqdTrzqQgMMoIcWrcQ2Bt9JKZV8v94GRJPEq52+GzWPhv5k/8OaysVy09eQwKwU9U2lCi0oApdTt9M9iRGAUmTPHzQz7+j3xdaN9dJU09QlVK62opQy4FBYcSsqJEEKVOOoz1HEm8RniQ1p4qeYp1Pr2/PXy/IpW6Np4oQwtplsEJTsSPTElRMdTJMnhhZRuaAen1VZ1NyDpHAHl8kGdZNoZrVUqQ4oObUmYM/MRuQrocTPWZInI4hqqs012YQ6vc6+35rifUVKKSRKt3zCfSVH1SpXsCJhqZpa744sF5DVOr0tp9CFFAJz6oCQYmegiAJPEDrHCmspWqfzUlKVNKXISp4BwLK4BJCyFbSYAA9oJ4E3Ebo3bHYGShbo22olyreKGkMALbWpRgLztJPXckBI7THeMvdNqZKVBdvpmg6oeTTJ2hRkjK4OCB3EGPV3yInT1vn0qWFKCmm0CEAbZnMT0JJg9CEj7kcSLS9sS1bl3CuBCduJV6g2Mwke0nrAk/SeEQS+EfF69FY1LG6pApI9m/wBR936JxbUiy0yqyrK3n1q3NpIILkACVTn9DJHtiONlronCnz6kO71KBKE42qxjHScnBic9eEdmp3dSXVdbUpdRsJCEqSAEiMJiYyD3yJPtmTW63JuDwdP7tpOGxu/KB2/nmO/TgNX1ZY0s6nmrH4ZsjZ5Wz6fw2+z6+vuWy1UDtyV6ihTigAoEEop0bpyegGfc/wA+Jxo3SKtWVrbDQUi20n752pW2f3qpy6uMAxMdkyJyZ4aNMWZd6UostJTTIBQgGf3yyrbHfcB6p7D29zVpfTrWj7KikbR5laSXXNhkbynseoiY9I2mDJzgCMhwIGXdB6qz3PDYy0HA6n0Xtq2ilZbpqVLbNPThLanEtlJUqCIUAJI3EAyTgJ+3Ex0By5RV0qnlOfh6AkIcrIj8QSdwQmI7EkCY6+8HXy20Ku9VTVbUKShDMhKwCC6CQPlBgmdo6zG0iOnBTp6Og0/p9N9uzYTZaKW7fbFZVWvzEEd07gmc+vIzkkRxTUttsQp2u1Tv3d6egUS0H+ISmfGIWbN/8j3W+x0VHpCgpLhV03VR/ZNpAn8UNwhxYidogRIyT3EcT3RlrdpmHr7dAt2r8yGElshDrwAGxAzO3pOc7usSGLlboe5axv69Q6ggVq4TT0illYokdBt6BO1IP93omJji4Phv8P8ATuXOkvdypkKbpcW+lcGGkjq+tMCD12g+844pS73BkOznZPU/oEzVNaymjL3fBJPC74T3bdXI1bqxttV9qpfaYcAUKBs53k9A4R06RtEQBxMNUahq+ceq3dOaUU5S2mnATcrnBJZSMeWgz85yBOJz0E8PGt7xcOZWohpGwOFCFkCsq5grE+qVdgPt798cHLkjygtHLXSrPkUyTTJMoxC7i5OXCTnbPuen24X6SGa4zAuGw5dgO5SHc7x92BqajeR3Idv30XzkhyCoNH2mmdq6UIYalVPTrkpB+bzFyAVLMSZz1gcU8+KPZL1y45xp1PUs25On9Uw1bnabclRcYaQHEvBRy5kHemEFMDBSZ6Sab0i9eXw9WFYClSlsdB9Z9uvFQ/j48htRaj8KdHrG03amp7Jy2dXcbjavw3qqQ9tY/EB7djykqjy9hBDiiTgRffC9kdBTfhsw3n6uPdVHW3l09VqmdnPyC5v3bmkk7j5xhOABn3/r+PEUvnNpFPMOyYIVB4CNZr2srCoblBMxkxP1P14QVF6ef9TjuO/qzwc0jqpek9EUbvzgASUhwkgbQkdv6HfiNV/NR95UIUY+mY4HOoNb2rTCFLuNypKJI6l99KP8TPA+1L4yNHae3JpqipubyZ9NM0Sk/wDyVA/UTxKjppH+w0lcZJo2e24BG2p1RV1i43FPvJMxwkcqnHlfvHCT7SeKsaq8d9zqkrRZ7TS0oPyuVThdUP8A4pgfzPA21R4idY6tCk1N9rG2j/6VMfw6B+iI/wAeCEVlqH7vICgSXmBh8mT9FdjVHMKx6QY33S6UFEEiQH3gkx9E9T+g4GuqfG3pCxBSKH8deHRIHkMltE/+5cY+oB4p86+updUtalLWsyVKMkn9eNfBCKxQN9skofLe5XfywAj1qrx5aguZUi1W2gtrZxveJfcj+SZ/TgZaq546s1nKbhfrg62oyW23PKb/APxRA4ijaCvABJ49qYWlG4pMTBPafb78E4qOGMeRoCGy1k0ntuK8KUoGZyf58YVk8eeM4kKMs6njOM4zjFizjOM4zjFiedEa5uvLvVFJeLNWvUFxoVhbLzRgg9wR3B6EHBHHV/4eXxJaPnLa2rbc3m7fqWibAqaUq9DyRA81qeqD3SZKZ9oPHIfhx0pqmv0VfqW6WurfobhROB1h9lW1bah7f5g4IwccQq6hZUtwdiORU2jrXwOyOXVfp65S8526ttrY+mMTKsH79/6+nFhtA800uoRCxumFJUr5uOIXw9fiPU3NOnp7ReHkW/UtKkF1tJ2t1qRguNz2/vJ6pPuOnRzlZznbraZB84EqgKz1+p4T5oHxPLJBumuGZkrQ9nJXvteoWL1Spykz1B7CPb/Xhq1XoZq4NqcbQMzkD+EjgP8AL/mmQ2g+YrrGff24MOk9bsXWmShUKQrBzn9fpxwXbIQm1foRTC3ISUkHBHUE8D672PyVEEf9u0D+fDl8Rb4oPI/wE25VJrS+PV+sqiiRXUGl7Syai5VbaypKFlRhphslKvU6selMpSvE1C0x8f7w4a/09T1dzqtY6Pr3nfLet9dZF1hYn/1A7TlSVt/UAKnG09eO4pJnN1hpwuJlYDglWSdt5QegmZg9DH+HHluiWophJO8lIKcgkCY+vAkq/ifeHMWqmrjzf0iaety0nc8X05SP3jQb8xvKhhaRjceiSRzh+Jv8Q++K8Ulzc5P8waWm0vcLHRUj9y0xWONmuO3zFeeqdpfbXKUrSlKkoITuOeJFLbpJn6CNPvXKarZGMnddRuaXiZ5b8i1uNav1zpewVLSQpVJVVqfxJJyIZTuXJxAKe89OKbePT4o1Pqe02/S3JzV9tqqG60y/2vcqJuop66nUFjay26oJCAQCSpMrEFKgAQTzR5mc99Wc+dcHUGtb5cdS3lFIih/GVqwX/IbJ2NqKQmdu45OeHDRF0Cq/aFB3bsSUqcVlIUI6RAnBJIwTmeGu2WSGF7ZXnUR06IZLXvkGjp9UaW65bbzzu1dQtSyFvq2lSilMD5pBIxnd2B+XiZaXuy75Y7bSPpo1C3o8qnX5Y3uhR3FRIAKiAO5kSIAJ4gNElnyWVK2ubiA4REkAFRIkH2EqPpP0IJBd5V2hSKNLroceQSSpKnN4JHdJ+5Ewe6vpxY1kpHVFS0AJY4uucdFbnuOxIXrVTZtdC3RqS2hVOgnalzbtUoiQQBkZSMAjMd54YCHGA0FBLTq3Q4lSkOSDOCQD6gNpT7dO44MWoeVlqreR1z1k7rC2s36huzdub06pCjXvtKbKzUAiB5aYIxMkdpHAZ1g6mgWWQnbLinwlKktrR6VE5kbRBAJMn7QRw41jAwEDoqPtlUZsEnc7qFajeP4pp3yAuVKQhIbUPMBP1Akwr0qSmJ6AKTwWPBL8MrXfjw03rzUWjXLamm5f0y6q4LrKgNO1aygrLTeCg7kIiTjbuGJA4Cd+d865BKC8EpRLxcSQdqSVQcf5SJMjG7ibae1rzP8AClQVNtbuOsNAp1na2xU0bRfpDd7e4CtJWgx5jS0KUd3qBAIEEjhMqgXZ0qxKN2ho1KpunrSq6ONo89akFXmLlzATtKSrJ9p+sRkYHE3To2v1Sll1mnU1bET5bykFCFFJkkgCJkSAD39gIa9B0KHrmzSHcpt1SFVCkyRsiI+ij8xTJkdRjjuD4uPE14Rqf4Slp01pi22FOtBQMNUlK3R+XcG6tIAeddWBuKSQT1M4jiunu1Zd25K4qKmfH4cQYXB5GrHbt/lcYWKBtyq/C0+9NG0opJkAknEqPTuenYH24dWqRd2rmLZQFQStQDm1JCW0/UdTjr1I/QEo36v8OyG2m0rqXVny0jJJMnriBmO3Un34KPJvQ/8Awzbxc6gbal1MILkAKJBVM9CIAO0g469RwsBwc8vedh9VekUJhhbFGMHoOwUo0bp1nSjDSadhwO04Hko2jcSTt3EQQe6QCZBEjIjggWLSB8xDLhJfWdzy20hwpJAwoKMJz0GSYg44lWhuTVjHIp3WVVrKz0+omLg3R0+k32XPxj7C0bjWwcEA9J3GQDidpeuXFjpRVGsqUF5KvUy22lRcdA6NCSSZ3ZPUFQPchPGquDbZA6vm3cR5R2+ChSA3CX+H0xw1pw49z7+3qn3TllpdI6dp664FSWlAOUrGE+bAMqUQMJAVBMD2wZKlnL/S9RzHun/Ft/ZcRaaE/h7RT7NocWDG4IP/AKaMZEGSR2PHmzaPrOb2u1UFQshpEP3RxoAt0bKQFBiB3JwexyT24sLy25XjUmrLa0mnbp6ChQkUzCU7UstDCdwEAHrOZOcHHFEXi8uLnTyuzI/6D09eiePDjpohAwYa0bqYcjOUKaekar7iyt2rq2yppsqny2zn1dp/3nvwbNYXpzSWlGaalEXO4thDSU4KEYBP6kgD2+/D9ozlY5abIKmoYXTnKziFuLHfb/c6R9B9ojFfXP6c1c/d6os1ToE0oV/06YwQFlJEkjAAnuTiBwtxcJXetc2pMZDTyH6kJDuHEFI6U63+z+wER+RPJ+n0haFNVe1bxSl25uiZUSJDcD3JMx/HtwfNHWRVa+1WPtoCl+mkZJiBHUj7fy4qfcfGnZtJadZYbstyqKiiQCKdxxJRUvH/AKjq3BkD9CfpngHc2/ic81ajUDlXZrtT6YpkwGaejpWni1tAMFx1ClLBUCT7iE9Bm1OHeGm0mBONhjPcn/A6eqrK81U9a8vB3P7+v5Lq4/W0OkbHVV1fVU1BRUTSqirq6l1LbTLaRKlrUogJSBJkkADilHxzPH/y+5A/Dq5gW9OtbSdS6303U01lpaBCLm5WtOgJcVtQSltpxtS2w+ogJU4FJ3FMcc7vGZ8Qfmd4m6GqtF91C9TadqXQ89Z6BxTNG6sNtoJUmdykEo8wNqJSFOLIHtzt8bl0a07yYujLDbLari41TqCEBO4qWFEmBk+ji2oK6Jz2wxM2OyWHWt7IzNI7cdEGdR+OO5uBSLXaqenR+VdQ4Vq/gmB/M8QHUniT1lqcKS7eaimbV+SlhkD9R6v58QdR3GOPHByOigj3a0IZLXTye08rdV171c6XHnHHnD1UtRUT+pzxq3ZmOPhEcZxJUUlZxk8ZxnGLxfQQONjVKXlhKZJUYECST7cagJPHZz4I3wsbDyT5UWfxHc3LXSXC83ZtVbobT1wbCmaVlEkXSoQrBmCWkqEbfWe3Au83iltdG+urHYY35k9AO5PRTbdb566obTUwy537z7ghL8Pj4BtbeNJ23mJz5aqrLZbghFRadHpdVT3K6IMbXKogb6dkzOxMOKH90Z4rV8XW02LRvitqtMaXt1qs9gsVI01T0VtphT0rS1ISXClIJzuBSVH1EpMyZ47H665jXTxH+bd6y8V9i0tctzlO62tSLtqJrcP3yFDNLRKE7FyHHhJSUIhSuN/xebFbLP4uLkLIltNqDLbCEsiGmXEIR5jQ+qFEgiSZJnM8VpwbxhXXi/SxVPka1hIjA9nce07+8joNhy5qwOJOFILZZY5oxqcXbvPXY7NH9oPXqqp8ZxnGcW+qyWcZxnGcYsWcZxnGATxixZxk44wiOM4xYlljvtXpy6U9dQ1D1JWUiw6y80spW2oGQQe3HSz4evxI0a9cpNN6lfao9StJCWXD6WbikdwOyx1Kf1GJA5j8KKK4P2+safp3nWX2FhxtxtRSttQMhQIyCD0PESso2VDNLufdSqWqfA/U3l1C/SZy05vJqG2jvUptYmMYx29+CXrvxcWfw3cldR671C+8uy6XoF176GSPOqSIShhEwN7iyhA+qp6Djj/8Oz4jatVP0mk9W1TbF7QAilqVkIbuMdvYO+46HqPbg7fFI8TGmWPBHqHSVfdGVah1aKQW+gSSXnA1VNrU8R0DadhEk5JAE8KYo3sqBDIP/SaYqlkkRewrmt4pfEVffFr4hNYcxtS+U1edX3JddUMsrKmqVJAS2wgqzsbbSlA+ieIK2ol0ykBWAPpn/XjR8zipIJVPT7cex6d4/vGT7DPDYxuNguJIxuvlRVKOQopx3PT2H06ceaZYqKxIdK1JSADBlQjqMnHXjRUrB64SBkkD7f0ffjdbWytSRGSk4E9D/l/vxOhbyQioky7ATzUqp0OOKpgtLSjDYdA3SYGf6H+PC7SN8NBeWnVha0AgnGCOmf4/1g8NFTKwlKcmR1GTgwB+nSOHLTlnW9VpUlKVqJhAxBJxGccSsnOy4jnlWDf5g2S6XtpVn/HIo0spbT+LcK3VK2gqH0HYAYyOhAg4cvbszVWdsISVQZM/KowQTAPXB9o6STxXHlByouWp7uinoKZ+seA3lDDaitRAlRIGT7nOBnvxbvwScwXfDfzSterU2KyamNrDqRb7u0t2lf3oKPUIEFM+nr06GZ4s/hOGcAuazJPwVMfaTcIngMdJgD4qO3Lcbe8/uJSCooODCcSeuMZkfT78C7W1wcU+8HipSWygJC9pJVtEp3GTjcOnTd2Hq4L3NO8CoQ/VNsMsJdWpQbaTsbBcUVFCB1gAwJJj08Ae9PfjbkNgSA2N8IlJIyQFEnBgDuB1OZJBjiRxZhnVKXBf4gMp5JmaQ0mpXUPhp1taUtpS4Ey4olRXtPQxEBQ+aBGFcPXNnnXqrnS7bKrWF+uWoamzUFPZLe7WuFw01HTgpYYwR6UjeqBGTJkzwvZ0Clnl1VXdd4tIdZr00JtqlAVh2t7vPKNgCmYBRu3E7ohPQiD6keQWaZpBVuacShxRb3BIISEhQI27kkK+xMCBJ4Spm6G5Ksqmk1nZR/RFmOnLSh5/aupcJWpSkx5ajEEd4TPTB9UmO/qvrXLnXNrcUp0mIClk7ROPuSR/I+3G1Sm2mN7gPloAU2k/+pGJA6YEk+3Qe/HqgonHWzUrBNVVnykJj0mfTt+mNo+3FJVVR4jsDkvtWy2tlJCGAbnclPPK3Ri79qBCnEJGyFrLifQn1RtJOE/rgSJmOD7Y7aw6265UKU1ZLQAt71AeavCtoOMqWZgkYCeoAPEW5Y6SVZLallggPuCHnCcLUQcqKhtAE9+ySe/E6uzNOaJhChutlDucYhSQah0pUreqTIkkkf3gAYAMcRGkOdpdyHNTa+cxx/h+07ktukX1VL69RXFIDlSoClYTgJQRCUAdQcyZmUiAcgA/aS01U6P0UzW1iPOvt7/dUbMlZpkZjb2lMkkjJ+oA4F3ILSyde8yvx9zINptKBVvuAna6RMpIPSIxIwD7iOLa8ldIOa81rR6lujH7nZ5lI1GWacH0Yj80bogf5cVfx5ffOY/6W8/0H6qdYaP7tDrd7RUh5JclqTlZoM1VyTNXVk1FXvPrfJ+VJnp0E/wkA8Wx8KfI4UNtRf7uyE1lw/fNtLRCkDO0qB9gRCT3yfrB+WHKdfMW7t3S6Sm0suhVLTlO41RTiVEYCcY7kz24tDYafyaedigkGEp9zP8AvxA4M4afO/8Aidxbz3YD+ePySZxbxG4g0lO7cnzEfl/lMfMWpDFGSDtIkQex+nFe+ZVbCXfVAJwT1+304N/M+rKEuAH0px0+vFc+Y1atxbkKSczKfyif5ni1FXIQl5g3QKDhgAJnp/rwEeYd12pcAMpj054Kev6ogOKk4z7H78AzmDcCtTkZjqT3PHJ5XVqF+s60OuOmcqJnEHim/wAQvUKW7LZKAFUv1S3znshEf4rHFuNVvlSnDBMyTHFEvH1fxcOaFBQpVKaGj3qHYKcUT/8AtCeCllZqqh6b/ooN3fopj6oErMqPHzj6TPbj5w6pLWcZxnGcYsWEzx6QOvGynoXKp9DbaVrccUEpSlO5SiegAGSTxe3wef2cTxP+LzTVFqJvSlDy+0nXKSW7trGr/ZgcbPVxFPtVULTHQhsA9uOU88cEZlmcGtHMkgAfEr0AuOBzQ1+D34I2fHX42NP6bvSXUaKsLa9Q6peSkwLfTQpTUxAU8vY0P/f36cdoefvMRXPznHV6eNNR02idLULVVeaIjZSqpzuaoLQkAhKULLSnHEj/ANKnIEb+Nnw9PgiK+HHoXWdAxzp0xdr5rpump7hXN6VqF/hqdhTi/IaUatsbFLUlSipOdgEATxKLl8OOrtentS0Vv5/0VHWakuDtyqam56SO5LpbSyhKVorJbS00gJQIVGwkiVE8fPnHPFVuutwZDS10PhxDLQZBvITjJ6eQbjfmrb4FbTUEL5KuJ5e8gHDCcRjcgerjsfRV35p69qub12uNMxWP2/TNteNNdrgxLb1xfSAU0VO7EICJCnVwNghtMFRKOfvxW9MW/UWnrJdrBRU6KDTC1WupVSIKaek3KlLCT0OwlW6CopUYUQTx0o5h/Db5sUOgrVadLag5d6w0pbHTTVDGmrrUWu7KASC4w2Kz9z+IcVBUoveYC4uAFKChTbxaaC1FeLBXctr7o28aVrKKgL7dHXW4UYUoN7UopW1bi6EhJWpbSikhBVmTxO4Mtxt1XFLCdTN8kOByDzc8jmTnYdB9bK4krrXfLJUU8D2iQDIBGDlu4YxpwcY5nqfpy0ODxnG+uoF0NU8ysjeytSDBkSDB40kQBxfS+WiML5xnGDhx07pW4avvtHa7VRVdyuNwdSxTUtKyp56ocUYShCEglSiegA49wvEgbQVnAmOJZyf5Ea08QutKfTmhtL33Vt9qiA3Q2micqnzmJKUA7U/VUD68dRvBB/Zw6DQ2k6PmH4tdQP6MsSgh5rRVsqEIu76VAlH4x8ymlCgFHyk7nSBHpOB0k0FcLRyC0mvSnKDl/ZOT2hEIBOxtTFwu6QDDj0TUufd5SBk4IGUbibj63WiM/wD2PG2kd/UnYe7cpq4f4Pr7rIGxjS09T+g5n8vVcTtS/Ao5j8i+Ttw1xzj1LpDlfbbe1u/Zj1V+1Ly66RIZ8inlCF++9wQNxjEcUivKKZu5PponHnaQLUGVvICHFpnBUkEgEjsCeOjHx1vG7U8w9ZtcurTWqVabOomr2xtq3TBLpIJB67B7BKuhUocc4HFb1EnhgsNZVVdCyqq2aHPGdPYHl8cblCr1RQUlY+mgfrDNie5HPHpnZeOM4zjDwYQpbaR5dM8lxtSkLbIUlSTBSRkEHseLaae0fzT8enhgRdbdpq9apq+WVxat71VRMB96vbqk+mUj1lxoto3ESkpdBMKyqpVOje4AJJ7ACSeP0jfCN8Ko8Kng50lp99jyL3cmDeL0oABf4upSlakE/wD20Btof/4ziSeB1yqGwxh5GT0RO1Me+bDTt1X58dUaVueitR3Cz3igrLVdbW8ulq6KraUy9TPIJCm1oUJSoHqDwgqF7EnOVDMZKozx+jvxzfBy5ZfEErWb5cDWaR12ldKh7UVtbS4utpG1Qpl5lRDbqi1KEOK9SClv5kJKDQHnL/ZY+clHri5J0BqvRWo9Mbql23OXKtcoK/YmCzTvpLZbL6wop3IJbGwqJSCBxypLnA8ZccH1Rioie0YauWyh5iz0hXqHDnaqQ78pBSkAYHQ9BxcDxafAc8Qfg2pP2teNNN6r023TGpfvOlnF3OntxEFSahKUhxraT85SUKAkKxHAO0pylUUsrU2VJcG7zNnoEQSQZyZI6/z4PUZbLuw5CCyMcDhyiNo0uuqILgO6BuKkwACIAnEf114JfLrQRqbgwA0JWZBIIUFYJ/SYz7zE54X0el2mKkU7cpJwFggR8xIHsIEHv1M5jgj8q9KKdqmlhoBTywSIGxSfmAx6k9ukGftwettvM1Q1iiXeoZR0bp3lGXwt6+1F4Y9Zsaj0hWotN0ZpF0qXFtpfaWh1lSFoCVdZQSBgYIzw8uU5oKUbAnzVqJKlD1b8de39Dpwh0haUN1A/dmGUyVdAcAGZSIMgHE5jv03alqvwNvcdI2pXKQkpkpEyev1x3n2zxe1otwpoDJgDbn7l8jcVXd1bVCFpzv8AVDXnBe01QeZbw0UrEEnchMGR0yemPqSex4h9osL13Qp0JCl1J80nyEFSEKwkCBJO3b/dj6GCSFZuaNxsVBqbT9Hb7DcEawtIttS7WUKKp6kaC/MDlI6qVMOSE+tPXoQQAeNlLp9Fqs4QUwUo3kEJTj/ICe3XJ4V3Uz66pe93IbJvhrI7XRxws9oob6gojbU7Qhtax8hbE7FbsEDHtnAziR04gtyQt9xDadqEpUEhIML2kTtIHpgkDKgBKVTGRxP9enakhBUz5qISSU+nOEkmAOhP2TGMkRzTGkqmsvNoVUUdSlFWpDjClwkubfzflMyOhAPfInhbuNN+MIWpytVX/wBsahyhdFbheHypY2UjaiBPUAASo+56AdckDvxLuW9sGp9UCoS3NJRo2tAo3pMTIIESrBJJI6HPEXuaRRUrVuYIQViFKC/TnvPaf8T32jg1cttHJ01YmGXWiHEALcQU4WEAEqAI7E/b7iRx87FwDTIei/QdrcYj6nmplpS0bKNSCQ29VKKA2gAFInr9BA/UDsInLtVm56gfZpPSim2tBSQU+c4D6UqIkynuDnBOCkcO1KHbLpolDZXU1I8tpsYUpe2TA6kAwDIj0xEcHvwj+AC73ty33vVITbaBfqTT7VCrdUn8ym1DYnduOTkgkxkcDXzPEDvD3cULuNTBFOH1DsNH19wTRyp5S1qrfZtJUVKtf7UeadvNWyySlQXuWGln5cpCiUzMJETMHoNy55UU1F5P4lve2AlIQkelYCQACABjEwf5xx50HoC16OolU1uoKejS+Qt0to/ePK7FxRysj3JOOkcT+w0yUdABHXMx9uFqPhaKSZlRWHUW5OOhJ6nvjolS78YSzh0VMNLeWeuFM9IUiWktIQhIS2AAEgekdgO3QcT+kSGaIqCSkFM9MDHWPfiG6TZO5CAQUqjv/LiW3B38JbCoKG7aRPeOG1oAGyRiSdyhnzRr5W4ZPfI6cV85i1JLrkFUZk46e/8AX/g08z7lt8z1lAEkn6cALmDVkqcgSTI67v148WBCDmJWJhwbiBHUA8AbmBVS6uST2gmMf68GbmJVEFzKsDHXgF8wamQvsJwO/wBvpxxdzXdgQ31NUFCVSYVBP2P+/wDnxzq8Vd9/b3Pa/LCwtundTTI+gQhII/jPHQDW9d5VM8oq27ZlRxA/045n62u5v+r7pXHrWVbr323LJ4YuHo/M9/ogd/f+G1nqmvjOM4fOW/Ly7c1deWrTljpFVt2vNSmlpmUmNy1dyeiUgSSo4ABJIA4aErpkQncY6T3PEw5J8h9V+InmfZNG6NslfftRahqkUdDSUzSlqdcWoJE4gASJJwOP0F/2dz4Yng8unK67asOqNHc7eY+j1/8A9Ru1CVqtWnZC9pbbfbQHGiG1nzjuQdhgnqbg8vV6M0rdqrxC1GmqOxUl4oTaOXOnbZY0MOUltecCG6lLDTaVu3C5r2FCBlLKmGht3uqI273WC3Uzqmc7DYAc3E8mgdSTsFvDG+V4Y0IC/C5+B9yw+F/oi36o1jb7NrvnU6yH6i5VyA9btPT/AOlSoVKZT0U8RKjO2EwTZ7W/M6o1nXzV3FyolSdqfMKYGRhIzE+5Huew4kWnfCLzH521pu/MDWddoSnfV5iNPaYTSv1zIIEfirk824C6O6aRttCCIDrkbis1X8LSx3m1uJs/NLnJYrgElSH16iTdmVL7FynrWnWlp6ykBOFGCDBHzbxv9mHG/GUpqq2qZBF/RDl2w/8ALSCC7vzTlZ7xabc4DQXu6u2+mUF7jSsXVJpnWW10DoKahLid341JwQruUQSCJgzHvxDeXaqSt1rebRcUuKRpu7NUDXnTscpFtsPsrUT85CHQ2pUgSyQYgkxDxEo114FdZUdi5pJsT1s1C441pnmDa6ZdLbaytSJapK6lJWaOocHpIQtbTqd22CClMIrdSal0xaqXVdDS1NDX827wu33G1vrFWLSqtpVtWqoJQoplgUzfm7SUrFSpUEpkUZL9ltytE76G5uDM40Ho5wOolvcFgcPfgHfCtOjvcNSxs9Hu3fPcZ23+OPgkdFea5nkDyp1ZU1blJcL5U2+zXWpXITc6iqbX5D6uxdRUBYKiJIdWn+4UyPU3MVetdUXfTGqbNbtXaPbttK67p25tIeaD1Q68S82tQ3tOJQjYnYtJxJ6g8D+6cqaWh1DYeTbNW8NGaXbt2r7UtTZ81inYS/R+SFx/1G65IqgokH951O3iOWzmLXWS43fmPfnk0ptFQ1pK/sqaCG6ZFE+4yutAgwBVOKVAx5SzjATxY1OZo5BVW2bDsEtxts92YxjuQHDHTAHZH6Olimj8CujBbq3z6AB2T6HBVFPHZ8JOj1DRVvMbke5WXGz3AKuDmmqtRNW2wokhylUfU4kAZaX+8T7q7c7q+jcoX1NOtracbJSpKwQpJHUEcdqr5qXUOguWq9K3J543K2Uf7Ysqiv8AfW5xDoXUUylDbuKPM2DP7xLoTBhR4rZ8QrwhWbnzWM6u0ZTsUGqq1lVRU0ySltF8bSJLiEYIeQgoKxHRRGSmVfR3DHFb5AKevcCSSGvHIgcs+vy3CQeMvs1dDCbhbGnAALmds7HT/j12VE+RHIjVHiS5oWbRujbPV3zUF9qUUtLTU6CpRUpUSY6JHUk9geO9vw+fh66J+FxQUluslstvNXxLXmnC7tcS6lu26Jplg9H4V+Hax6nkAuvRtaABB4DXw5OSGn/A7yLeqdP1tqc19f7YHdTave2KotJUi0qJS0pR2uuqSYGxQSSnapQR5hJ9tdZp+1aGW3XVVzs2lnAK+uonaxVPedTvKSD+Lu1X6VU7aoSQygpcKYEsNlLZh8Z8XOhY6npSOxJzt8tzno0bnrgbpY4X4RmqXiRzSSeQx9Tnb4nYeqnlTcm7hrV69Uda5za15bXlJrtTVjotumdJudTT0c7kskSQfLD9YpJ9W0GEwDxD6vZ5Kcn9QcwNV301wtdOp1hNPR/hKU1SlHyVBC1Kce/ed3FpAAPoHTh9t2v9Q67s9E/Z9PPUtkoGtltRT0jVrtTDKiClNN5m0BsyVb2W1zkkkgnjnz8eTnrX2LSVj5dPV7VZUXB5N2rFNhakJayhgJWsAqO1LhKgEiVkQQNxpyw0tVfL9FRkaWA6nF2NZa3c+X+gHlsAd+ZVtXSWOw2WWYS6pMaQG5Lcnbd39RHPY425LmtzN1tVcxda3K81jrrz9e8pwqcUVKIJ6knuTJP1J4jyu3HwmTxnH1kAAMBfMhOTkrOM4zj1u3K4xeKynwlvDEPFL429KWurpjUWKwO/t67AiUKYpyFJbViIcdLSCDEhRjPH6UdH20sMJkSVdf8AuUcmf1zxzK/s6HhXGgvD9deYtfTFNy17VeXSKUnKbfTKUhMewce81X18pHcTx1V0pQFK2pE9usfrwo3mo8SfQOTfzTVaINEOsjcqUaUoClaQRJjaRHX6/wBe3E4tdAPKSkjdtEYHzD69+GTTVv8AIZSSANuPUeg6/wCPEpYbKWikAgDuR0+n04E9coq5an/QVAbkkgjcR0kRBHt7g9QY44c/G08PeifDx4pG2tG25u3J1Pa03i425ojyaOtcee9bSD6kIcA37EQhKjiAYT3EqiYJ9+x7/wBTwJPE/wCDLlv4vLEKPXGmqS51DLP4elujMsXO3J37/wBw+n1oG8TtykyQQQTJiyXIUdR4j8lpGCotTTCRuOq/OXp3TYulfsU6hCSNsnJjcEzgZBJ6xJjpBHFh7PyqpdOt0rrN0oruiupW6pZpyT+EcUsgsrSUg+YA2Cdu4bVIyRIB18YPwravwaagYvdkertTcv6l5tlFbVLbTWUVQ4CnyH0tpTO4oG1aEwcJO08C63WlilrENvuM0wUsIUokhttEiVKCASR3hIJABgHpx9M8CW+nqacV8bgQfz7e8L5l+1ziuop6r+G6SMDP+/il1kpaa16dfK2Q+66kpb2kpO8nqRBnEiP+76Dgfa7qnKt3yQQsKMBrIHuAQBn6D3PXHEn1TdVgNqlSW0wWxEkjdg/rjqR/KOCF4fPBpzF59MuOWe2V9bb6RxT6mQgusturSncokQCtQSiYgwlIMxw/XKqi0CHUAOpVG2aklEjqx4LnZyBzQ0sPh2csXKCz68dvViq3L7c6i3JtaagKr6ZTISrzVskHa2qYBkyRnrmOajo/w6AtqNxEpkhQVn8wz6ZSQPf9ODHzL5FXXlLf37fd6CooKpBKVpW1sUpJ+/8Al0n3OYFzR10y/wArmdOm3WptdvrHqtu5IpCmuqA4hKS0t0mPLTtCkgJBlRyePRb/ALvSgghwJ5jHL4c+2V2jvBrK3SQQRtj1/T3Ku/M15Lz7yd26kBKQp1X5PUcqJkSYMTJPTrhn0/qyoeuzFTUun8SyEtJRvO9RwrcsdeoGDIgEAxx55lXMtLdO5IWk+b8gWFEEQ2nESoSCYgJBHeOJX4OfC9qnxk85qDl7o/yarUl2feXSuVLqqdlBS3veUqZhITu6ndEEdSOKhr6sCtc89Ff9uoj/AA5sY6qE8mtMDUd+duDyZbbUtayrA2piY6eqSBJMkkwCeDbpKiVd7gpa0hLTbMgBBAS3JVuHSFYUYPecjpxB9L25rS2j6Gkwkup854LTEJE4PYerdERP3PBD0Zbi5YyFpC11SkKcEKKtoAhMATMgJJAnJyYnj5mr58N0jkv0Up4iG63cypZpDVCrBzEst4/CM1ot9Y2pukdTvbeCSFKKhHQHb75STMZ46o2DZXUzb7S0PIfSlxDqTuDgVlKp+oIP68cpdM2n9u6lpkKAhs/u9qASIUComJI3QAZ+sdBHT/k7zMs/MOzIVQXGjrq+mShFe02TvpntvqSUnIEhUdcDrngfRVjNZjccHZIXHVE8COZoyNwUQbVTkkAyT3SQMcSayMbiBBBEwY6fT68MVtp9wSY3IAg+/wBOJTYGyXQVBKQCNuevt/Offguq3Dt91NdK0x3oOFGYBnh21bUCmtcEqUQI+/1406YaG1BMgDrGccItf3A+StPQA4J6Rx7nZYQg3zJqpKzu2ggid0gjgEcwKmCrqCCTE/Tgw8xq5ThWT6SJMdZHAJ5gVilKXI2yTgfw/XvxqV4EHuYlf1ncpJ7/AF/y4CWtnt5WI/Q5A68F3mO8ApRII7Ge/twFtZPhClhWQkHA7jPHE81JYEE/EZfzpzlffqyRLNC6UmYklJA/mRxzoeI3QMxxeDx56gNr5L1lOkwqtqGWBB91bz/JBH68UdJJOeG+wMxTl/c/kla/PzMGdh+a+cWf+FzyQvHPbmxrS1acc8nUw0XcxZlpUUuCrcQlptKCBIUvcUSP7+MxxWDiw/wwvEsjwu+Lqw32pqGqO23BDlprah1oOop2XoHmFPfaQlUDOMZg8GznGyBrr18EP4cGtPCtZrhprmOw5ZNUc5WA5qexJfgWHR1tWl18vhrAqK+qcaowmY8pT3UyB1G5M64oudPjXulBX0bb6tFacpr/AESSr93Su1dTU0rSkIGJQzTuAE/L5x6EAisfgc1I5qblNqvmnUKIc5m3T9l2ICR5Wm7UpdLShufUE1FQKqpM5Jd9UkTxLvDtzcoeSnxIrA7fHk0dp5r6WXpJqqWYaau1FUqraRlap2p85h+sCSeqmkpGVDihrlxvBW/aLScOE+SEOcexlLSWj3tH1KcaOySNsU1xxucAf8cjKJ/xxvibPfCr8C1drmzW5i5awvde1YdOtVDZXTN1bqFuF50CJQ222o7Z9SikdJ45H/Bn/tM/PbmB4/ND6D5x6lpNbaR5oXpuww5aqekfsVXVOeXSrp1sISVN+cW0KQ4FelZIMpk9u/iWfDr0Z8ULwq3PlhrR+ut9NUVDVytl1otpqLTWtBXl1CEq9CxtWtCkKwpC1CUnaoUF+F9/ZatE/Dv8RNu5p685gq5k6l0s+qt0zQU9t/ZtFRupSQipdQtxxbrqJlGUoQqFGSBF+gZKSshrd1dX4wmibNzN8CGqdP31DaaK+LYoDUnL9A4txPlvs9/NS6GymCM98QeIfh78Y991tyo5fs17qVv6MurNxvrCDn8Hb0OIqnyfTvdWHhtJ9P7tWSQTxfr4x/j7tV00/W0NruS3KaxsVDC3mKgJpaBQlTlQpZw4sNpbSIAA8+ErO8AcafDNq1y16NcQ4HwjVqXaS4hKyV0lG84POdSZClFtK1AAAlSlxhU8I3HtqgqqVnisDnA7Z7Eb/RWX9mUrzPM3VhmnJ94O31XSO485UWzmzcdVvJpm9K1tkYs1nuTS1rqK55l52pWtpE7Q09+IUGNol38KDtPmI3ijVnMGsHha5nvXyiqKmvrbhcUVtnpnBUfjXayvbqaUpWcElDqIHq3KC0+4Dhz55k2i6VvL/Tool0IoLsLwloKS8KNFJTqbpAJPd1xIHpElHaDwOqu8ruviIFOXwmkobPTvVNMXUJcerC482w4Oo/dtKfBAwkutkkEA8UJZqKMxsmdFpwGuH/GJ2lnxIzkjrghfRFLZtUbhr8xdo77vGXH4HBHpkJz8QGu6Gt1ly9TS17VQ5clXK5sVBpFKbfS6yktpSRAglSVpJ9J8oRkRwL71q2psGqr/AHCjqi2jRz7tRQNqdAcO9LT1W0TO1KTG3CoClnsAOI5cr26nQlS9StU6Ljbq1lq1OO1CVMtUdPVksKnuHCt6E90JQIIE8DbmFzEqWKS+W2nrKakoa9hhyscdIcefPlhBaT3UpxQWpcGc56DixLTZ9DfCiOdPl3/5ZJ+TsKRV3ZrY9Uw2dh2239JAHzbq92FZfkpq4AVQpUsKVYrj5tGuqrEIttv8weY26pMldU8EKSEOEKSNpiDkG3Q2qbXqStbr6Wkd13d0K81C0U5qKSle3kqWnzFClQoKzvcU656vnJEmk3JvVS6jXFxLVPZBTN2ylpUIultFc0ylsqI9E70IG4wtcjcPUoGJunyT1BqvUtmpqpik0XeKVgSPwtzqKNKiQSRhh1B3RJAUPr1EL/GWumj1OOB6u08+hOM/IhCrPDDNKSwDGf7cj34yAfiCixRDVl4X+Iut5ore6oeaW6ZZuNUMTCnFgNJIJgwlQ9Ig8cf/AIz+r3tReNu/0iy+W7ShqlAWsqAUhtKDEmJISCYAEnoOOwFk11qGzvJJ0ZVOFIBCqWvoHyFEdNqnkq987MyffjlZ8cnk7c9O+Im365eoqygoNbUSXE09SEpcpn2oQ6ISSNqlDcCMeqB0ngJ9i0zGX+USaGl7DpDSD1BO+S4nHdLP2zNe60RBm7WvGdsdCBtgAD/KoxxnGdTxnH1QvmNZxKuS3Km5c7+a+nNH2dHmXPUtxZttPIwlTiwncfokEqP0B4ioE8dHv7Ov4XTzH5/3vmRXUwXb9FU/4GgUpMg1tSkhSgfdDAXPt5w9xxxqJhFGZD0XanhMsgYOq7L+HXlJbOUnLLT2mbM0GLVYKBi30ifZplsIST7zBUZySSTmeDfpe3FW0hJATAH0+n6jiJaTtstoAEiNsEdRE/19+CZpW2zskAnocZPCE5xcS49U8MaGgAKS2ei2sRmD0MZ4WrUWxG0wnAEzjv142UiA22UkwYEE9DxoqHdoOFJAke0f1/PjxZndJauo3AkZgAmfvjhMpYUuB6ldJ7/+ONlQRtCphIO0YzGP8+3CZxwECQZVggdgR/XTjFshF46+SNf4hPDVfLFafPcvLTjNxt7LJSDVvsqw0dxSAFJUrJPpMHPTjk3zs5Z6q5D379k6pslZYq5KDsS+namrSkgktLHpcSD1UkkHpPy8dvC6CqCQVewgkf19uKu/GB5f2PVfgd1Hfrt5rVx0SW7lZ3UO7CipddbYU0o/3HUKKT3BSkiCOLP4A45ntj221zQY3u59QTt8lUf2k/Z5T3cOuzTiWNvLoQN9/Xnhcr2OYRRZ0Wo01IaVdWm4B9VMDVgttqGxLnXyhO4pn5gk9Rx3i+Edq/S938Idkp7S5Ror6TcLghJCXC6TJUR1gg4J7cfnas9yXXXkJSlRB2rEgJKjMJCgZ6iYH/dAAweLBcq+d170Cyr9jXevt6lgb1U7ykFQP97aYEGQB/rxeNTZxeoDA6TQc5B/RfPcd6dw7UMqGRCTYgjOPkV0B+N7qDTV35g2elo6mkTcGmFfjHm0hZbnoDGScf1HHJ/mtdgt1bbTiVNg7FGQBnqP8sfp2kic0ucFbqB916uqah59353XnPMWoSJMkxGZkmPr7ibRXiJq+RHOTTesqGit9zq9NXNq501JcqcuUj62iTseRAJSYGAQRAMzEm6yWO02plCx+ssbjPLJ/RA7HSTXe7vuUrNHiOzgdB+qEVv1s5p3mdZbq3S26uds9e1VttVtKKqkeWlSVpadZV6XGiQNwO2QVAnMj5o/mhe6bmOjU1juFZpi6P3J2pD9n30QtwcUp0+VsJDaUBSgET6QkGew2c9OcNbz35yao17cqG2W+5auuNTdaiioKfyaWlecUSUspP5QrEZJ2kzKiAi5e2ZSqlLiQ0IAQS0BJkhRUTJzBJKhmAfuKiDXTz6upX0GZGU9NjOwRBdV+0ri6SC8XloZQpOCR0In8pxtzJ65HXgo0FEinoqSmklBBcU4AfUSRhQED5eoJJGBMEcQPSNnLtT5pQ4l2llwyNqWyEkAgxMAGZ+8dI4LPL60J1FrunZKXFpQhCSUpgLSVEgKkEgfL0EkE9ySfmS6zCOMuX6GFupwx0TzouzK0xUs1TiUtOMtqdcC1BMFIJAwOgO0wR3yMji1vw89NKt2ibheXWwhNbVKe8wABS0N4iR1AUVqE9QQOkcV/wCYun10FYhhhohx9SUn92FFRJkiCPoY9uLjeHfTqNJ8nKOnRAWpjarakeoqnMdes/ftxVF+vD4YWVEftagR/wDnf81Du8DJKYxH+rZHGzOprGkLSc9Du6p79P17e/EusLMFIAgq65z9/txENNPlavLTtKgoJ67ZIAwP66jia6eaC1HB2kiYESf/ABxbtguRr7fFVuxlwGff1VAXGmFPUvhHIHbKnenGSGU/Yyev9DiMcyq7c2sKSSlSYKZifrxKrQrZSlRwAnH3/qP48D3mZXeVuC1bZkhSRk8GCMKGgrzIr1FazIjJKZ6n6/b+p4CWvK30uDBnMESBwWuYdZuU5hQGQATAOP5cBPXlZCT1iSBBkfYcakrdiEev6oDeScqERmf04DGqKhT61ykpiZEzGeCvzAqT6oCUk9ft/hwIdWvyXOwEzHHAkZUgclTr4jV+AobBbwoy5UPPqHYhISkfzVxVU9Z4O/j8v5ufNqjogvciioQoj+6payT/ACCeAQcAcPlqj0UrB8fmkm6Sa6lx/ey+cFXwfc+qfw5c97Pqep0xo7VzNOFsLt+p7eK23KS4NqlLbIVkJKgFQSndIE8Crj0lzaoEdeJssTZGOjfyIx+8KFE/Q4O7LvHyK+Ira6mistt0vRL0Y1T06U0eg7hWJqbQ+yBO3T9xnbIklNI8qCSQkMmdx05jVmmPGbyPXSitqG2qsIqKSsYJYqrbVsqlt5Ewpt5l1JHZQUkgjJ4/Pbyi8SeoeVja6Jl1Fxsb42v2ysSHqZ0T/dUCAfqM8dBvCV8SK2DTxpWXmGk7T/ySoaekJTvUB+ZwjO7cokNgkHCR80/aL9l07Z23m0gioY4ODwd9v7jzPoTk8wSdsfQH2f3e21zDQTvDQRjQ7YfDpjuPoraaY+NXzu8KVwRpHmje65X7PTsor/Q25usbuKAISp+nCkrbWIBUW/MSds7EZBZOfXxnLTqzRtczduct3uzD7BXUUdnsVdR3J8mISpTjSW4SZG4ux+8VDaYHA85l89NI839HK/GP0FRSOIEpLYedWYmUAkyUxhSyJzEJTxWbUfIPTd0uaHbay9TWxQUadK3AfPVuPrCykySCPlG1PuZ28WJwvx7WS0obdIS2QbE42Pr6E/JRb59jdO6ozb5vKenPHoot4hPFHffFFX09ls9Cuw6OafH4el3y9cHC55gW+sHapQWpS8EgboJgRw+8vtPv6dondMUzqXr7dqFpoOhcppi688HFbkHcGkojqQVLHX1SG+7Wqh0s7VKtb7XnMMLqKl5tnc1bkJz+HVuO5biwEpOQUhU4hIU7VXM5rQ9jq6G3viovlxShb9SgBaEq2QVelIXIIhDYgI27oElSp1zuE9cWkDIPIfLn6DnjqjVj4corNA6NjvP/AFO235jA7nPy+iI2oNein0lYKqlfqqvVmpn7fVioq0fvSlsoKkpQ3hLDSCoJiSQ4clSyox/Ule/pLWFPbrdVebX3C3u/tCuckqbSXmgmpd6HdIcCEAAAFCQNqVECms1G/pu1UiaAIbqyG6eqqqxakIYDaUqSnsIjbtQJCtyxJUkkQ2+8wnHb1UqbuDrpUhbD9fUNesjqNiB9JAzABPWTMKmsrg4gct+fryHuHQIx/G4YWBxGCC3ryxz95PUp95l8wUpstdYbMkOUNPU7w4k7UoQh0LayoZ9YWP1JMdwVzW5khddVUdIpC3XPRU1KZyO7bfYJnJV1V75jhPr7mgl2lfoLctwsrqC+pZcncqACon8yj79oEcQF14uEkzJ78PlttrYmhxHr/tUrxVxY+peYYHbctuQGTsPmjL4bOe7WltYUKLrWP0TjTqfw11bcUh6njolagDKO5BSQYEgieOj3Jbm3alM01VfWnbWdgKNS2N00fm7xhx/y5CAr/wC8lbS4gED08ce0uwODn4X/ABpXTkjVs0F1TVXbT6VSGkvlL1J9Wz7dik4IJHATirhZtyhJj2djpsfhtj4HI9EX4B46ioJBSXL+WeTsZx7+uPUEELtNYL1qVmnbdor5p7VNC+PMQqqSbdW7SAU/vGUqYcGevloEdjwMvHbyHuvi88MN3047pWtF8tSnLvZ3GKynqQy82j942SNiyHWwQAEH1oRgdeB/4ZOYNu5l6ddv+gtQPs0aFE1SaJAU2ysgqUH6NYLSP/c35U/9xO7g/aS5vX+3NJLzFvr2krDf4y0O7XoIwfIeVInodjqhEfXj5hntdbY7syrp2s8SN2dwY3eowCGnI22znsvo26W6ku9rfEx7nxSDYtIkb6dC4Y9QML8/lxtz1qrXWH21NvMqKFoUNpSR7jtwnJ2k/Xi6Hxi/DHT8uudC9dWChqaXTuryHnkLpVMppa8gGoZB+VULJUCkkQtOSZ4petMAcfYNpuLK+kjq4tg8A47Ht8F8V3a3SUFXJSS82Ej/AH8QsQjcZM/px+kP4S/hXT4XPBzpDT79MKe9V7H7YvJ2wv8AF1AC1IViZbR5bWRjyo7ccUfhLeGMeKTxraXtdVTfibHp9Rv12BTKVMU6klLZkQfMeLSI9lHtPH6S9DWg01O2CMkSSnqo9eB99qMNELeu5Uyy0+SZj02CmulrZvIMbUpMgDJ6TwR9PUgQhO35QPv+v+XEX0pQEKG7H5ukjPE8trAp0hIHUYxB/wB+FtMHILetSUMkgGMGPcz2+vCOrcSggqjeD06lXbpxuqHcQcycfXhvefBUgFQIEp3A+x/yIP8AA8YvQFrfX5aFkSpWcpyeEjziUkqAhJOfr7fy/wAOPZqSQTJCgqDGRMx/p/HhKpYVux0zE9/6/wATxi9WLcHmTKSYGU8Cjxpch7h4p/DrfOXlDdaWyJ1K/SMV1yfphVKo6VFQh54tNEgKeKUBKJKQlSgqcQSiPSTgp6SOPO6SqUn2gZgxn+fv7cdIZnRSNlZzBBHwXKeBk0ZikGx2PuXFXxifDt1R4I7tR19TU0t70neKhVJRXemSsJp3sqSzVIIlDqkgqEShUKCVSOD18IHwJWHx1aw1AdQ3SoprPptltZYplDzqlS1EAiRCR7kDrMTPF+fEvyYpfEV4f9XaKrC0gaitzjFK478tJVJG+mensUPJQZ7Z+vHFblD4huZvgQ5n3hm11tz0nqu2VDluutPuO5hxEhaFAylSZIIOQU7SDBB4vbhTimrulA+DWGTN68tu6+fOLuD6O2XCOofGXwO6c9x0z+SNHxivCPZ/BjzzVYLJdDcKOqoWq1lpR2uttqO0IWBgqESCBnHfpz61/eUOIUGNi0oAHykIXgqCoEAZExnqSPSCSWvEt4kdT8+tT1uoNUXGpvV5qv8ArVDrkl8g7AMDoJERAxnoeAlbrHW621RRWi1sfjbjcKlqipmicvOuqDbYkwJUTAnoTkTjg1cquVsQjmfqIG/v7ofZqGB0pmhZpaScDsFqs1uVeHGmgylaEvwd6VQQVfMRIwJ6D+8ehzwRLXZW7VZnahSQinWmCJA80bjuCo6HP16+2OF2n+QF45Z8wrnpXUtvct1/0zWv2y50anEOGlfZXtWjehWyApJGJBKSSTAIkOuaM0tChmnn1KKvMQnO4RCoA9zJ/U8E7NbT93NU74IVxBeAKltFH8U9aZpk0dkqn1OFAqXyhhs+oqhQTuzncAe5OT9RwQ/D80A6/Vl1QDK0pSFAjcJ2lQx9Osk/URHA6vDiKHSbCioAs0pWifQJ37cyCRlPTESZ6iSlyOYFFpM71FDiUhKgo7QMggDsEnORnMY4+Kr44mmPyX6gwnz4PVF/mJQN12t9P71BtAp/PUsyNhiMx7TJMjp79bSaCq00XLyhSClKFRvJUBtAAP8AH/HiruplqpqHT9d5a2iwgMK2+k7SDKZMgjAIEY+gODbozVoodE06QQqHxOzoQoHA+n6+/txTd2jMkLB2yudbEXNbhHi23FDtuSoYU8oFtREbTM/+P58E3lxdkXylQsEhxPpcBwQsdf8ACfrxWzSuu/MtRaW6FOU7oStROQJHqOfePoJA4NPJav3a1LaV+isbUtJMhKhAUQPbI9uivrxP+z27zUFy+5u3ZKQPce4/JV5xTZw+nM3Jzd0b/N8q1FQlICQCdsGf9OBPzLrlBS4MD/t78FC9Pli1kQNxGQO/68BjmDWpU4sGREgGc/19OPoRyq5B/X1VtDhMQcHBnrwENe1SklYiD37n78F/XtV6FwQZ7JBmOAjrtz5xt2pggGf1Mx/hxzet2IS67dBK8z+nvwJdWvShZHQiff7cEzXdQS8sEgyPfPAo1lVCmpXVqI2hM/aDxxA82FIacBc6fFHfDfeemoHBBRTvppkx7NpCf8uB6oQBw5awvQ1Dqi51wn/nKt18A9YUsn/Phs4smFmiNrewVezP1PLu5WcZxnGcdFyX1BjhVbbvU2erQ/SvOMPo6LQYUPp9uEnGTHGEZGCvWuLTqacFFjR/ipvdlfQLhNalIgvNlCKjqCCFqSoAyJmOueueJbUeKFnUTDIdWwyVKR5ilsKD6EJIJQlU7BMGIAAJJiTxXrjOB0tqpXnVpwUz0vGF0gZ4fiFw9VYu58+v2m/SU7VdTfgaVW9tlDAbb3jMuL7jcd0ASSJP1fOTN+a1XzKRb2Kxq5VQbcqhTBPrqwhJLzKASPMUtsLKQTKlNgfmg1Z3H3PEr5Xaoe0/qWhqaWrcoq+hqEVNHUoVtUy6kyFT9CB/rxxfaI/DLWbHBRCLjSrfKC8dR1PToF0dofCjatXUFtRUVhZtt4Q2vTte2wX2KlJBWaV5HymckAJlxJlKt4PDFzY+Fc47ZH019dU6SbSUpRc22V1dnKsk+Y+lJcpxOQh9LYAVIUoAKVKfCp4rdN6otNVar8xSClvXrudqed2NsPk7lVFCkn1NqMOKZQQ6yrLczAuDy25iX/ltSUj9rr16206EBluo/GNtXVtHUoLqlBFUAn8qy06IyHD6lUtcuIbraajw3jkds7B3ucdg7uHbHm3nhXjHabdeqPxof6huN8j0IBzjsR8eS4j+JjwZa38Ltxb/AOIKBuptFZ6qG70LoqKCuT1BbdT6TgiR2kT1HAjXx+iHXlLyp8UOlrnYtUaXsi665BSHGallVoubTzgIDu1SUeY4BmVJWSIExxxE8cvhFu/g85z1Onq1Lr9rfBftlcUFLdYyfzJkA46QQDg8WPwdxnHeQ6CVjo5mDcOGMjuOhVG8XcHTWhwmaQ6Jx2IOce//AGgvx6AgzPHkiOM4eUkJ70XzAvfLm/M3Ww3a4We406gpupo31MuoIMjKSD1gx04t5yV+M7rHS9O1S64sds1cylJSqvYQmiuKiZ9bhALTy+mVICj3UTnilHGcQa+2Ulazw6uMPHqP3hF7Vf7jbJPFoJnRn0P5jkfiul3Pv4hnJrxa+G3UGlq643nTdTUNirpae6281CzVNhQSUOsgoTM9PSPVJOMc262mTT1S2krS6ELKQtOUqAPUcJZPvxNfDvyaufiG52aY0TZ0FVw1NcWaFCwJDKVH1un/ALUICln6IPHC0WeltcJgpBhmc4znHuXa+8QVl5qRVVxDpMAZAxnHLOOq7C/2dfwrK5feHGv17X0paufMCrCqcrEKRQU6lIaiegW6XV/UBByCOOqGk7aEAK29hIOYHv8Az4GHIHljbeWGgrPp6y06KS1WSiaoaRsCNjTTYQge07QCfqTwctKWslQwIPWRPaIj24WayczTOf6o3SQ+FC1ilGmaANpHeI6p6/78SQJ2NQSn9ex9/wCXCG1UvkMiO6Ynv7cb3FHO2M5A6j7cRl3O+y01dQQYB6HP14b3VAiI+Yjr278b6pwLRIJAwQY6cIKp2FEHKTj/AF4xerUp4lpUo2haiSJ6fc8aFwXDJAMRMYOO/Ht1SvLJ+YgbQZ+b7+3Glbgnd27mOnT9eMWLx5hO0x95MRj2/wA+PpHZU4yZwM4/hx9bhMQlIIJSABGf6HG1tuVoCZWVkAEZJVgAfr9OMWLW7ChEgk9onuc/bjmF8erkto/R+odNcxqa9Wq26xvxTRXOylyay7MIQQ1XpQJIS0EpbcUQlKgluCpTZB3fEc/tENh5I37UGhuT1tY1NquzV5t9dqK4tpesbBQIeFMhC0qqXEuDy95KWpSo+sQDyB5leI3UfOfWtz1Fqi+Vt61Be6g1Fbcq1zzXqhcRKowEJGEoSAlASAkAAcPvCNBUU1Q2te7QO3Uj17D6pB4tq6esp3UTG6ievQe71Ul1Lqv9suKIX5mwFIMBaIEEd8wAE9I3Rk4lnt77rV4SptflpWkoBPpCiEgNrIBkK3QoREKJ+U5EeRqRtdOpDjnluODJjcQIkiQZ3YH1yf1eNCuivrVVLzag2QIBABSD0k4HeJgkgACSOHaSd1RLg9UnxUzKWIkDkj1ywcXQ0UVClF/aFuLUsqWJG8kqyonr16kmck8fNc6oF1fSy0ltI8xW1S46TtVuMwrqnBPUEDBkR1vU6qC1ja6t1apWSVYHy+r5pPcGcglJnsNVvq1vOB8mdiQoAwkgRjp9BBiB1kiSRYLK3RTNpo+WFWD7YZKt1XN15IoX9aEP/h3VKCHmCyoBQbSmDhIUJISPT7kp29eC5ycbcc0r5flqUVgZ8sQralIwfynvBiCQOAvzGKkMVDiTsdYIMyYQCYz9YUOsRHYjJj8O1zTdbW6IQpTyErATtIKtqZAkQcj7kQfrx8RXuJxoXPHQhfqiHATAHsjfrCnbPLQqT5inKZttYKjs2pOYicDHbr9usl5Z1hvGkU04dAdCN2xH5T+YE9zg9v8AEjhhu7Ip6FFMobjVUwSlREbBtgdO5wCc9enDDyN5gtaWrmqOuUlCWXw2ggE7wOuVdITHt/gOKpdA6Wmdp3IOV0kHkIG6JbN+f01ekKWFuU7raGHVQJP93+G769Rnix3hX5hsV2pre0/UJLjKi1uWQB6kqnJ6dsH37Y4AHMSzsVd5aYQG/wAPcGw5TlWRkT9ZPTB6fYE8RG2avvugqw/hy+8hjdsQqSomflgmZA9p7Y6ngdRksnjqWDzMIOOWcKDW0La2mdDnBIIXTbU90bqLckNOtrABnaoK657cBrXlblwzAJMgmM+/+314B3KDx4MW+qprTfWiyw6pDRqSDsQpRA3E9SnM+wEx14GvOT4ha6zUFYzY2KY0LClpbqFNqWt4JkBZkgAH2A6d5ni6qDieKen8WRpa7kW8z7/cqjl4GuQqDAwZGM55BEzmFc2mKd9xxZSGwSo/3T9/1A/24CmuLuzV+aW1ev8AOnoR7fX/AF4GeqfE5cda14bqXXA3JQlCUwlSgPlKB3MxP8O3EXr+dSKepARVtthJkgmUrAHQHBInA+ggHHGG9yOnA8P8P6593ZFv+gXtpS50g8XmAOXuynHW7+1xUiDJx1g+3AF8TWpxpblVqCskpW1ROxHUEp2pj9TxNtceIG10bSVfu1KcI3IKpJSRMgDMR/DuBHFbvFbzVHMzlJerfa6aqW4hxlQCGypS2A4NylRMGR0HUH6Hhkt7RNO0DlkJKuduq6WmfLLGQACqXnrxhOOPTiCgkEQRg4yOPPFjKsFnGcZxnGLFnGcZxnGLFnGcZxnGLFnHtt0oWDnHtjjxxnGLESuW3Oz9ksijuiPNpwU7XdoWREABaSDuSPtPT24Odj8Zus+W+l/2roDUwYqKUeXVWyoKK1lTZHzMhwFQjumYxgY4qIhRT9uPSKhTRlCiD9OB09rp5j52gjqCMg+8HZHabiCshi8NriMciDhw9xCudZvjmc1qK1N0lwsmh7yhhBS3+Lt69qSRClbUrCTPcRHsOBV4r/iBX7xgWihpNRaW0bak2tJFIuy0SqNTZJkk+ohQPtAzngBEcZx5S2ehpna6eJrT6ABRKu8V1U3TUyuePUk/mvvU+3HzjOM4JIas4zjACo4BPG6konK2obaabW864oJShsFS1GegAzxixatu6O3HTf8As5PhVVqrmdqTmncKYKpLA3+xrQtQ61LqQqocT7FDO1E+7+PlPFH7N4KuaFy0/wDtmo0ZebNZQf8A+43lr9mUny7iQt/YFCM+me3Hen4P+kNCaH8IWjtL6Q1Pp3UlfbLczW3tVtrEvrRV1X751SwPUn1HYncBhtIMHHAu61OiA6OZ2RS10xfMHOGw3VvdF2bY03AkmBHY9z/lwT9MW/y20zA/KcjrHEY0pa0nbA3RhWPt/lwQbLR/h2oI3kZBT+ccJw9U2FLkNeSJyFEZUR0+3CWtd8segH0jcAO/fA4VuKSlKCJgGUn2PDdVukpmRvBII7q78erUd0jrHvmUEqMgnb34SOLIMzhWevQ/XhQ+AlaYVOZ65/8AHCdTQKldNgUADH1/1/z4xbLWlSnGh8xBkHt79OPi2FdBk5jEAjjalpSnEhCN6yQkJTn1GIH8eOdGuv7R3yp5Xc/OZ2kb7p++XC16NuRttgvGnlorBqBbZDdQVBxSEMpS6HNqwpQUlHQmJk01HNUEthbkhR6irigAMrsZVqPH542tMeAHw43DX2pGl19QpxNustpac8t69V60qU3ThUHYgJSpbi4OxCVEAmAePPMH+0ic9NfckNY6Yet2jbNdtSuFuh1DZWHqSssdIuQ6w0grWhSyg7UvqPmIlRyogpA/xIfiYaz+JFzbaut5bFl0hY3njprTqChaLS26ltK1OOpSkvvrDSSpw4EbUBKetbKt/wA0bUjpgD+97/r/AL8PtosMUMAfUty8/RIV3v0s0+imcQxIn6gMogJ2hOEgdEgDoJ+3CJFQpo9MpwOk4mfv2+g+vHuve3O4kJmR6ZIHCo6tYotDv2gWi3OPO1qalNxWhRq2kpQU+SnO3YSdxkTI4Kyu3wFBhj6lahfVbg2dq21TtBJJ69T1zj/DgpaQqbezYqCqZr1VNa+t1NVSrZWgUTYUA2SsHavzEk4T02wT24DdJUulBaOW1ncv0gkkHGevvAnOOH2zV5p0ADYUpEKJGTt/h/A9f8OlLKWyalrVwB7NKONPcnL1/wAs2VPKcSAkyVKV2Bk9gO3YIP8AeMze76Xq9Ns0jNdTKp1VSQ6A60tr8QJwRuj0jaqCTEhWe3An0RdQ0EKc3gEgLK1KzkCAYJ3SSQP+2PqbO+KLx7am8ZFr5eUWpLZY6Oj5cWVFioP2VSfh1VjIWlRW/mN6gEAhJ2g7iACs8NtHVEuAxz+iR7hRkdeXLbmtOomP2oghxZ3Lp1QEwRKfUPSYA9+v5YAJIPD14cNWmyXKlClLW0GhuTuJ2x2mI7xHuTPQDjbzU0dW8udb3Gx1CQupttSunK0Kn1pxIJ27pI7jGBAgcQ/SledNauK1pmmfcIW3MhWDuyIxjAI2jHeOPnGSjD4paSQbkL9FJ5CdFRGfLsr+8yU2u86JtddbXVqZLDfmLWkHaYTKDBgxMDuR1PAV1fYnWKxypQlYZSoOHae4BhU56YknIiT7F35f8xVN6JqrSSlbBG5JIBJYMK6GVAiUz0JHfM8PWir3T3NP4erbSW3xKFqGPpHbt9cR24pZ9PJQvcCM4P0ROFp8Pmkeieai7vbjp+5uF1umlykqAqHKZQ6ImdsdPuBHfEtptXXCrtATXUQujaAUpfGV7ff6jAAIzk/TgRcwbIrSGrlrSHF0yiogEA+naAcnvAgE4A29+CFyv5yU1FTKZqlNOsuelC42rI/lPc/m7kwM8eVtM17BNC3K3azGzQh7zB1wq1uS1bS827O9mFEqT1kGDPT26/zGWo+a9lFOUfs2tYUgSElRO2T3EHvnMmIJnizmq6ax6yZWhmrS2ogwjzDLhPqMdEyCD7DA6xwJNdco6Zxai4+lTPRCUKkIJPUCIzJ+5nABjgraquHAEoIKyaTAGyAep+dSKphTdHSvrCgdpLREAA9DmB74xnIkkwO66su92cUWkKbBn1DcCZ985iDiYE9COhzvvKxqlcLiWzuBIG9RSSInEnHT9Jx14gGrdFqYQreXApaQAkmVHE5nt26f4cPFA+B/sfVA6yqDG4IwhJdLLcLkrY9Ur9UpUW1SAScwZ6ZgiT1ni2/w1/hm3HxFaOu+t62oUzpkVSrWwyEhX4upbCFLcn8oQF7enqKsYB4qnqewVVKqP3hUYSElwBSSTAIgzkxA/wBJ47ofBRs1Xyt8D+mdIampX6S6rqK64VNLVwHKXz6lakojsdgQqDkFaunThkjb5cgqt+J7lmn8P+49eyplzV+BDoDW7DqqxmuoKxYxVUKw27PvkFKvsoHimHiX+AjzH5Zofr9B19Lrq3NAq/BqAobmkewQo+W73+VYUeySccfpB1lynZrGy7ToS4yoH/4+36fXgSax5XBhbifJlIkQof4HiXBc54tmnI7FVrNb4Zdy3B9Nl+TrXHL2+ct9Rv2jUFouVjulMdrtHX0y6d9BEjKFgGMHPThnUmAOvH6b/ED4OdGeIHTy7VrLS9q1JSIEsisZlxjt+7dEONn6pUn2yMcc5PFT/Z5WXnKi48qtSG3nJFmvyitoHsluqSJT9nEEZ+cdODlPe4X7SeU/RB6izyx7x+YLlSTgcZwSOf3hJ5ieGG+qoNb6Uu1iO6Gql1ndSVP1bfTLa/0VORIBxwOVt7FRwXa4OGppyEKc0tOHDBXnjOMjj6lO7j1ar5xnGxLClKAAJKsAdz+nBL5S+C3mvzzdR/wpy91beGXDtFS1bnEUoP1eWEtj9VDjwuAGSV61pdsAhgI7zx9E9uL18pv7Plzv142y7fajSujmXQCUVlaqrqWz7FunSsA/dUfXiz/J/wDs1WlLQtpesdd6hvziSFqYtlI1b2lDuklXmrP3BSfpxCkudMzYvUyO31D+TVx28sgTHD9onlhqPmVXfhtO2G8X2oKgny7fROVSpPQHYDH68foN5S/Bp5DcrFMrpeW1kulQ16kv3pblzdWfch1RbP8A+A4snpDlJb9I0bdLbaCloKVsBKWaRlNO0lIGAEpAEfTiBLfYx/LaT9FPisrzu92Pqvz1cpfgweIjmq62o6Ef01SuH/6i/wBUigCfqWjL0fUN8Wg5Tf2ay61xZc1rzHpaQKSN9PZLap8hXcea8pA/UJP247KUuiEhPyCB2gpn647cOtLo6QMYQesAQOIEl7qHbNwP36qbHZ4G+1krn5yZ+AdyH5dBh6usN31hUNq3JdvdxWttR9i0z5aCPun9eLS8p/CRoXk2yhvSmjdM6cATsBtlrZpnI9i4B5ip9io8HSn0hHpCNsnEjp9OHSg0iS4nYkIcUoJBmNqpwZ4gSVc0ntuJU+OliZ7LQF+eT4w/iPf5o+LzV+nqC7VNZYNNVhtNMkqltstbQ6huCQUh7fK+qinPQcRX4dHjpvngB5hXvUthslFqX9vWlVudoq2pXTsLX5qHUuqKElR2luIEfPJONphvxILtYbr47ubj2l7Suw6fGqq5umoC15Pk7HNi4bHybnErWB7LBgdOB7ou4Nlrasp81RIAyCfv3j7frw5U9MySNsWNsD9+9R2Frcl5X6IfCX8bDkT4hecWm+XdHdbrZ9V6noaV2mNZSBNtNc6wHV28VO7/AK7aj5fqSAtcJSZ6k34mfxU9EfDF5d2+qvdC5qnWN9qPLtWl6Otbp6p1tMKdqX1KkssISRClJJWtSUpxuI/NmNHLqXP3KFOKCgpJA2hBBEEHqD0gzIjtE8auZD911VqC4Xa8XO5Xu+3FXnVdyrKtdVV1Ss+px5xSlqMT1VPb248PDMQlDgfL1CjOrZSw5591130//a2dLVmqaGnvnJG/W2zPJZTV1dJqJqqqKZReh5SGS2gOISz6kpC0qUsFJKRB4u54bPiveHzxbM0LWj+Z+nmrzcGHXk2S9OC03RkNgFwLaeITIGfStQKQopJCTH5ZbuwqjcAgpVlJSn+Ufb+uvCYsF2G1jzBJKW1epCfrnjlPYad5wzyqK25yR891+x1FxYulIzU0r9PUU9Q15rDzDqXmqhM4UhaSUqTI+YE9OuOIvzq566J8OujRqLXuqrFo6wKqEUSa67VaaZhx9YJSykn53CErO1MmEqPQE8fmV8G3xUeeHgQtH7H5d6xVT6cVVGtXYblRtXC2vOlO1R8twbkiACQ0tAJSCQTwn8bPxGua/wAQa/Wet5kXtmopbAwlmhtltZNHbWXCCF1Ip0qKA84CApfUgQIEjiHHwzKZMF3l+q9ff4wzIHm+i62/Fy+PBpXkjywb0pyM1VprW+t9T07iXr5bKpNfb9NUqkqQpSXG1QqsVMtJkhtMuGTsSeEtO3KQFbiBBE4/U5/onjy0n8Q93IKiBPY9Zx/lj24UtN5PUqM9RBOOG612yKkbhnXmUoXW5SVL8v8AkvoOxrAjuf8AtH+nbhw0Rox/mTrOgsVLU0NHU3NzyUvV1QllhBEn1LVhPTv3j34aax4JISCOwn3zwidcCT1A3kAiPlnrI7j/AF4nyvA2Q2GM5ytN4YNuq32lLacWy4psqbO5BIJSSkjqMcNbx8xRKgBGVQZjt+v368KahwrJkqjuOsDuQP6/lx7tlvNU6CsScHb3Of8AHiAfO7ZFWkMZkrZbaFSyoKG0rMwpM9/bv/X6yG0aeqHXElxkpE+neCAOm0QY7SZ6e46xIOVGlmHdUW6orqf8XQtPoW80MF9IIJQB23dP19uLu/Fn8TXIfxN1/Lxnkny4Xoqt0/avIvZ/DoYFY8dpbbCUE+aUlKyXDBV0zJHE9lE9pBI2KGyXCNxc0O3CpzpynW0gNs+altO3a55Y24g9T1E/pCZ69ZPfLodPaWdcdSpLtShYQhRy6SdsxEqk7xGJUkgZAHHuisTNtbK1gvuqQElSCBux2OZMmJyTgdYmDc0NS/i3kBDgbYUZZCnAUgJCSRk5EDbAIB3HJKhwUncaOAvPtEbIRTNFdOGD2RzKuFqrUFVcbuLpWVDj71a4S+oKHmLUVesqx1KlEYjJGO3DPfqZNqriVIaUhMCEZBnMBXQESB9gY7g7n3vO0o+0n5qR4lKSr1BJKkgSBnHYQP4jh6asX7b0nT1aUtqQCEJJ9Ux+WN3pjOOs4VmeKlrKEuInjX3vRVTSDTvCedDanqKa2teS84XWFKRhPpJjdEYI9p7wPYy702unLU+p1CFJadPmjbkBXZM5kdMkmJ7cDWw1ZtNyW0pQAIKcIwcgnABE9J6/6yVyrWGxUNoJplubnPUCXATEDB9R9Q7jr0BgI94tOpxk05yi8L2t8pRIe5mWrXFpSxWPhdQhJKlElQT2B3Gc4P8AH643s8r2LolC6Cvf3rAkT0USIJ64BAgGcHIxwKmaS2XF1S1OoYIKlqKnCOkE+pPWZAOcQSOoBcbJVV2mlLVR17yKYepxK3ISkE9ZzEj/AAOcjhPmtpjGIiR6HkpxcCNkRqnkvfklKmKlSzuSE7mNu5IBOfVOBBkgjPbB42s8l9bUoU429TVJQCkE75X6oChnImTHTPeSOGex+JC6WB9LDzm9xMGT68dp7T1zGZJx3ea/xeXQ04SLhSpQoFKMphQjrA6jAkECI6dwPfDXZ0hoKiSmRo2TVqjSGo9NNPLq6NohMqSralIGSYP6xH/tEdeBRr/WztK04PI2KEz6AQDBETkZ75Mex4lWv/FZX1TIDlSxUhM7vVu2Kic9BETjH0nPAD11zbrb3ULUtlCgRuSENiB2ASf4Y7k8Mdnt85IMg3SncppSTqCb7pqL9t39igbo3krqnkMpVkwVKAHpT1iRhOZ+vHUzwCVWstHFmmu6qhbKFbWnFkkiMDr+nXioXwjfDXWeJvxI097q7S0/pDRhNfWvOD0Gs2j8MymCASFkOdClIQJHqE9aqDl9SadCfJbSlKTkhIjHDnGPDZpAVU8ROL5w3sjny718LhRMtvyVEQcDOOHrUGkKa+MhbYAKhBHAd07dVW2oTtUZSSBnKuCjo7V/mNJQVgpH5T3HsONkGOyg+qOXKqZxY2SRgdo4g990SmfU3EHI29v0/wDGeLL1drYvtJvRG5QgE9fsf69+IRqnQ6WStQbQPLHvtAHWST0Ed+w4wrNXVU38UuoND8j+SV91Dr922N6WpmSH6OsbRUN3JwpV5dO20oEOuuEFKU7ZzOACeOBfPPUHKzntq6uuGn9I0XKmprFjyKWjq3qi2trn/wBQL3FAVJktQhO2QiMcXI/tBnjksXiB5uad5faGvVtv+k9EtLrq6ut9QH6WtujyShSEOJO1YYaARIJ9TjkEjjnGXCqpKpSIzPvwyWynMTNWSCVCqoWyjDhlXu8LH9nzufPjQ9l1ZX809Mq09e2vxNKvT1C/XqqW9ykkBbwZShQUkggpMEHi3nKX+zo8k9HJbXfU6r1i+k5/H3H8Iyuf/tU4Sof/AOw9OHD+zl+KrS3NPkvUcmWdNsad1No1td0NZTuKUzqVt1076he8yipSQlKkj0qQgKSBtUOOoNFy7Hl/9MJIjHc54iV1fUtkLNWAuNPboAMlu6qnyd+Htyu5JhP/AAxy90pZ3EAJL7NsaW+QOhLqwpaj9Zngx0fL6QkLSpxKR6d+dvt+n04LTPL8BCjskJTtPsTPQ/y4cKbQ4Tkp3d54Evkc45ccogI2NGGjCE9LofaTsR2gyIAHDnT6I2lKdgMeoADA9v6+/twUafR6Ns7NpAAInBHCyn0gltcEAZkyPrPGi2x6oaUmjdx3QoAZwP8AP/Thwp9Ipj5UwDn6D7fw4ITGl0oVgfNhX1HCpNgQiRgjAP2j/fjFnJQKn0eCSdgA6H2iev34WM6XLYHowBiBk/TibM2ZDQEgEZyf6zx7XbG0JCjCzmPqOMXhKirGmkbAoJC0+wEkcK2LEEJTAJ2HOM/pw/pZbQSARMY7ZH0/rvxji0MjcMdp/wA/48YsBXIn+04/D205dvD7/wDr/Y7caDV+mq6ktmoHKSnSlF1oahZaRUVBEfvGXPLSFxJS7CiQkRxB0c7trUTuUNwgDoPp/n+nH7CedHLSx89+Uup9Eakp01OntW2uptNwbUmf3LrZQVAf3kkhQ+qU8flT8SHgm174M+Z2pbHqy0umm0zqBzTi7uyPMt9fUpp26gJacgBSzTusuqTG5PmQQCI4cbBWa2eG7mPyQeujLX6uhTvpxlL9vbDYSErSVKUsjJAPSBOZPQjuRjht1NZ0O0y005bKDIRtSSrpPpAzkgZV7z78L9C07lRQmSvy0gjbvIKsJ7jIxt69zjpiSOULTZLSEhbiyP8Ap+ohI6EIOQRBIJ7oyMGXuOEuZqfsEIrrk4uEMIyUC9Q6HXSVDgUhRWCQUmQEH2+sfWftieI5V2w0Z2QOskx8w/y7ffPB61BpYISfM2B9UhaCoEmMEz1HykZHbHXiF1nLh15LjimzBMbts+YfpnOfb/bjm23ucctCXq64xw7SO3Q6qEtKccLDbjLMnZvVuUMDE9/fH09uPiWd8JJIAgerpnM/+OJXdtB1DB3EEdxHbt1/UfXhmetC6VSiQdoI6ffjY0zmHcIQK6OX2SkLI2JHQH36E/1PGxx7yhEiMD0yTPt/X8eMcSULJGBGMYP9f58LdO6qa0yzc0vWiiuxrqJdK2agE/gVk/8AWRH5hGJ9xnjnJIGjAUmKEvOSmKqqEtgbiAI7d/8AX2/qeENS7O6em7aCeg69Y+38+NlUVTEdOh9x7f5/x4yho11Dg24HWB3ziP67cQXOLtgiLYwwZK+UFrXXOpwoARgCI6fy4nektDrdI8xMpyTHUjb/AOP0+3HrQ+k/xDwJbhKQSSkSBPTHtP8AHi3PgW8AWtvG3rZWntDWxusq6OmNQ+48sJYQgKAKlEwAkqEde4AHB630DQ3XJsB3Sxd7o/V4UO5PZBmz2Cm03Z1vuJRtAPRUFWZOe/369B2y20FAwbi7V1SYacG5tBTt3JGCfuAJMxjPq4KPiu5MX3w8c0blo3VVGKG5WlzyKunUQApcjsZ9OZGSIjIyOBJUA1tRDQQl2pQts+qcCB6QcmCBG49hAyCDU8zGAMaNggVBTyOLnvO7uf8Aha9QXRdXSkqKypSStwpSZ9kyeoBBORJ7ZxA2vV182vWtT+Gx5oWBjZCVBUhQBTtJyB6sHaT0leurl/yiUpCUCoTkOLKkhKgVbVhI6EBPYA9SYBPEDcU3WVyQWvxAeT5QSp7KVeXCQVJBCinbuIxuHsI4TLvOXyaScqwLPTtjjy0K6ml7+liqcQ44ksVAU0szKlE9CTA9UZ6mARHE75Jupo1XCyuhMj10zjySSQmSEZ7GFAAnOfYcQ7lXybvHMnQmv9U2+ts1NR6AoW7tcGqytSw+6hbwaT5CFZdXKspTkDHdIKvSFX+JubdY2t5LzCIdWlsrSlQJBMgj6SAeqfpHAqztEkPhvX1Neasw1PjRHONjj3A/kU8a40240866lDiCAdiiQAnP8VTB6EdMyclkpdd1WmKxKiFLS8Av0wSEpkESQMyAkq9gAJngzt6aReqVtatnneUFkLSSESJBj2A+mZycRwNeaXK521KIaTuQpRWpM4Mdz1nv79+0DjW58O5aXjkplBxLDKQ0ndJHrzR6kCXaN5DS3CS60pfloZImQIOZ9j3CZM7uPIuryHgFKJH/AKe4BS/mIwPuE9ROfmEcQWkoKq11DikJdO31KS50cVgJ9IyICsx+hmTw+Iurjill9KFHfkbNwCumCDBAB7nEEDhDqbH3TXDVNIzlO1U+i5IUhTzp8s+lM+kHGSR7wZOQZiY6R6u0g9dcmrXChCo9H0knoSZnGJnI7LqZ9mpQA064N235U9ukBJ69R1kAH+G01gbUEkuqIQHNqwAqJxGQQCTIxn7SeBTrJURguaFK+8Ru2BUSf0aKR8qcDjrQMBSFA7T3GAMZBjBj34ZrjpVVW3LQfCIkbsIAjp16A4gfY9uCM66iCp5CkpUCVHYVKKZghREScH+sceHKpFMyspS00tCSkJMbgYz/AAMg/wAI7CO0Tx82qPUUjJBqWeDHm5d/B14kdP64oGqi4U9KtVPX0CXik3GidGx5nPpST6VJVBhSUzMcdw+Vuv7bzy5Saf1laGK1m2aloEV1O3Vo2OoSvG1Y6YIIkYIgjrxwoqdu9QCUoSkBAWsylJjp7erAiB0meo47W/Deor9VeBPlw7qR2oernLe4qm8+N4ovPcFIOkx5AbInO0pHE2OR7x5gq140tccLWVDeZOPf/lTVykUw6SoylWMCCOHiw3pVO4gAgqiCmMK9uN90s4DihAVH8R34ph8TP4ht58Gt509YNGUdruWo6+ncuFeitaU43RMFSU00gEZdUl0Rn0QZEg8d42lx0hItPSyVDxFEMkq/NdztsfKvST1+1PfbPp2xUZCamvudW3SUzJV0SpxwhO49hMnMA8cwPi//ANoI5X80fDvrvlHymYv2qa3V1tXaH9VNf8hbqRpak+cllLiS9UBaAWydrYIckKxxTv4rvjia8dnMLSj1ot1ztWnNN2pLJo618bV1zii4+6EAlHo/6SFkbilJmJCRTC729TaiJCSCCo5nMmM5n+X04J0tMzIceaKfwJ7GB8/Psosthurqdj76KRsJUQrYSEwDCQB0kwB7cNPmT7yQMDrw53ZoNLJJnHv1nrw0vvAHtPbGBwwM3QKpwHbLpr/ZYGV1vxH7i2thx+nb0XcnFKFKhxDK/NpwlRWQVNEytIKSCokpJiRx+iM21pLZIIxII44Jf2TC/u2vn9zmp0IpQ27pehfU4pP74FNbtCEnptO4lXeUo47l/wDFxqQF/wAZ6jhdun83C4xuySQpEaZrzUylBUFSk/3evT9D/Dj6otNpj0iI/SMf19+IurVZxC8ggifbhO7qgqUsEhIKvTnGR/X+3A7ZdFLjVNoAgpEGInAPTjwbmhsgn1T3HSOIkrUsOKMnasyRA9PtxrXqMkbh6k9579OnGYXoUuRc0gkCB6h1PHpy5pLiVBRSDII9vrwNNf8AOGxcotA3jVWprtT2TT9gpl1dwuFTu8qkZHVagATAkYAJ+/A3uPxO+QFt5W1Gs186OXK9O0ydzlQ3eG1vLyQEpph/zClkgwkNkn7CeN2QyPGWgn3LV0jG80R/F34vNNeCzw/3zmJq1bgtFicp2iwyQl2qdffbZQ2gHqr1lX2QrhJ4UPGdoXxp8lbRr7QN2auNhu29vZUFLFZQutkJdYeaJlDiCpO7qmFoIJCgT+fz433xi3/iGa+Z0foOru1Byf007vYbdKqdWpqwK/8Ar3mSApCEglDLapKRKyApRAotbNU3CzMPtUlbV07dQ0ph5LTym0uNqKFKSYMQotokd9iZ6DhggsgfCC84cfp6Iaa0+IdPsr9S2p/jV+GHSWtq/T9bzj0wu4W2uat1T+GQ/UU7T7igmEvNoLTjaSYW42pSEQdxHB90pzKsfMS2m4advdn1Db/MWwuqtle1WsJcQtSFJ8xtShuCkqSRPVCh1B4/HrVa7uVRpijsiXUJt1vfW+w15aQpKlYMqAkiB07SeDR8Pvx5aq8BniGsmtrI5UVtJTti33O1uVK0sXG2qJ305SPSFJ3FbaiPQ5CsyrjrJYWaMsO68bXPzuF+rI1gU7ukeqN0nEjHHNn+0p82eX1P4S7Nom9XY/8AHlTemL7YLfS+WtxgNbkPvVXqCmmXGnFJSoetxaYSFJSspG3Ov+04afuPhBau2g7C/aub90rlUP7IuSfxVJZWkAKVXF0AJdSvclLbfzbt+4QgFXIPm3zk1Hz25nXjWmsbzUXvUuoapVZX1zxBcqHD0iBCEJACUoSNqEgJSABHGlptcrZfFl2AW1VUtIwN0TtEXHzmfLQCPLbCAdgEkmJI6bYkx/GI4le8UtOunpQtTz6AXVD1BQgjqCSREgDIJgyDwGNCauLA8rdCGwsEkCIgSRjt26R/IGmx01Ezb6Sqcfpq124IU44ht4l+mg7Ql3cAEqMBQifT3PTizraxsxDT0SNea4UsRewblKrRpf8AbJDjwKwtJJjptAyCegSNvQGOo+vE95N6e0baeZForddWm43vSNFUJXdqO3ueTUVjUEFttwwEq6HqPliRjhttC37mhDTaUIkCEoaO0+oDM5EHABwD2mOJ5yn5iWzkVzBs+oK7Ttm1Y3aKs1K7PdEKXR10JILbkdpIPQ524IkcOsdPGyIgD9Cfiqjmnnqqge/3oG8ydN2pd5rl2+ndorUt9xVOy64HHadpSz5aVqj5gmCSBnJ6cDO96IXWKCtnlpMmVQAM9QPacR9IHBu1zczrDU9wuDFAzQoq3nKgU7DZ8imQVqUG0SZCE4AIyPSPpxELjZ/wrhO9tK4CCSASPzHrJj0RPfAxKeA9W0P2TTQ0/gDPVB6t0d5bSitojqEpGCIGZHvkA9e3DFX6cU88EpbSqVdQMKJjP+P8uCzdLQt7e4pBPkYG4AFcxCVQcQMkzI/WRGF2wVr0NNoUmZEAAqKfyzORGMzPbrwvVUIB0t5php5dtTioAnTa3FICE7wqQmFyFmYGe3frMj3zxIbFoRaUNfukqLyQoH3BJGD9ZH6QZI4nlj5fOUbfnKStVQ4CjdtymACECY98gduCdyF5aaZvnMC1o1ld6iwaaqalLNyudPTmpet7KgoechoH17fT6UnAPEqhsxzrk/JBrrxCxrdMZ9ENbBpj9mttpUElQxPXZHSYGD9O/frxazwAePHWXw+9U1mpdJCkqXrjSimqKB9nzG6hoGU+YAQEkKBjacZ6HHAMGmWbpfax5Ky5bad1TTZKQ2pxtMknr0IxkmFGM9eG2/3E17pU4FinQs7UIQf3YBjKQIEmAMEgdJggnJYG6Cxw27d0txyFzw9rvN37KVeMPxNai8XvPi7631O9SqvV2cLz/lbUJ2hP7tCBAIbCIgnsR9eBnarXUOUq3mE1inW9qEfuzucCUmD7hM9FT6YUdxMDiWaT5U12obTU1cOspYaipWUhJQFKgqV3MqgGPoOp4uP8GPwsaG8Vfi5tOk9XNpXZWWHq9VCl0oFctsAhuTmFdSnrASOo4gXKP7pSmplG3Qd+yK2yVlbVikidv1PbG5XMTXdoradSi4yWfwju5W/OSlSyVEYMwlMxtPqM+mRGax1TD8uLWpSW5G9RDkeakp3QohMwckR6e8if0K/2i74a3JXk14Q6TXWlrFa9G3uirUW9LVKShuubUlRMozuUnYk4GQADHH58r22ikujTRbbbceWg+pwgpIASU4OVBJSAQIBB6ySK8dK6Rxc/mrNbEyNgDOStk7pOot9/rrPW079LVKaJLTjam1gSSAoKKYE9AqIwMGOHXR7irXeGFOJUoPN+W7Hq2GIJAOAPYwIIgR14d+dHiJvviB551Wu9VKp6m/3mpLta8wyltp5cJgACIT2KQTgmPfh35ycxaXmnrk3hmx2rTbNQlDS6GhbhDRQhKSsTkFZyT23ZxI44wSCF+tp6r6fbGaqmEb24ON9+R7eqsF4ZtG/8aaauQ/aVBTfsSnRUIpnH9jlYS4EwykYUYztGOpnB4W849F0zlBTgDZ5o2kJAGxUgGTMxP2n9eBz4eb04zXU5Q6ht5kgt7HPSR3CcH056kmVdTwYNe3KlqqaHBKakBQXkhC+gBg94VjrgdAOLIonRzw5VN3KKooapxaUDrbyhTc72djJeUFiBClQD+YxkEQTu3YxmOJVe/CY3dbck06gl1KAlKkypCpwEp9oMffqYPSXaUQhirc8wJekENK3lKR8sCPeIzA6nGJ4l3/Ei2G2yydkrCTEqJMwD9TPbE/y40ksdPKCCFtBxlWRuBB2CqJzD8Ntz0sXalhsutA+ptRKSg5xumCf9sRgj20Wm/XW5tW+jp362tUS0ilbZLi3zMGEpzk/cycdJ46BMst6vpFoqUuB1aMqURCZPdWAn8xgkjPUCBxH+Xmjr34f+a9o1vpJqmpr9p+oFTTLfpEPIQSCn1oPUkE+0YiDwu1vDOn+WSQmqj44MkZJHmHLoFSKuuz1kuamK2hXR1TDhbUghaFoUkGUZMpIOCD0gn5shM5WsvtBRP7o/lKtoG4dOsQcgAD7zxZ7m7ykuvOjmJetT3ZlK77qKpcq615hISgPLJUopSkSPUOsCAIkzwNtY+G+rs9OVtUj7IUCjrt6iFfKBP8wTERJ4ES8LTvGprdkwUfGtN5WSOAceYyhPT0H7WcLKNzqnAWA0hsrW+tXpgYg5URAEnoOp471+B3Tev7L4VtI23mUyhGq6CmNO4ylfmPMUzYCadD5GC+lsDcU4+UEbgeOa/wAKXkZYH/EDdNba6RTUemeVtr/4gerKp0CmpqlLqU0/miCVlK1FSUpglSERu+Utvjx+KVrbxF62uNq0dfLvpfl4y4yaSlp1inrK0sqUU1DjrZ8wFaxuDYUANiJkgyqVVpkMpYBhc73M66ytpIAAG7lx9eg+HNWe+Lt44m+Ta9NaR0jqZyl1QxcBdrwLbVAqp6ZoQildUk+lTiiSpBztTkdOOYPOvmzeedWua7UeqLs9dbxXqBfcc3Qlv/02UemQ2iVAAACBJkk8MVXRrStSkoWSSqXPNlaiSSSSATvmCcwSep4a6yzvhaN+5ZTnegFM5yAY+43dBPuY41bbHjyhFLXb6aiiAG7up6qOagdRtUVKAgElIhJUJxBV6oyevv3xwPtULTBSnoSQdvUfr9B7x29uCRe7QhhtQUlPpG8pTChEEH0jAzg5kwfpwP8AVSUpAMCRCUkwe8kiBHt0+v0HE+ChcweZDrtOwtOEN76j1kwR3AjGc/58NCmt7hzw+35I3rhJBOMQIz/t/XZrZYU4/EZkp+hPEwNxsqxqW5k2XWH+y7cl9TUPMnmJzFbfoGdKizosDjSqgKq6qqU8h9tQaSfQ2lLa/WsQoqARJSuOyKLqSJBycj69JB4/OH8HDnvU+Gzx4aHublwrqHTl9rRZL61T1IbYqGalKmWi+D6XENPradjr6PSQTx+iV5a2S42sKStpRQtA6gg5/hnhbuzSJQ48isEJYMHqnRd1UFGTuSoEAzmPtx4VefMUUpWN6oOD/P8AThv84qWQTJ6gz04+CFLnuoZnr+vAtbBqcRcySMkdlDrxsTX7YAUrarBHb7/z4bWG3KhQSlLi1kQQASSOv+/EF8RHia0Z4XuU1+1hq29W2koLFROVRpE1rYq61YBCKZludynXFgIAjBJJgJPGzGF5AbutXEDmVRH+04+J6z6b8Pej+V1NcaZ/VF/vDeoKm3SSuloGGX2m3nABAC33VBAJBJZKoITPHEBxP7zfsQFp/NthX34JHih8R2p/F9z41JzE1Y6h696jqA6ttrFPQspSEM0zQJ9LTTYShPUmJJJUeB082tIAAGcDM/x/hw+0NJ93gEZ59UtzzeI8uXlf7sAH1CZ+o48IQpxQJTEdc8bmqIrKsn0CfofcwOFztjeoassPJ8tSYmFBQAIB6j6EYnvniYAtQeiTUzG3b2kyN2P4nrHCxhlLRCnCpSk9j16dPrP+XGM7W1Ao6yMz/wDt7fT+saHKoIAKd0kelSjJP6Dj1bZAStyrCI3ET7R8oye2ONKVqq3gEpSRPTt+o40NsqqCCsKCTlKT39v8e3DpRMBCJMJEQBjpx2jiLyh1ZWhgx1S+x1KretsoUvcj5FBW1QwYiPafr/hwUOWuqEtrSStIQgAq3kpEE+wPuev/AI4F9MhTywkJMq+k8SCw3E0DqYXtkpx7/wBR269ODtC7wSMpQryajIKuXzGXo7l27ZBpjVStTprLG1W3N1ujLX7OrXEr8ylUlWCUmAVjIkkAdOB6m9LrHj5soITMOboUdw++4YA7Z+3A2suoVLphC3VKCt5ROCRISE4nImOkR1PQF7kbyH1RzkrbizpbTl41NU2ikdudY3RMuKFJTsplx1wpMwJEq6FS8EzAOtr3OGMofR2+KJ2rC6DeDj4RulvEd4JbxzCuWp00NxpmnnGGUbFoY2IBlwnA/rpxzi1/aGLRX1DSw0tNIqAtDgAcKiNuxMndAiSACAqSenE5s3ii1do3l3Vadst3rKSxVwmop26hXluABIVuSD8smNqhJj3BPEFprVV6vr3amq3eUV9VLVK1E4CTJTAnp7HpMnjvBBK7LSckntyU6+Xei8Nojbp081EqyyP6jrFuwkUnslIdLqQVYEjpHeJGJPs/WPQSLczTrISl1YSQkqlaI7gzMxtHWSB7DibWjSDYfU4SXX0GQ4tuFJ+iQYgYjABEe2RJrNpFLSEuuypRKlrEgYAEkxnqR16wr2gMNDw6NQfJzVV3jjA6SyM7LX4ZvDdT86ebumdNXG9WrTLF8q00qq+uhFLR4MFZxJMiOmSn9N3N3kqxonmne9OUNxobxTWGufoW6+lWPw9YGlFPmtLPRJgkdR7ZyXJu2mueKVN7mxuKkbDtg9x39sT7E9+GTX2qGdJ06vJUPOIKEBIKcZzjpicgxAPuYYJbfHTxmR58uPr+aSIrtPVTiKP2j9AkHOnkjqrltoG03q4WC423Tl8So2+vU0UM3HaU7g0uJUd2T9B0iQA9SMftCrbUGg4htaVJQlsALMGClOITJEjrAOc4NvO7x98x/EFyM0ny11Fc2rno/RhAs1L+CQh0CFN7nVp9T0AqQOkD368Bu6PI0vZlmohwIO4Heny1L9XynsSMzB/mTwlySOlcXyDAGfl0VnU8AhjbHGck4+fVK9QcyHbFY2aBrym1g+gh0n09ZicwARiCPb5QYty28RWouS+u6W+aduj9ruiXErQ+w8pDiV7UwoqHdMjIiSRISCRxGLzcF1fnvrS6VPAqCAknYArAwD1KcCcRiAMsValVTVKX+/KFrUv5inaVCAo+lI2gqVIMSpYyY4VL1cXVX4Tt2jkE6WC1spPxWDDjzKM3it+I1za8ZlLbmNf6vu+oKa2ILlM2/Vq2toAhS0JQkx6gApUnEzAI4gPIbwxM8/dMcyLw9rPRmk6rl/ppV/pqO8rLb+oQlYCaWlQgq3vwR6fUEbfUOpEBulUuqpUIQ6vy3WULUkJIIMn8o2mAdwIjJSqCCUkxq4bnHWGtop23tym1yQVJkq3iBCv7uD2TJESFF7Gt9lOoe52NSti60uqs6XGSVPU5MJnDQATKIIEYIPScdo4eqe/oqLNQ1O0NGfKWsSopUI6f3ZHZMkQT78NFpS48hTPlmpgggmSJwIOR1J/XcehJ4+K3W+oXSOKJSXEraWsFPl5jp+VPpEyZxPXji+IBxaV9D2q5amCUFHHkPqiltOoVmsqhTIJ3JKAFyqYg9iTnPbcQB1JO2pta2HUWolm3IrWrW4pCtlQ4HakNSgORgCcqInoMSDJ4pnoy8uN1Bplr3ONyWvmAcA6TkmYk+8k9uCbpvWpNQyipdVJbhveohxaTPzAHpA6yMDIABgzaa7wsRu5LnxLZW1sf3iPnhWYTo2lpa41NsW+7Yn3lPUFXUsJS48yJSArJAUUwCkEpBA691DrbCCUggAOl0grHpGE5Bge3XOCPpwM9Mc3nWbSzSJr/ADKRClbGFrISwV+o7RgAHoSB2mOFl25kOPU61BAcWgSSr0oKhgxIGSRjPsIEwXWOraQCqentrmZaQjto7VmnrRoA21yzuft9Nal5Fx/EENt04RlotQQVlXfrtPcEcSGzU9Nd9pUykJScFO1cqMmQTgARP+WRxWGk5hor2FrDqnVjKSHAU4k+3pyCYHuBjiTaZ5+v2UJQXHGlpABWrcUAexHWMdQDiTExMiKSFx3KDVEE8LC5i6JeCrweaf5v11bVXhpTlDSJT5iEHYt1R6SeoBAnBmBHbiS+PD4fOj7Dyiqr7YqZdGq3gFxrzCUlJOYnoeKyeDj4lyOQ99dcqkC4UVdDdUwp1KVgjoQRgHJwe3fideMX4vFo5v8ALxdisducoaOr9dQ888lSlAdRAwB1mT+o4kupa91wY6F4+77Z3GMdcg9VWUkkzQ4uY81OrynfvtvyxjmufnNfSVwp7BdLBQrUxZ7pXM1ldTCA3VutBYZ3EkBYbLjigBPrIOYECh7lwmnKvU8W4WslLcl8g4UEomRkZPWMkEYJmteblJerk6ufKgglK1AKWnP5Vd+pEiZAwOvEI1RzIapVSlX40pCVp/eQmQQdxz0gRBxAkdchbhRUonc9gG5X0LYrtcTTta8b4UZq9AMstrGxKFIlKwSncAAdogHcmMCMYMe44jGqLZS2fDigysnosBSlEEdhmJJgR7Zxx41ZzLqnEoQ3UtwmFtJY+VB2hKSCD6gAYMenPUHPA3vd3qLsSEqU4D6tqDlafcGCCAfbpGTHC5UzUsR0tCb6c1kgy8rVrC9U6gpDSQuDgtqkdYBlRkqOBgjP8COrxNWsqbb80rUcqyTJ9x1wPc9veOJ0dD1t2dUU0617VbRCTtT8wMrIABmJyfyxnHGxXLIJcPnPNrWRuIEq2n82foc4Hb6cAZp3PPkC3npzp85QXuVgVUICgkyepEGfv279Ppwjt2ng5UhO3G4gbcnt0+33/T3LWo9OMUowkubfSDv+UDp1wP1iJGeGG22eKlISkpgSYMQY6T1H8uMjpnPO6VawRRyZB5J65d6I8xTaxClBSVDPQ9ck/YfwxMcdqPhG+Je+c6OUV405qm7Vd8vej321s3GufU9V1lE/u2eYpXqUWloWgE9UKR145H6Hs7qw2UI8wyFbidpRMRnpAkd+/wDC53wpufdn5B8+69eprixa7Fqu2G3uVzy4pad9LqXW1rVPpbkFG8zt3ZIkx3uVkL6Jxa3LgM+qF1V4pyQ3K6tJSU9R16KAyT0/x4a9f66tnK7QV51NfKlNHZdP0Ttxrn9m4ssto3LUEjJOIAHUqHA85reOTl1yr1HbbAq6r1HqS7+SaO22cIfU8HQC2VPKUhhtKkncNzmE56QeKceOn4vGj+bfhm1loqwaf1Zbr9eA1Qtv1IpnKbamoSt07krMyhsgQFAhXWRPCbR2Ctm0uMZDCcZ5e9RKi8UsZcwSAuA5Ku3j/wDi98w/EZWXqyaSq6nRXLSvpzSGgQhCLncmgQpbj9SkkoKikfu2VAITKSVlU8c+L5TNrfU88sqd3KUSVFTiyeuVGZOcnJ6kk8EnVlPdb+8pbqS2Y3BCpjaREkQTtwfYYJnIBg14tlPbaglxzzFqwkBXb2+v6Dh3jo4qduiMYS6KmSRxc481GXaZTii22ghAVjqCfof58fRZShne4JEjamMAdvr9uHI16W5DKEIn8wP17d+3CKuqgidyj9ACSY65Pt/pxjgOa7brQpCkLGyYSc4AGcRHfhMt0JG1IPpx6TP8+nvxrqazzMJISB7Hr/DhTZLu1Qpr/OoKevTVUymWlOqI/CqUcOpiPUAI9uOWcnAXrnhoyUicdU7BBTkYAEgzxtYodz0ncFHvPT6E8bqelhcJgrOJmRPsP5dOHO327ecgjd1AzxLgpi85KEV1wEY2K0U1HsJTECOh6DHX78KW2ZEyBAyomeNymwlWY9/m4Q1tb+72pkDqRuM/rj69+nBAhsbUFYXzuyVvfuQpyQgmDjr1/wAuv+PCm01ZXUNZ2K3RMYT2M/TIGfY+x4YWlFdTJVAhQJ7Z/o/5cPtlShtLfllCirJlM9ziD0EE/rP3EXxi4qcIGsaiFpZ8W5tDQTUbkr2kj0qwDIQesSRIj78GTkb4j9bcha25VmjNS3XT1fd7cu1V7tE5sU9TOJAcZXIKYJIIMggztjrwDrI8VF5ISIdcRvSckzJHt/eInoSB168ErRNrVd/KcWVOF3BBUpWf+4kkmZA7D6nHDFbmiTAS5dZvBaXZwpFpzT/7Rq4KSloOGG0EKAglIknCgJAwcT1HE+sNgLTrLbIQXm8l2ApM+wJ+u72Gf4J9L21TqErMJBSYCfT+VOfftEdY6RJHE4tVm8gNpPoLchRgREA56Z956Y4tG0WwNbqwqO4hv7nuLCUmt1gSy00qJVEJkSCZ3Awe0R1yY9uHFu1l59SQColUkYCcd56dCPoP8HJdEmip0egyEgbSCCnrjpiD7e/XHCC6XhFgoy+VtoViCTlJyAf1k9epmIyeGcxRxMy/kkHx5ah+iMZJSLWOqaXSenV+tKFuDzFrkAIGRHbB6yDn7cCnmXobUgoLHcLjS+TQaop1VdtW3UJWaxlK1NFxLaSSIWmBIhUpiRu4Rax1XV6xva2WFK2NEhSk5nMGAYwQDkwMEDhsrLoq1Ujeyrc8xoBTSt0hlICjuBAmRP8AAyADHCFebi6qk0tPkCtjhyyR0EWs7yuSMOJtdN5ywpLphotFpSgtAwoZkxIj2xkg8QbU9bVXmC3O1SJTuJUpCAqYEEKB6kqwkCehVl9TUN3q+0tOpYRSpcKEQoJSgAyT0Pqgz3gk9+p7+KRyq5D8oLNoRPJXWdfqw3SzIqL8h4pP4WrMIIV6QUrUSRsztgZPCrXSF0RIOB+qdrcAyoaxw8x+n+VULU9yDtQ4tpDiEOSUBKZiDsO2QSrCU4EEArBIHVpSUs1CXyny22kLWlTQDhlIOE9QrrkmUknMAceKx4PPvFLiHVuqELKvMWgLAUpJgQomTMDMQSkE8aq65KZp3yhXzrSspDigt3bJKgIOEJbBM+qTkkdEuZ+2VYkEeCAm281CWlklJQlhYkFQ3IlJDfqAHoBAkHHzn8ySWNttNS7Spa2pcQ6ASEYUsEJ3ExBImSeme/QrawrFclCylpDywle9ClKbT6UlYGFKnIxOERmJKFa1NtbgyyXKn0KSVFQb3DcCBJSYgFWARtIwZ4FOdlFGq19GULo2Slxhbji1llrchSpGxEQMqjpBJBxKogiRrsabpscT+K8kvKAUYbIhRmVZSOsyJA78RfRN8S7TN07j9RteLcjzAougH+4ImAFQYgncVEgRxbr4fXgvu3jI5u09gs79PSKqWhVuVji1OhoJHqUkmQYJEHJmc+8mVoJVgWG6lrC0nAAVYam1P2dpipdQpryklQIWQnacmRAO0ycf/jjiQ2y6JqqJSAotOpBS4JTCUkQQRIAEHOCOkzPFo/Hx4Eb34O9eKsl8eRcRXNmpYrky4p9BMq64CiQQVKySoRxTK/tvaIvK0ZU15hTvTkoMA9YgRuKgR6v048FMfaarCor7EIQHnIPI9FPLPfaj5FvFK0YeAWFKViRnuYJxHeM9OJBqbVzabxVN2x2tqKMHaz+KWhNTG0TIk5yQB/dH3HA5pb05W+elRAcpWEnadxGAIiSQPcDp8sYE8SO06iU4wW6gJS6WikPEFSXRuSNpzhJV2Ez7HEFoZZGswhFcaSV+chOy9bCpeC1oJUVSkKA2ORtOFJzCZwMJO7oMDh6ptbI1A3RttNUDQp0JbCkKBVVrKioLWZIKlExJ27toG0HPCO3N2q6PIKvLaUuf3bioCjlRIJTIwR0gyTHsH1nQ9luiN4rmm3FwVNpJdKIk/LM7oExOBu7jiSyQu5HdBJqBrTvuEhGrTS0u1K3m/SpCkpTvUUyCkKVHcpmCYkdY4Zr7zGfdfV1DhUCtRQDKwQBlWAUzkgEkDpIw/wBfoS0UTCUftTzNxPpCxKQRlQITtAmE/afoSxXfT9pokEIeYc2wSlgnd0lQBGEgyrEY3cd9VQ4YD1xaKCJwcWclELxq2pUpNOULKEynYYlK49SiCDnb7SQSc5A4jdfeaivIHmKLa1nYtCtqVKBMlSlYBHbEdepPE9XpW1vEJWhLbY9SUxtwFRCVLHuDJ2/MP1H2k0vahUbkOU7jajv85DYEkFMbSogQZEEAmQQMTEF9vmfsXIoziajhadLAhqxZKm7PrKW1FKlpVJ3KkHr82SEzGI+4jh9t+i6ilaUpSEKTO1tRUlKSoJMiCAlUSoz6sj3PE2rnKG0UYCEMhUeW+FhLhBhM5nvlUTiehHEO1DWVl5q3kp3SswtBhITBIiJBIBSSQrHUn341/hMMQydyoc3GT5Dpi2HuX2kt6bxeqa3suGrq33UstNhyBuMJQkrUAkCTGfb9eGXXdur9MXeutdYymnrLW84y8224HEU7qFbVgbRCvUCJJORjoOFFBp119bbaVqcSte5DSBswBknqCSJPUwSJMRMhsPJ1qutyqmsXVoDbIcZKGVKC1ASEzAgkkgkYkdenHpgYwbDCiurKipOS5BW60jr7xSrcAlJCQMewkYg9T/Gfrw46OsbCKtKqtZCSsDYAqSR1iSAIPbrkZHXjZrK21FFWODyQwEuQlsNgLT0wB1GB/nPbhrttmr6twlhsulBASveClAORk4GJzMe3vxzZOyN+ojOFBq7W9zCC7BVibzetKU2j6NVA35dWw0ZbCwJPYZODj37HoCeIxcubv4BhSqVxlhMy0WhCdu5MnfAMSfy9MicZjunOVd1uOz8R5gZZKWlhCQNqiFdFrhKCMdZEicKE8Smm5SN6baTUVrlMisUNohAUpJyZEkDp0k5HU4I4OV96mrJQ6JgYAANvRI1Dw1T2+ItnlLySTlx33OfkOiZGK+46gU6KJh8oX6gYLaEpMYUTlSfUkQY94JEhlvDVPaadT1Q+XXylKUICpeWraJCQMAHtEjBg44ll4ti31hlpC6dBVKVrWlbpXgewAkAdNvzdcwY9dNKsUdMXVu+W7ghRQoOQTkEnpEScgAjv2FzSycnlSYqWJ7vwW7d0M9X3x6qQUNJdQyYKEGEmI646kD7x2zkDm/ocaCifVkhQ/vmepPTJ9z7cE3WtdT2111LJRugFaSfUknOQSBJye+MnJngVX1T1xdUPLIJUQoBIJBHacdgR0/04DSPLnbKbI2OJuCd0z1dyS0kgKBOQVbsf0ONf7Hqaiwm5hKPwfn/hiouDcF7d0FPzEQOsRx9fsrzpUkqWokwYPy/fHGpNuU04ScAyoR269D7Z+/Ggie7oh0tW1oSRthSwYT37YHf+v04X0lt2rJGVHIkEdv44+n+/CiioiYSlIBBgR2/zP68PlBYlKbCyN3ZIGZPtjp1j+PE+npN0Fq7h2SCjoNpSYmehERw6Jb/DsmY3EEgEwTH+PC6jsYp/UspKUpJkyEgAT/D/AB4QXVRW4pKQkkTAIyn+GOh+vQcEyxsbMlA8unkGybLjVlaylJICvQAcDp0PX/zHvwo0fqShsH7T/HWC2ag/aVseoKYVq3EmgeVGyqb2qEutjoFSn3B4TOUS33QI2lQI6fMABIHvj+OI+u2ntvp3JCvKWSn0rk4kn9I9v4Znga8l5RuLTG3CR0dKWfKnaAFASVxI6wYyT+nY8PFrpv3KJOwuKCJ29dwEGfuekZk+/C2zaddqllhhtBUoRAiSoZgHp16nv/LiX0nLattT6nHmKimG/elC1SsddqnI/n0kjHfjpFSvfyC4z10TdnFadH2NTq1FaSGwtKCmSAuUqT3ndEdSoRJAHUA1aD0+XgQsrBGXdxG87v730JIAyTHuOInpDRooyhIbSFBfqGwkkxOCInGOvX9eDVpLUdksXLq4W+pspqLvW1DJpbgH1b6RCCsLR5eQvzCQN3UbcdTw+8PUAZ55Oiq7iy8Of+FD1Sq33NFqAS0hK1IykhQMQmSe5IjvPtjsH2wXRx2uZStzaCrcgIXhIMAkQTBEASf7o6xxBrG887ucdSFvvrG1sNJKnSo9NoJKs/TqQMDiY21//hukNW+pZqCZSVLAQADJJwdyuhj1RuyDMh9irQ0ZJ2VWVVqfISGjfuuufw6/hqcsecHhNpNQ6iYRcbxekuFxwOwbeQSAI7KHUz78ckfGzY2NKc8L7pm31jL1vs9a7T+aj1JWhCoUspAgfKPlESesHiT2rxicwuVOn6+0WjUd4sNK+Ciopm6ksI3QYBBPQpjvMH3jiunMPmE5U3RdSuoZffW6XFKdTA3T7L6gkT9CcjB4WKqaoYZnzzFzXnIB/pHYfkm210EB8GOCANdGCHOByXHueyn+n/CfzB1jyNvet9OaTu1forTJT+1rq2gq8lfpEJGZ2pJMAqCRBJMjiuV81S9cXnEIKoVO5sBKipUmYSM5VECIwT8pjiyXJr4wXNnw7+FPUfJnTjtic0tqtp9p9yspCqvpi6CHShcpELbHqJSqAcGTitVgs6E0z1S+gimQ6FMoIiRvA6j0jPyqAGcwCY4UZK2WokMYGGg7e71Vjw0ENLGJHHLj+foldO83p+nFc6Q+4lKtiJSCmTJndEmZkgZnCRIJgOobs5WPONrceDgBVsSolSlFKkHaMiUz3lXYdZDtrTUQr9zXmJbC0ltkAypcHaencj8wGCe8nhitWlrpq4VdbbqKtrRRMqfrk0TKyzRU4MFwqR8qQoknaNo7kTt4DXOqyfDYdgmC0URaPGkG5SOhWmoDqxKfJRCkyVNrCikGDJAAyrPcTkiOGW73NDtI6gKeU6rYlajJwEK9QJAzComJAChkcLq2qabom0s7CsKGwlG/zITnB9MJO07QDBM4IHEcK2qirbLSQd6wFKcCnCkmcDochPuJPWIgA55PLgJhhZg5K8tVaXqcFwsBxQ29JPpklUGSpJJSQfoYzwnfrWm22UOJUolIbUlJTlJgwVZn0xmB1VkRn29dSEpWhb6kpSGwCqCiSYAPQJnIgY68JH0ht55AdZbCHTsG4EJO4BRmDMdZnHUexglS1Z+mqjaUbypwIUSpKg8tsKCZAAyqPyhGcFKgojcscWQ8Gni71H4U9f2++6dudLRXWlchJC/NSsKSApsj/wBRJkSB7/ljgGNWlTrLW1twJeUtLZSolYUAgByff0wFA5XJMJ+ZvYZrLLtSlDrexRJW2YjcVESYJgbgokzO36JPDHPQOaMrLbeA04CuR4uvG7qzxW67/wCIdZ1TQfQwGGSxLDTTeSNqSNwAJTMiYVPcEA/U1pZ1FTrlTcQpLpMrS0okgEmAVbSCqeg6SIjiMcwOfd65x3Rusv1ciormKVm2pU2whr8O1TJS0hASjb6wlABVgkkrBKscI9L8wm7bdAKh0KQstqcQFD1gKBKFJTvMmQAJOdvckiEZPD2TnS3ESRhp2C9nQ7lFVoVSPq2bFJR6wlZ7AQpXy/XEwDtOYIGgNLPXhva+CpYQnGw7QkErKvUJjI7EAA4kmZHy+uemNbWKrqf20TfKYNCmpfJSoVExvBUkwjan5ZGZEwRPE65Va3tvLXU7NzrrHZdS25lCgbY8tSaWqCmylKiU7T6FKQcADAmQSOJ8Dm+1nK0mkcMlqgOoOXbdjcDDTyUIW4krCVlxJkwEgwkKIJA3AxITt68Ru43Fy2sqVDzLAKgUqUN6IkkSDtKon05mJxOSNcL7RVBIUo7Q6skEgt7lEJ3ZgDdBECZVtOR8zRVaFotRMJS04PLUpxsLYAVtBBMyBkJG7MyANuYHEvwwd2ryOrkaMZQxuWs2kLKU/im2JhYUQorG4xO1RmUyJmevSeGw6wZYfSlSqplQTI3oKNhEnA6kSIJM/oDHE/u/htuaWEPp3uMLJCiCdySZxBlMyoCZG0Z9iYpceSz9AFKWxWMqeKzJUSEAABAidylYJED7Y6do9bNsIHXvmc4kOSKj1tQobOXXlLCVje3lJk42kSfdIAxCczkSHTd/OoKlKGXXnVFKVJKQlPlyc7TKQcqiUxIIz1HDVYOV5VUswyEOpKXN+5aRuJABURtEkJ2pVOd4wOoNHJ/l5bdMUrYddUmoUvchS0klO1JI27uqc9ComBJPXgtbKZtRMIn+UHqlG9V1RSQOmblxHQcymuzcp6nUVG665TVSA8lKqby1EpJk/M4pUpAkxsGTOEkjhwo/C9XlgrqEIQwAhCQ4VJTsG4wCpPp6YA29THQ8GG2a7sui6XznXGFLSkOEIdBKslJgAhWPf2IBgQDDNd+KJNwqagISy8XdywhsJHf1EkZMKBx1iMQDBG822mpX+Gx2pReF75VVkHjTxlh7FR5PI2ktFKX6hbGzzIO9SR2AUIGRCdpI7AHHqE9Ofh6aa8OT/g+vCdX/APCa7sF1Bua7mUoqUtx+72boUAE9CMzMknjlLdOZlfe6tTg8wqUN6FJfK1OKiPV6QQN0GMbQFCPZwtd3fTSjzzVKQYUoEk+mQFbcEyDPQDM/qoVUHiYA2Vhw3EOj0OJHLcc1Fue+i9Pr5lXYWdDtVa231uUyluAJCPMMEgpJJg/ZJH68Ri32UUDi/LRTJcA8kKKQ44PUUlCt2E9VKiM5T2AE3vd7tqHVOvVFKhCnNihuLu0kJ+nqVgSZIjGBwq0NetI3S2X5Vw1NTWJ222pyqt5cpis3qpb2g0yFI+UqCyRuMYIPXjyOiYN3lZdb65zNMXNP3hY5d6Z1Pzf0+xrnUruk9PtvFqovD1MKgUaUI3pV5WT6lBAP5fUJEQVQbmFR2+l1Rdm6K5Lr6ZmuWG6hLSg4tALgQsFIlCSACU7knIGSOIDqXm4urU47vrHN6hMCQ4QjJlUx17FPzI6CIi72ta68PJRSvVIp24Lfl7lLcgRMEjatRxmCIVlREK8lqo2jRFuUuQxSF3i1B2Uu1BrCksTADSUpWlG1IQkFwjduMJBGyTJyT0x2BgGp77W6geS0t3y1Pq2BlB3rRBIOI3bZCsjJj8wnhyt2lap5JePniUpWVEJAgKBz9jsMDbE4gSOHeh5fihUsLStbjhLZO1JXGATBwe0iDBg9N3HkVpqandwwFyq+KqalboYc+5C3/hAV9G2t5wKDilLiNziQZUJBEwSSMxGZj8zbU6SQpRS2hKhugQqEnbkyCM+0GB/Lg1q0Gq4+S08ryt6kjy5UPKBOwKCeqiOwPsY6ji2nh9+B1zB8TfIir19ZmEN28oWumYeqEocrggGQgH5ozHST+o4kzWuOlALygAvctYSGBcy7np4qDiS2dpJCEnsJ+0DH8YM+3EarbEfMWopED5s4n+vpweuZ2hHNDXmpoHabynqVam6gLSIQpKoOf0VPTp2zxDtM6Ho9Q6ytlLeK5Vpt9XUIZqazyS+aJoqhbvlJ9S9o9UdTHvjjnLTBq5MncUPrbZ8oSlKyVAwQY3exMxg8GjkHz61D4ebNrKmsdHYqlGuLG9YLgbjQN1SqdhwgqUwpf/ScBAheD34ilTbbbZ7nUIo3/wAcy08tLNSoBsPpCvSoggwNoBg9N4HGmofcq1qaBUlskBIIMKkCAR1VP0/w47RRNaMqJM9z3YTNdkOOqUC6hKimTKjIIEgGYjPvgduGty1CQlQDhWnfsg5T1BGe8AicfwB4kX7Dfq9oSHBuSSEmVAnInGTkHqInEcOlLoV1sphEKW6pCTnKZ7jGP1zH6caGmfIchdBWxwjBKgqLPtBU4kGSApK0EoSJggRjt2z/AIcOFHYgppBQkqIkSBAGYAgTkwc9ZBAAjiY0OhgKlpKw4qFbwABvT6pKiMSraMDvuxmOHG1acYolFSkLSUgKQoHaE7e5mB7dTH0njvDbTncKNPdgRscqQ+GZdu5ac0rFeb1bG7tbrZXNVVbQOK2IqmkupK0KMwQtMJmeihxcv4uXi/5YeNzmfpS6ctNH/wDC1NaLb+Gq3HKRqlXULUQoBSWzG1tIKQr/AE4plpehbu1XTNLUmmYWr1vJRv8AJABlQSYO0TkSCMntl5FWGGypltG0jY1CQpSjIIMdTJBzkkkxjbwepaOEFr3ZyP3ySxWVU79QaR5v0/fxUw5SafsdVrixUF3vKLHb6+sZpqq4lpTrdqZcdCVulIyoNo9UgmUgjHaUc6OWFg0Vzz1JYtHaiRrXTVtqVtW29tNbGri3tBKwAduCFCRg7ZE44GdDSppW3Ha0wlKUjy43z/8AEyJ9HzHtgAdONrWp6ispSzQn8FQEhK1KIH4lUwIT3j5gEiQYMgxwabKWbM7cv1KXXUeo6nHOTz/QKcV17t2hmkJQ4hypebP735incBCW0pCcT2AG4pncEkAsdw1Mupfcq64OM7UoV5O2EgfMoiIOTJATgE4joYpUXxizredSFv1LeHXFwAVBJAnOMmf06cNPMjmDZXdF0VLRpqG9RNVjrdXUIfSqlcaVCadDKQNylT5ilqIgEpiAJ4jTVohGuU7qZTW505EcIwDzPVbde8zn7hV1C0uBThlW70kJjad2O8GDGY+44gztS7dSqpdD61OKAS0JK90DaBnHUTImDPc8Rw3tC3TUrUhakIWEhbwKwrrIgdBn8xAmJnPF3vge+GnRXio8ZFksvMCqpkWQtVB/CuOhCnnEJBbZCiBlZmTGYMRPCvU3UzAud0TjS2ZtOGtZzKqTZtGOVDwqKoFTSE7w33fCgr1pBPqSSAJwOwzE/NVPvGiQltva03IASIUCqMjdJk9AfqImAB1w+Pf4GOU/hWpNJ1Whaduy193LgqremoKw2hMBLmTuAVJEf9v345Ya2oWG6FLrSw3uQdqkp8vYAARmTA749jHaJFFC2WlMse2QodXVmKtbDJvgoTVlCp1Zd3tsMuOOJSVStG6VAkiUgSMgq29Mg9OJ54dPHHr/AMIlt17TaDuVtoUcyNPuaXvyn6NNQ5UUbiVFwMqXJS4QpSt6SQNwiYBA+1RWtU9xdbQ+2ryX/MDiD5beEkbkKE+mCSInuT3mKVVxcS3uKVkJSlpxJTLaiVZIPQgbgAlXcjuSAlVbhqIVi0bSWgrTVVSVtJSGkObdq0ArKwpCUhQEDpuEGTklMCIJGtuxu1d3dt1CuouhL5ZZ/DMEOVCSIKdplad2PSATgkgTBQ1z6U7vKBKyPJTKxuV+UAQSZOSe24xPfh/5M84NQeHrmhY9aaYqzaNRafqzW2+saOKR4IKUrAUkjaRIgzuJ7dOBxKIAKPsuOW+6OI2JbcCvKIU0FhCyTIUnJ+UEJ25Bjpw3u1RaaR6i2W0oASViU5MSB1gT1yMdwTwoutTUXq6OVDzrztU+tTjriQVKU6pQK5KcZKiSR2P24SfiF1oZ85SkturK1An05hMwcAnAn2A6wZ0Kz1XT3x9+B+7/AA/ufC9B3i+W+9LRQs1Ka+lSoSl1EbFGNycJI9WAAekiAdXpbr6Mo8unUtpIHlpaKEtBRkJSMnpJABgwDKtxHD4ddVevWU1V3qX6p14FtbtQ+HnSoN7Q2A5J2gHA3TCSokbEgtVfTpTWhLTjJp9wH4cLAQ6CsBaACTIkpJ69IAJE8WDE7U3D90rVFOY362bBRS5MOUoJQ8EUyFbVIUJlM4BHqgQUwYGR3JMIKy4ipS8yVKS/6/JKCoBBUZIM4iSQCCFSJkpB4k9PTKeo0pab85lCSpaXQkkwJCcYACRMAYBUenDfUaVUXgFNoUVuKaKUO5CVeiEk+lUkYxB2me5Aiut59toRi3XQtIa8poqb7UtqU6HFOFsJcQqJgJQAApBkbSUCEkhJ3AJTEw40XNG50byqeqXvSkHakqV5iwYSoJXtMEwCZ9MA9zw1CzPOpaQW1IUhzalQktnamSd0GVbjG4iMx1HpS/sU+WoBThp2l7mkqUk7TJKk5BAXG0bT1gkiIhf8KRvspmiuDe6k55x1LzjPlvVCCpSQk7pKFFJhZJkiAE9twkQZHD3p/n+7RvB92oWghRCm0oLi0K/LshQCSAIzBhGI6AXP2MIBCP3LmUBQchvOIJEYMmEiTkGIBlN+x1lzY4hKVESjzDKNpAEzMT9gAM5kYz71LHyUqORsmDlWd0V4ylaatpaefBYXgjaE7IHUyYCZ2gT7kdJPDoeeln1PUOLcqXApa9iwl1ptCyBISteYylcqnG38wA4CV48NGttHcsqHWNVa7hbtOXFXlIqmWFFDK0kHMqyodj0//KeID+9aSChbYcbIhPmrSUjACdxBkxnEYSTI2xxJjus4ADlu9oI55CvTyncteu01q3rmxTtopws7lqQREH0oT+ZJJJhMgnrIkxrUevk2CqXT0qm0KYX5aVIaUreJQBB6boiZmeh6EGplr1VXCrCqd8tIcT/1QA2kpKoRtCSJEpUTnqZJ6wutdZXXBKDv/dqhspMrUuT8oKiQkkqJntkDrgrFepS3DBuhVXDTkZcOSOt45oU1Y8lTtZU1K94UttpQndIEiJO6SSQBMkwesxuu5p01O2tLBR5iUStJCYQdwweyDJSZwqScSeItTaWdvDLanAypLY3O1BJcSNqOoBJAGDnr6QCT1Hi4cunUKaR5LLK0FSAkErcaPTZuIBWqYmIOFEyAeMlqqlx1HdQ2eByZhOV45xVyKZxJcNOlO8k7FHCSd0JMCTJM5BLsCD0YrxzZr6h5QVU53BtsqJI3xhKBPqTAgbfSO27B4dnfC1qWm5dta1ftdYxpsXAWxNzCUltqt27koJPyqEqJgCQCR8oAhzWji04lNHTEu+UtBUoEqTtmVKxIAAVtEYKR6sZjGeY7LdzGtGcpS3rKqutXDZeUtwBPr9RUqD3TgmffqQYMHj5+KqrygtNuKdStahtQk5B9iqRHo/L7xuE8OVl5cVVwec8xUlW9S2RkdYTu2EFQgHCj+WYJIkh6K5NUvmoNQ6K1xJCyhO4oKQBAEAST1BIBH96eJ9FZqqreNkGuN+paNmXHdQrS2jm7ptqAyp9zeHSt9BAAEQcSYEgQFQSTAjgjWTQYpaBK1JbJWhMQkBpRIG70gZJBgACDAIz0L/KDkhp/Utm1M9XaksWnntN2Z2606LhUFtV2W0oJFKykAfvlQQBP5cTmB9cOY9FTIBp0NvLd3ALWRCxnAUDJ9KQCEnqSI4daGz0dICZCNQ/VVpcOIq2uOmAYaUpoNLCjdDiQ2Gqckh95SUhCRISRgkExEEjAAjvw7aeotFq05flXTVSbLW2m3Kq7ewihW4LvUhaQKZKgSEFQUsgr6BJmOnA3vGsay8kuuv7Q2gLQpRAbWvegle0TIkKzIUQmOs8MNUlKyhRadeJAUHcpDaYPqQkndGTJPXAIzB3nrmnaMYXCmtrtnVLs+ieKzVKqi4FSEtBQUtKoSPUYn0jBkwnJyYVIwAb6eFT432rPDP4alaBatdnrkU9O4igfckLogo+ojbAWmVAgDIM9ccc5F3sUrCg2xSMlTkKCmgVISEqKjBVA6xCifpA6OXKHSi+b/M6wWCqvFLZaW81rFE5X1rxbapm3nAkKWs/KhJUZk9jkxPASsLZR+MM4R+mzDnwjhe+b/MVjUeqrlXuFl6prqlx5SgkDbkkuSrO0kkwBIkZE4YdN8jtVc1ba9XWyzVVwpGE+a8aZpag2M/N3iBGR24JfxHvB/Q+BbxIVmhrdq21a1pKWmZqkVlEUjaVoK/JUEqgKEdAYIInPF6Pgj/Fb5R+Dbkhq7T+v6ByjuNwqE1TD7FKahVchLYQWFEiYkSJ/vHvwNqpHOGYm6s4RODS04kOFycv+n16ZuQp3kKdqW0pGxKSBuIg7Se5xJEAmdoAgcJKO2ru1UhsNEpcWCEIAUpeBEbesx0j6nJwUvFPru2c8ef2oNR2W3G02+63B55mjaAKadtTpKUo2kDoY2nAM+5ATaT0ApTqXHVpNQ6opASkLIgABUYgkd5z+s8EKC3vmeGgIJc7tHTtLiQpd4ZvDVWc5dW26z2+mFXX3F9FPTtoTAqFLMgfQDPTp17Rxfnm38AXWfKjlK7qPfQXD8HT+fVUtLhTQgFRAjMZmOvfgOfD95jU3h2506Y1JXMB6ms9Wl19JysJMhX2I3GO478deufHxVuU6eRFyftt7buVyudCtqnoW0HzCpSYhQ7DPDNWUdwo3wR0dPra/2jjPX6bdVWtPdbfX/en1tWY3MHkAOM7fXfovzx8wtIv2S6+TsKvKWUKlMBJzmT0Oc5MfXA4i9DpOpu1YEpaWjyyQtcFKUAfLIPc9P4ZweD/zBoWL9qh6qO1G98vKKsATJEDsAJz37e3EbvVytWnrK4EIUrYFbpwlZKSDuI75Eye0z6eC9RZACXyENaotu4kcWNijaXOKg9LokUTLUK8pttsqQt0wlSgQSe5JEiCDORJmeEVXdqS1vw1tdcKlNFwNlSAgETtAJUT2+p9xI4W6juFw1Ql5Sj+FpEggNghGZCJBGQIWemOmZnaz195pbKk+Ult1xKlOIVI3LEiJVHSEnA/7pJJkhZjE04ZyHVNVO2Vzcy7nsP1WxNmNaymruZUimIKW0AfMTMIxgYMnMxuJ6EjRW3ldzqHktKShptMvHyyskZ9IPUAZBznvPThhcvbt7fW4VhIKStb64KWkwJSPr1BzjiL655koprYKWl3oo2kFRQVDJ7E46n0iDI6AlJE8RJayGFhceX1KmxW6eolDBufoP9pXrLWdNbWjToCkra2oXvPrWSCBtI9x9gBkkdRBa6sqa4oeqAGE7fM8wKiIAgj2EKmB6vVkAAwjrKx78Q8p9SkrQVl0mQSZBKZx1UQJPUkSIAPG6lt4tri3Nhc/DuqbKXUh3zFgHaYxE5IBONoKpHCDX3B9TMTyCsy22qKkhxjLuqdqOiCHVObS0mnDTTqAlXpAIykHqkekCQJMDHeXaa5qVvL64U1Ta3naaqQUvMqaUptwGEhOxSTKiAMiYwcxgD126ob8kqhKVPqc9RCggSFKVuIABKSTMdgCO/H2kqg3TF55xlRXueLJUd7YETAIiNpAA7dZSN3G9POGclzqKXxOYRh1hz71DzJqUVupL3X3N0qS0l+sqlvLkpxC1yI6GBGCDgSOBrrrmA/W1DiGtwLSf3ZCCQIPuAYE5yIxI3do9e9Rfgmi048ErUCVo3StQBhRKpPZSSI/VOI4i1Zc6httaAlbKlpkpCQ2CoiJIkZxIPaE53CeNq2+O8IwxnmuVDYImzCd45Jdcn3rhUPpLakBLa1OiFGUneohSVKjckAHMRBMZVLReFr/ABLjKXijzVo8zcd/ykwdwJOSATEjIHYHgweA/Tug9eeLHQdn5o3den+XlyurVNeaxDiYZYKVbVKgEJBVsTChCdyjETNqv7RP4a/Dj4bOcWkKPkBe6CtZu1p82/UduuouVHblBSUtOBxKjtccQDuQqRASY+UBaOT5imgYBAC5x1G5x5RcVKTlaZICTgEkKEKyDj6gdIHHSDwh+BDwwc2PhNc0+Y+seZJsPNSw1FS1arZ+PaS6HGm9zLYpzKny+Soen5ds8c5326r8SmrSy8lCyIWBtTuT6iEGACQDgAYCUnthE/dnkUzO6EhnaiVD1FMkgAg5BxMZxOO+rTg5Wzhnkpbr7mBbdcaO0nbqfS1utN4sjD7Vxu1E8pTt9StaVM+Y38jamW5SCkkqCgSeEnPTS1i0DzJuVq03q2k1xp+kDCqa90lvco26vzGmnFANPBLiFtrUplU9S0YJTxEFn8QraooCm4bG31JjPTMDoAD0j9Txo9T0ubdnZXqyepjrnH+A9+MJysyrsczfEvaOcHMav1DatHaf0Ja7jSMUyrPaajyaVry2EoW4CtSzvcHqcELkqA2y4Bw4ou72pqNbVS4relDrqA4yE7nV7IKVhJVuCl/KpKgMbVEE7a6aW1Ahyrp3SpQRUtlCoQUNGBBQCDjC/USTPpySsgE+vtSNGaT0veXLtpm4q1JRPOop6GtS9V24NOqaWKxmUeS47s3hoqUFhZODkscFW7n2UJ7G8ip7bLax+3aQVLiGwry1Ph353uikrKcbpTtMHMe3Bg1Fyz0zedLKdRUodUtMHykIHRCQobzhQAMEkHrMQSDXzT9+WpgslldQlL/lOB5twqB2j0yCCdwXjPzKRABnbOKjm4bly8t2naez0VA7YXqmoVXMU5FbWodDY2PSY2t7DAMBJdUCZJh0snEVPA18U0evUPkkfiPhuoqpI5aaYx6TnA6+iIvOnwCcxOSfLDT2t9WaVq7dpjWIQmjuKsl1WxSkJVA3pCh6hv2lXq9QngTVegEVIUp5a3nIUttJQklwHqorBBiSB+bpH5uCx4h/iR82PEp4fdJ8uNS3+gqdL6R2LoVt04Q44lCNjbjqmgoubWVKCYSkggmM8Aml1BcLCkofSpKvMPqQ5LYViTMztGBuB2wrHvwPgNO/aZmN+i71JqogDE75pxqOUzyWQGfMZ2JTiVgGSem7cP8AtJAA7yTB400XK1yjeSUISg7/ADAjeQCBE+n2lAKsCRuwDHE95Aau0TqHU71HzCut80/Zk0FSW6m3UYq3V1nlqNO2tE/9NTiUoUfyzMpT6uG2jvpqnEOPKbYcSsKKkJMqJI/MkCYIjqJ6j34kNs1HLks9FBHEddD7aInMrxe61154TLPylrW0N6YstZ+JZWlKXDG6QnHSDt6K7bsDHFdajlgKdCXVIDOQWlOjZ5UZ2gbTkkyOvQAk9OCFW1i0KS22ttalpOTASkSCoqgSAEiCOvXAxw3UGoaCxakp66so2qyjYeQ5UMqUsofQIUpO/sCAUkyFRmSRPEaexQMy7CIU/FVTJ5cqMUmjNjKFlaghKgpSA4dqVknaQFerqBMAnIEz0lul9Aodd2rbSmG0ndMg9BEd8AnBBkBXRIgicw9RNeLfxDX1/lLyzqrLT1AcraTTdtLlW7RsMU8vrxBUJDqzEABXzTgOPh61JYk3FX49sJWhxKFbVbiIGAmIBSEwAPvOOk6yWqCombANie6HcR8RVFFTvqXAuDeg5plodIooqR5RKw6zMJU4CVbk9ciCogpSDuT1SACY4ba6wuVbnUFS1QFMoCd53JgknI/LBRlI6nqeC1zJvFjav77lndFRSqCgkqZ2K2QEpXGIVJmZMT78Rmr1S21TLh9xaCVJVtAAdG443dDI6iTIjIM8H6nhtjHlpPJLdBxxJJE1wB3781B1aVvFz08baaypRbVLFT+DL6gwl4D/AKgRO3zClJzM/N177aDkq0w6pS3VKbSopU4kJkCIVG8JEqAmIIjcO44fKzWdCl4JWoguQ4oU5IU3KfmGJPXoTjaDBOSyXbUNRdg6UMU6AILinlFtpsp9KgqfUScnsRuVEZ4iG2U0Z5ZKmniWslHtYCckUdnsjSE/MtKgQ0D5ra0gnBmMED7iSmTPDbeuZaUUv4WnpVAiQVuulanTtMkZGCE9OhIEqGOGt2203nbqm4gtok7W/SYxClKx0ke84k9uNC9Z2jTrS26SnogYkIEOEHAAkjpP92AYBnIjsC5o2OgIbNUCR4JaXu+iT3mvvWsStbqXVNbhmPQ0DnuAJCY/KN0caG9KKSy47VVakectSpEEOgHHX1KIG3MSYUJMbuGi981Kisd3NNhtIJ3VDxJISZEJQI9IAkTkQfbhLqyx6itOkbPqCrdZVb9Qh80ZYqWlv7miG3C41uLjY3KTAWhO5MlMgSB01RTt5kuKIwwVbwAAGD6p5uNVTUdSVIaUQJhdSvZB3dSSSRInIJMHBE4YL3qAKWtJdCHQsNKQlA2LV/dM4lWMEHOfdPDPT0z90c8xwvKQg+YlO+VqH1OYMdDAJOI6J4dKbRRQlC1+YzTwdqXG43JEhR6QTux9SJI7cRw58v8ALauznxQfzXZKa13AMuIUhourKlQVLEuJCjncRtAOe3QTk8am6qrr/MCFKebUoqcUBO8kzJHyicH69568ShvS9tpadYJW69O1aHQAhgHaUGAI/vEg4iPm4V1KGrXTpKklTu1C/LCTAB+UlShJmTnJg/fjYUJzl68fdWYwwKM1Gj6hawalCgNkpLiyklEY6diM47YzMl0suiXq4LlBQ0mCpayUBXQ4yEnJGZzEAGOJHY7Ebw75r6EBCyklJgKcEzBIMzgZxOegJ4mFo0+0H3F1I2q3iCvcFSTAMYkmT0jr24K0llD3ZIwPVL9z4j8JuAd/T9FHNJ8tUpZBUgBxKtylLJBQTM4jHvhMwD0kcEiz0NDpy3pS6yhbjigpZBA2pzJ+oyM46TnoH/RnJ+5aq5aas1LbBQVFs0Wyy7cQ7UtNPoQ6vYkpbJCljcIJSDtj7HgXXjUKHEEVD4bpwobokFZIzmDIPt1jMcNFP4NID4YyRsfRItQ+ouTsOJ0/mpQNXVFSSGUFKSsALSoIC4zHvP8A3fSCOnE35Cc+9OcteZ7FdqjRrevbKhh5ldncqlU6X1qbKUuFaQSCgkKg9oMjB4DaKev1KyNjAo6FtJ8x4pgARClDsrp3Kj6pMdvtRqe36etqkMrWt11vzHSuApQCyEpVgERBJAI3bs9xxCqb2NDmHJz6kfUbhFaPhUPcHEAY9M/PKfrle6u8PbWB5QSvcYWkpBzAKp9xBI9u8kcRO+XujsxK11FHV1TcBkBY8hoA/UErJHsOgSSep4jGseb9RVOJpKVQNIQAIbW0l5sFSZ2rKlAjPXcTOZMAQ+vrCismoKnKhTaZUtsDaT0ABEgQSQJzEnsCs1l4mndlyd7fw/T0rQ1v/tPV/wBf1lY44lsuLWAStSVGDEAkGCTnbg4x2nEZW+usfacfLjhXKdrhBSoASIyBsAnqQOo77uNdK6h5QK0hpICU7lrCUpQSYPtjBJIkwB7wjuFf57bhDaPIQrzl78hcLgFQ+aDunqRJABgmA8tTpGuQpjp6MuIZGEmump3a8ww4UsFQKwkkAp3EeoiNxicDunIAJPEVHlqblAWlZaClbUNkNgpPcknJwcA/NOYHC66ViHLgoy3UshxI3rbWTUtJ/IITuSFETJ27SoCAUcM1UVuLaZQSp9W2nQC0loOLBUFLBCgEeooBJEQTKsAlQr690rtinW325kDduaVOsihaCnUrZQEqWUrBKS3sSUqAUTBVuc9RTHyx88hHX3NxRcpw2ZbXKEJRKUOGUyNpAwUExEQ2MQI4SVr6nN623AG3CopLbYKXCAolyPYSlIPcJkiAY1vLUKJ5e90tpIQBEpG5BAWkKTJK5UoElJwkEdJGiXCI6M7JYh5TCg88pxPkblpb8zzFJVu3+YU4KmiASNpyUJz04SXS6O07boSahJaP76HE7Wdx2qSpSTCjCkgHPUpPRQKC5VqXANxSkJCH/wDo4dkJ3Ix1CSRAxuCZx3bbm+l6pUt9aklSNiFJWHFEDYCd0dQEnviAYHfwzu6LBCMp0oLZW3x5VPQMVVatphbpaoWFvOIS0hS1OKCc7EphaiISkbj2KeGRTrTTbLhbS4pSDKSUFtQBKj83ypmT0g7o6Agyrlhzu1RyNvNzuej75WadrLza6vT9Y5TqG6ooKtHl1DCpGQ62SVGJGIAUBxDK15KEtqa/d7AXQVJgxO1PXB2AACIwD7ccMldgMJVR3B2ibDoUlgtKMlELQDGSqAPTBSIknr7gceaqpaq1uLDyVBLhg7yd0jr2OcSoAElPQmeEVe8tK1rG6Hwpzes/NBIEKAz0juCQT7cfKja1WLCiFlFQoJJAEwCJKuv6TGCep48WKa3fxAapv/Ja08tnryv/AIO07d6q/wBBb1tso/DV1Uhtp51LoHmHclCRtUspGyREq4YtIctr1r+k1BUWWyV17p9OW1d2ujlHSKWi10aVoaVUPFKf3bYU42lSyAAVYIJkMjZkZS2D+Yx8qRGenYDMZMx7jj7b71WW2iqksVD9MiuQaZ4NOKbTUtSFKaUUwCkHaSk+nCcSAeMXmegW+0WOr1FWoYpqZysqXHEBLbTe8vAdCAnqPYAdBGSDCW+WOssFc5S1yV0dUw8ULQ6Cktmckp/rp9eJFR1N95K6xttexUs092pm6e5UjrLiXsOJStsSCU7gDlJEgjj7q6/ap8S3My9XutS9fNS3RL10r1ssoQVJbRvdXAACdqETjsDkk8YsIX//2Q==" />
These are GGUF quantized versions of [Envoid/Fish-8x7B](https://huggingface.co/Envoid/Fish-8x7B).
The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using `wiki.train.raw`.
The IQ2_XXS and IQ2_XS versions are compatible with llama.cpp, version `147b17a` or later. The IQ3_XXS requires version `f4d7e54` or later.
Some model files above 50GB are split into smaller files. To concatenate them, use the `cat` command (on Windows, use PowerShell): `cat foo-Q6_K.gguf.* > foo-Q6_K.gguf` | {"language": ["en"], "license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences"]} | null | Artefact2/Fish-8x7B-GGUF | [
"gguf",
"not-for-all-audiences",
"en",
"license:cc-by-nc-4.0",
"region:us"
] | 2024-02-08T18:23:39+00:00 | [] | [
"en"
] | TAGS
#gguf #not-for-all-audiences #en #license-cc-by-nc-4.0 #region-us
|
<img 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3NvZnQgU0NEIENsYWltYW50cyBSU0EgQ0EwggIiMA0GCSqGSIb3DQEBAQUAA4ICDwAwggIKAoICAQDXJeLb8443bNoExaat+SmT0DUBXhFfqUaWfR6k7SCD6AEWd5EAq0w3Ue+rrNM0kanwhMIOjG41cUIdInoLdbgaHVBeBccEvAJNgir6KJpZo/H8VqNbFi+cQ9h4JLGZHNGFAKZ/rGaQ4kSzZ+zecVwYl9Z0w+ipTO/B412lryhgfZhaJdbifP89TEAaEqE1LDUVMPlISMjvDPoHyA6i/CMw0L+QB0u+ziGT5H7dhbBUXWf7vN369iZS0+Dxt7FfXowzHmnNG5ll9wZinCP6EUyVDdBX3iZMHYCa66kKQMICuyplN2jmRfjy0kV3nGVJC/sUvV262AdgZ1t914LzPeAsrCQRQid5M2aIc6wv/fX24tjor+OVpHxrqraMglsxWXQPuOui1x8HmMaeMLB/TOaHVc4iMrVLikoE9wcYGb0V9mnCpBFaD0S9/pzo6sx9xpl3g1O1L0WU0kIx/SxdnMQ9epxGDtJcjxCgZHHM56MdlmclOybk2DQO4cOO8lp0Dj+FfK2eZ7Bd+t/ZXC0C1RgIGKK3kWotVyv0nUtDPXOu0vwTcd/ckeOtRuBjgLHXPjJM/7rTXF2BN7HnwOtPIG5yniRaxfOUvrtmOfhOO61cWvylcMLNaYnHzwTsbtL2sFxIOV0gSWGJAlbmv2FQSgWXK1cWohnLQiktiGWBNVX1aQIDAQABo4IBjjCCAYowDgYDVR0PAQH/BAQDAgGGMBAGCSsGAQQBgjcVAQQDAgEAMB0GA1UdDgQWBBSLrZr8j3XNzg2Naa18TKRgVtm0RDARBgNVHSAECjAIMAYGBFUdIAAwGQYJKwYBBAGCNxQCBAweCgBTAHUAYgBDAEEwDwYDVR0TAQH/BAUwAwEB/zAfBgNVHSMEGDAWgBQLs2g7r9qv7nCldtkh98xEFgfQ+DBsBgNVHR8EZTBjMGGgX6BdhltodHRwOi8vd3d3Lm1pY3Jvc29mdC5jb20vcGtpb3BzL2NybC9NaWNyb3NvZnQlMjBTdXBwbHklMjBDaGFpbiUyMFJTQSUyMFJvb3QlMjBDQSUyMDIwMjIuY3JsMHkGCCsGAQUFBwEBBG0wazBpBggrBgEFBQcwAoZdaHR0cDovL3d3dy5taWNyb3NvZnQuY29tL3BraW9wcy9jZXJ0cy9NaWNyb3NvZnQlMjBTdXBwbHklMjBDaGFpbiUyMFJTQSUyMFJvb3QlMjBDQSUyMDIwMjIuY3J0MA0GCSqGSIb3DQEBDAUAA4ICAQBpxEcsFAQ9oJ+nX90jxdm0zaRrpzJVb7sSue3McYCfUjf3mnVTtU/AsWpQAWg6C/z65YH0AEeBV8qlDb+QoGDWjAc5Q9JKHzoIdJxJA07Q0GwJHAA8mPCkG9OJDMATcdXHe5Rr3gyWFWADZfcBEPdExeaQrAlXycwDlRtYpI86tjoqzb/9OKs4hl8FfK8kjm+9XeWCdiwlmPBMl6GdH/otPRkHzxtWuFv2ZPfVsIzsA04/QwhmUmY8OrCeKMTD4rY4aOrmgjR7MghQRfDoDNAueUDs5yYVdfkb5z6u3kpXPP5H/AsGrY5U3hvjmTQjGKvqc9vaSTsb9tHXk5g+6EQRK+OC8UE5K/lE+bhBiExQTCfwJeWgahGxTeXy807rrE3KZUg4j80lnaMt0DNbMSxhlPF9zLzMK8edCPctFwKvfWMOoE9mTf9giYJ3V2g45mQKOZfk93VcUkcNazLd+iiUzFlYTB8NLmu4Sc1Lqgr507Wtip3UEANCuVZCt/KyK3xupM40vubUyWHk3QxwPwaXy5/3kGxDtKzy7hVTCc5ILiHBnNQyvjiNYU8zOt2Fs/JkIMpPy7sqAvurpoGsSxv/0od0ns4p3Zg2ZIskuaI66ccB/6qLeAp1AAwTbV7lOgtiqhl9vKS9CyFLH4Sd44M+ZiGKGlunx26nOprkYnZSxVkFszCCBa8wggOXoAMCAQICEGgo1Ux+XNq9QzmuDMFaKjUwDQYJKoZIhvcNAQEMBQAwXzELMAkGA1UEBhMCVVMxHjAcBgNVBAoTFU1pY3Jvc29mdCBDb3Jwb3JhdGlvbjEwMC4GA1UEAxMnTWljcm9zb2Z0IFN1cHBseSBDaGFpbiBSU0EgUm9vdCBDQSAyMDIyMB4XDTIyMDIxNzAwMTIzNloXDTQ3MDIxNzAwMjEwOVowXzELMAkGA1UEBhMCVVMxHjAcBgNVBAoTFU1pY3Jvc29mdCBDb3Jwb3JhdGlvbjEwMC4GA1UEAxMnTWljcm9zb2Z0IFN1cHBseSBDaGFpbiBSU0EgUm9vdCBDQSAyMDIyMIICIjANBgkqhkiG9w0BAQEFAAOCAg8AMIICCgKCAgEAniUBZhkfZDTBnTkjYh1xi1bqJdKbH+8nAYK/d9iUM4MYSJtQnnuWZMLQw19F/zKc6BcXvXjtdZhfOgYIKxw3m0ZKkAqwr0aSPjOJKvq45zJj8yPHbtIU+yZY7v4GhFT6wR83qtvU7FYqv0m9zOsC7cZO/KwZtRI1aRWJF02jaOpsHimaCfPOeiHGCdEZ6o8wRmk7aAQrfIot1mNd6m3WOZ69Bj5b7i8RWyhrp1KkaF5MpOquziO/TDZx2oFFUI7Khs7/U8O4Q7Mk7gd6orT6xwode8ZSNTHsCB+EgJJb+LHaOdbJ5+WJBH5Rf/TmamRHSer47Kb2oENT/trDIyTYJdoTLCq3P5TedxxMeBxq+ZqP62oVd3etSYTOEEDHmUgP1ZYegJxzoTihA2/TTSDQtUPk9y54D073vL9l2m2QC1u/3uonJ5lk+Dl8cz3WIdLu1vNTES5Vw9zq8SlX3lGheHOQCy/1yXU2643SbY55XboaOP/fGQGo0sjR1vLrivUu0cyTE5uckHhlY3kExPGen4w682QM/pgdk+KPVqVjUyO4bnMWRRq293sPzaQy/1r+lo3hh3jbcIOoJIVpIMJtEg3lefYqWc/Wq+eB5qCxiC0IjAuxz9dsNq+e+QNn2UFzqatFuHFgWBjUFixlutEF3pLFUBARkM5HzPuvvyPAnwUCAwEAAaNnMGUwDgYDVR0PAQH/BAQDAgGGMA8GA1UdEwEB/wQFMAMBAf8wHQYDVR0OBBYEFAuzaDuv2q/ucKV22SH3zEQWB9D4MBAGCSsGAQQBgjcVAQQDAgEAMBEGA1UdIAQKMAgwBgYEVR0gADANBgkqhkiG9w0BAQwFAAOCAgEASMc3///BaFfXi0NmRjomay/o+t5ooY9H8T00lXraVTH0ldI4Xyy6j6WNUTFqiVVobCtCZGqFJKBRA8fd0XJY7WwejNiRxedJEZ0ZejdYHndE+8IImELETeObig7PQEVPG4BwWYyTgegP1cgmlan3H3cGuMrvnPvoZtrlOeDS0mLDp9S2GJonmyZQSnKX1bNbKqT9Xy9+5mKjJ2YM+tkZzBEdMagBUgjmVAyZYyvq2ITUtAgW775KW4hY9AYWoOt6XeHHRNa7L1VWJfCeDOQPEtvAf69WXcaJDnGpVhLkuZyoZB61R5WSrtBwyJN9fFpY8QXxSrhschiprh9XmSZ0ZvUdD99d8Oc3W1+68LTv5GMHfh8yGGmpcFqS+XmcWNR+v3JdU0YrbqOZYNaFjGZ3Fnav4sUYW+JdCDbWZjcXZfAuz6HlvOaNDWW0VlNdn8ivTm5Rz4i+kuow+yzndT9CYMRx55efc8efytG4bCPqUCgdDkPM9akbQOummOXlD8WSL6WWx9f6PBjuHRthA/2G5yRBM73Y87ZgfPMcggPVYK/f9CCk5IEGIlrMhTN9ZPjkuL+AF9T7IT9jruePtxdE7HIuNckL0IEd6XIDCUHZ3wlI5s23shxgJRlS8z0SSe2dlCKOcSj4wQdUc904CLSFjxRsqgCvQKu1h862OVxz+ZBmc2lnVHN0oWl0c3RUb2tlbnOBoWN2YWxZFzQwghcwMAMCAQAwghcnBgkqhkiG9w0BBwKgghcYMIIXFAIBAzEPMA0GCWCGSAFlAwQCAQUAMHcGCyqGSIb3DQEJEAEEoGgEZjBkAgEBBglghkgBhv1sBwEwMTANBglghkgBZQMEAgEFAAQgtbgZ8oIWmS5XENkuY1VBNvFYGj1GMTUpNuIyT0vMtCACECZn80XjVP6AQSxxVKEpiNkYDzIwMjQwMjA4MDgxNDMxWqCCEwkwggbCMIIEqqADAgECAhAFRK/zlJ0IOaa/2z9f5WEWMA0GCSqGSIb3DQEBCwUAMGMxCzAJBgNVBAYTAlVTMRcwFQYDVQQKEw5EaWdpQ2VydCwgSW5jLjE7MDkGA1UEAxMyRGlnaUNlcnQgVHJ1c3RlZCBHNCBSU0E0MDk2IFNIQTI1NiBUaW1lU3RhbXBpbmcgQ0EwHhcNMjMwNzE0MDAwMDAwWhcNMzQxMDEzMjM1OTU5WjBIMQswCQYDVQQGEwJVUzEXMBUGA1UEChMORGlnaUNlcnQsIEluYy4xIDAeBgNVBAMTF0RpZ2lDZXJ0IFRpbWVzdGFtcCAyMDIzMIICIjANBgkqhkiG9w0BAQEFAAOCAg8AMIICCgKCAgEAo1NFhx2DjlusPlSzI+DPn9fl0uddoQ4J3C9Io5d6OyqcZ9xiFVjBqZMRp82qsmrdECmKHmJjadNYnDVxvzqX65RQjxwg6seaOy+WZuNp52n+W8PWKyAcwZeUtKVQgfLPywemMGjKg0La/H8JJJSkghraarrYO8pd3hkYhftF6g1hbJ3+cV7EBpo88MUueQ8bZlLjyNY+X9pD04T10Mf2SC1eRXWWdf7dEKEbg8G45lKVtUfXeCk5a+B4WZfjRCtK1ZXO7wgX6oJkTf8j48qG7rSkIWRw69XloNpjsy7pBe6q9iT1HbybHLK3X9/w7nZ9MZllR1WdSiQvrCuXvp/k/XtzPjLuUjT71Lvr1KAsNJvj3m5kGQc3AZEPHLVRzapMZoOIaGK7vEEbeBlt5NkP4FhB+9ixLOFRr7StFQYU6mIIE9NpHnxkTZ0P387RXoyqq1AVybPKvNfEO2hEo6U7Qv1zfe7dCv95NBB+plwKWEwAPoVpdceDZNZ1zY8SdlalJPrXxGshuugfNJgvOuprAbD3+yqG7HtSOKmYCaFxsmxxrz64b5bV4RAT/mFHCoz+8LbH1cfebCTwv0KCyqBxPZySkwS0aXAnDU+3tTbRyV8IpHCj7ArxES5k4MsiK8rxKBMhSVF+BmbTO77665E42FEHypS34lCh8zrTioPLQHsCAwEAAaOCAYswggGHMA4GA1UdDwEB/wQEAwIHgDAMBgNVHRMBAf8EAjAAMBYGA1UdJQEB/wQMMAoGCCsGAQUFBwMIMCAGA1UdIAQZMBcwCAYGZ4EMAQQCMAsGCWCGSAGG/WwHATAfBgNVHSMEGDAWgBS6FtltTYUvcyl2mi91jGogj57IbzAdBgNVHQ4EFgQUpbbvE+fvzdBkodVWqWUxo97V40kwWgYDVR0fBFMwUTBPoE2gS4ZJaHR0cDovL2NybDMuZGlnaWNlcnQuY29tL0RpZ2lDZXJ0VHJ1c3RlZEc0UlNBNDA5NlNIQTI1NlRpbWVTdGFtcGluZ0NBLmNybDCBkAYIKwYBBQUHAQEEgYMwgYAwJAYIKwYBBQUHMAGGGGh0dHA6Ly9vY3NwLmRpZ2ljZXJ0LmNvbTBYBggrBgEFBQcwAoZMaHR0cDovL2NhY2VydHMuZGlnaWNlcnQuY29tL0RpZ2lDZXJ0VHJ1c3RlZEc0UlNBNDA5NlNIQTI1NlRpbWVTdGFtcGluZ0NBLmNydDANBgkqhkiG9w0BAQsFAAOCAgEAgRrW3qCptZgXvHCNT4o8aJzYJf/LLOTN6l0ikuyMIgKpuM+AqNnn48XtJoKKcS8Y3U623mzX4WCcK+3tPUiOuGu6fF29wmE3aEl3o+uQqhLXJ4Xzjh6S2sJAOJ9dyKAuJXglnSoFeoQpmLZXeY/bJlYrsPOnvTcM2Jh2T1a5UsK2nTipgedtQVyMadG5K8TGe8+c+njikxp2oml101DkRBK+IA2eqUTQ+OVJdwhaIcW0z5iVGlS6ubzBaRm6zxbygzc0brBBJt3eWpdPM43UjXd9dUWhpVgmagNF3tlQtVCMr1a9TMXhRsUo063nQwBw3syYnhmJA+rUkTfvTVLzyWAhxFZH7doRS4wyw4jmWOK22z75X7BC1o/jF5HRqsBV44a/rCcsQdCaM0qoNtS5cpZ+l3k4SF/Kwtw9Mt911jZnWon49qfH5U81PAC9vpwqbHkB3NpE5jreODsHXjlY9HxzMVWggBHLFAx+rrz+pOt5Zapo1iLKO+uagjVXKBbLafIymrLS2Dq4sUaGa7oX/cR3bBVsrquvczroSUa31X/MtjjA2Owc9bahuEMs305MfR5ocMB3CtQC4Fxguyj/OOVSWtasFyIjTvTs0xf7UGv/B3cfcZdEQcm4RtNsMnxYL2dHZeUbc7aZ+WssBkbvQR7w8F/g29mtkIBEr4AQQYowggauMIIElqADAgECAhAHNje3JFR82Ees/ShmKl5bMA0GCSqGSIb3DQEBCwUAMGIxCzAJBgNVBAYTAlVTMRUwEwYDVQQKEwxEaWdpQ2VydCBJbmMxGTAXBgNVBAsTEHd3dy5kaWdpY2VydC5jb20xITAfBgNVBAMTGERpZ2lDZXJ0IFRydXN0ZWQgUm9vdCBHNDAeFw0yMjAzMjMwMDAwMDBaFw0zNzAzMjIyMzU5NTlaMGMxCzAJBgNVBAYTAlVTMRcwFQYDVQQKEw5EaWdpQ2VydCwgSW5jLjE7MDkGA1UEAxMyRGlnaUNlcnQgVHJ1c3RlZCBHNCBSU0E0MDk2IFNIQTI1NiBUaW1lU3RhbXBpbmcgQ0EwggIiMA0GCSqGSIb3DQEBAQUAA4ICDwAwggIKAoICAQDGhjUGSbPBPXJJUVXHJQPE8pE3qZdRodbSg9GeTKJtoLDMg/la9hGhRBVCX6SI82j6ffOciQt/nR+eDzMfUBMLJnOWbfhXqAJ9/UO0hNoR8XOxs+4rgISKIhjf69o9xBd/qxkrPkLcZ47qUT3w1lbU5ygt69OxtXXnHwZljZQp09nsad/ZkIdGAHvbREGJ3HxqV3rwN3mfXazL6IRktFLydkf3YYMZ3V+0VAshaG43IbtArF+y3kp9zvU5EmfvDqVjbOSmxR3NNg1c1eYbqMFkdECnwHLFuk4fsbVYTXn+149zk6wsOeKlSNbwsDETqVcplicu9Yemj052FVUmcJgmf6AaRyBD40NjgHt1biclkJg6OBGz9vae5jtb7IHeIhTZgirHkr+g3uM+onP65x9abJTyUpURK1h0QCirc0PO30qhHGs4xSnzyqqWc0Jon7ZGs506o9UD4L/wojzKQtwYSH8UNM/STKvvmz3+DrhkKvp1KCRB7UK/BZxmSVJQ9FHzNklNiyDSLFc1eSuo80VgvCONWPfcYd6T/jnA+bIwpUzX6ZhKWD7TA4j+s4/TXkt2ElGTyYwMO1uKIqjBJgj5FBASA31fI7tk42PgpuE+9sJ0sj8eCXbsq11GdeJgo1gJASgADoRU7s7pXcheMBK9Rp6103a50g5rmQzSM7TNsQIDAQABo4IBXTCCAVkwEgYDVR0TAQH/BAgwBgEB/wIBADAdBgNVHQ4EFgQUuhbZbU2FL3MpdpovdYxqII+eyG8wHwYDVR0jBBgwFoAU7NfjgtJxXWRM3y5nP+e6mK4cD08wDgYDVR0PAQH/BAQDAgGGMBMGA1UdJQQMMAoGCCsGAQUFBwMIMHcGCCsGAQUFBwEBBGswaTAkBggrBgEFBQcwAYYYaHR0cDovL29jc3AuZGlnaWNlcnQuY29tMEEGCCsGAQUFBzAChjVodHRwOi8vY2FjZXJ0cy5kaWdpY2VydC5jb20vRGlnaUNlcnRUcnVzdGVkUm9vdEc0LmNydDBDBgNVHR8EPDA6MDigNqA0hjJodHRwOi8vY3JsMy5kaWdpY2VydC5jb20vRGlnaUNlcnRUcnVzdGVkUm9vdEc0LmNybDAgBgNVHSAEGTAXMAgGBmeBDAEEAjALBglghkgBhv1sBwEwDQYJKoZIhvcNAQELBQADggIBAH1ZjsCTtm+YqUQiAX5m1tghQuGwGC4QTRPPMFPOvxj7x1Bd4ksp+3CKDaopafxpwc8dB+k+YMjYC+VcW9dth/qEICU0MWfNthKWb8RQTGIdDAiCqBa9qVbPFXONASIlzpVpP0d3+3J0FNf/q0+KLHqrhc1DX+1gtqpPkWaeLJ7giqzl/Yy8ZCaHbJK9nXzQcAp876i8dU+6WvepELJd6f8oVInw1YpxdmXazPByoyP6wCeCRK6ZJxurJB4mwbfeKuv2nrF5mYGjVoarCkXJ38SNoOeY+/umnXKvxMfBwWpx2cYTgAnEtp/Nh4cku0+jSbl3ZpHxcpzpSwJSpzd+k1OsOx0ISQ+UzTl63f8lY5knLD0/a6fxZsNBzU+2QJshIUDQtxMkzdwdeDrknq3lNHGS1yZr5Dhzq6YBT70/O3itTK37xJV77QpfMzmHQXh6OOmc4d0j/R0o08f56PGYX/sr2H7yRp11LB4nLCbbbxV7HhmLNriT1ObyF5lZynDwN7+YAN8gFk8n+2BnFqFmut1VwDophrCYoCvtlUG3OtUVmDG0YgkPCr2B2RP+v6TR81fZvAT6gt4y3wSJ8ADNXcL50CN/AAvkdgIm2fBldkKmKYcJRyvmfxqkhQ/8mJb2VVQrH4D6wPIOK+XW+6kvRBVK5xMOHds3OBqhK/bt1nz8MIIFjTCCBHWgAwIBAgIQDpsYjvnQLefv21DiCEAYWjANBgkqhkiG9w0BAQwFADBlMQswCQYDVQQGEwJVUzEVMBMGA1UEChMMRGlnaUNlcnQgSW5jMRkwFwYDVQQLExB3d3cuZGlnaWNlcnQuY29tMSQwIgYDVQQDExtEaWdpQ2VydCBBc3N1cmVkIElEIFJvb3QgQ0EwHhcNMjIwODAxMDAwMDAwWhcNMzExMTA5MjM1OTU5WjBiMQswCQYDVQQGEwJVUzEVMBMGA1UEChMMRGlnaUNlcnQgSW5jMRkwFwYDVQQLExB3d3cuZGlnaWNlcnQuY29tMSEwHwYDVQQDExhEaWdpQ2VydCBUcnVzdGVkIFJvb3QgRzQwggIiMA0GCSqGSIb3DQEBAQUAA4ICDwAwggIKAoICAQC/5pBzaN675F1KPDAiMGkz7MKnJS7JIT3yithZwuEppz1Yq3aaza57G4QNxDAf8xukOBbrVsaXbR2rsnnyyhHS5F/WBTxSD1Ifxp4VpX6+n6lXFllVcq9ok3DCsrp1mWpzMpTREEQQLt+C8weE5nQ7bXHiLQwb7iDVySAdYyktzuxeTsiT+CFhmzTrBcZe7FsavOvJz82sNEBfsXpm7nfISKhmV1efVFiODCu3T6cw2Vbuyntd463JT17lNecxy9qTXtyOj4DatpGYQJB5w3jHtrHEtWoYOAMQjdjUN6QuBX2I9YI+EJFwq1WCQTLX2wRzKm6RAXwhTNS8rhsDdV14Ztk6MUSaM0C/CNdaSaTC5qmgZ92kJ7yhTzm1EVgX9yRcRo9k98FpiHaYdj1ZXUJ2h4mXaXpI8OCiEhtmmnTK3kse5w5jrubU75KSOp493ADkRSWJtppEGSt+wJS00mFt6zPZxd9LBADMfRyVw4/3IbKyEbe7f/LVjHAsQWCqsWMYRJUadmJ+9oCw++hkpjPRiQfhvbfmQ6QYuKZ3AeEPlAwhHbJUKSWJbOUOUlFHdL4mrLZBdd56rF+NP8m800ERElvlEFDrMcXKchYiCd98THU/Y+whX8QgUWtvsauGi0/C1kVfnSD8oR7FwI+isX4KJpn15GkvmB0t9dmpsh3lGwIDAQABo4IBOjCCATYwDwYDVR0TAQH/BAUwAwEB/zAdBgNVHQ4EFgQU7NfjgtJxXWRM3y5nP+e6mK4cD08wHwYDVR0jBBgwFoAUReuir/SSy4IxLVGLp6chnfNtyA8wDgYDVR0PAQH/BAQDAgGGMHkGCCsGAQUFBwEBBG0wazAkBggrBgEFBQcwAYYYaHR0cDovL29jc3AuZGlnaWNlcnQuY29tMEMGCCsGAQUFBzAChjdodHRwOi8vY2FjZXJ0cy5kaWdpY2VydC5jb20vRGlnaUNlcnRBc3N1cmVkSURSb290Q0EuY3J0MEUGA1UdHwQ+MDwwOqA4oDaGNGh0dHA6Ly9jcmwzLmRpZ2ljZXJ0LmNvbS9EaWdpQ2VydEFzc3VyZWRJRFJvb3RDQS5jcmwwEQYDVR0gBAowCDAGBgRVHSAAMA0GCSqGSIb3DQEBDAUAA4IBAQBwoL9DXFXnOF+go3QbPbYW1/e/Vwe9mqyhhyzshV6pGrsi+IcaaVQi7aSId229GhT0E0p6Ly23OO/0/4C5+KH38nLeJLxSA8hO0Cre+i1Wz/n096wwepqLsl7Uz9FDRJtDIeuWcqFItJnLnU+nBgMTdydE1Od/6Fmo8L8vC6bp8jQ87PcDx4eo0kxAGTVGamlUsLihVo7spNU96LHc/RzY9HdaXFSMb++hUD38dglohJ9vytsgjTVgHAIDyyCwrFigDkBjxZgiwbJZ9VVrzyerbHbObyMt9H5xaiNrIv8SuFQtJ37YOtnwtoeW/VvRXKwYw02fc7cBqZ9Xql4o4rmUMYIDdjCCA3ICAQEwdzBjMQswCQYDVQQGEwJVUzEXMBUGA1UEChMORGlnaUNlcnQsIEluYy4xOzA5BgNVBAMTMkRpZ2lDZXJ0IFRydXN0ZWQgRzQgUlNBNDA5NiBTSEEyNTYgVGltZVN0YW1waW5nIENBAhAFRK/zlJ0IOaa/2z9f5WEWMA0GCWCGSAFlAwQCAQUAoIHRMBoGCSqGSIb3DQEJAzENBgsqhkiG9w0BCRABBDAcBgkqhkiG9w0BCQUxDxcNMjQwMjA4MDgxNDMxWjArBgsqhkiG9w0BCRACDDEcMBowGDAWBBRm8CsywsLJD4JdzqqKycZPGZzPQDAvBgkqhkiG9w0BCQQxIgQgtjbcUw8JQ+4BJRYa3i6d02c3ponVYOJR1O+/tvEoOcIwNwYLKoZIhvcNAQkQAi8xKDAmMCQwIgQg0vbkbe10IszR1EBXaEE2b4KK2lWarjMWr00amtQMeCgwDQYJKoZIhvcNAQEBBQAEggIASj+0yT/ruWTV+BtWc7aN6dgh5DBlH8jj8pAijzf8u23eMA8nWRDXHCHEa+VMg9yq+mBSeO8dGY9y7T4CJc3C3BLG9FPrMv0a9h8QAoFBqtGak5HrkZ85VTUUbmDpjWdVBtuiY5Y6Rj979Gm0jJGr7HNtFL/kXBy1C0kmP1iKMsymeF/+0oZw1Cgj3wY+JBqdTiDj8sCyDe2bPLCUl9LOMoVOPzRWjVK8jiZGKty5RbnirqLzXtfu9dk82EkFZyvTVOXg3lJ8kyqBfpgmB7m8TM/RemZ4cHwpaiKP6pB3FjQvzC1DPsiD1N+ngD+Zw2PCRWPTaIJ9VLZkFQA/CepE6WV9qiz7lhzfXK+bG/HC737MZrwDvK2wSF6m0Cb+dRDfBVfleX0FCRKcEa8s/qqstFDCG/gm7Mzrsdns4Mw/0SKVqN1BvX9JJlOqhlzlNahdg9heLB8U4uxnvbasHNcVauiLqAU3GG9l/NkZyWXd14qaYpvNLMpg/6KIs6MxSTttNg1EMgA7ir9cv1zsdA2gyJAlf0SIMjESFEA3/di4RJ512rzQ4sCeEiu0mDW1zUgU7WSjki4QvkWpNnnYASQ6SShPr2n6azwe3rtWE12pTFyY84t+6/0aeIjjTc+0xDos491Kx8R8WqZG/Emc74+QN7s+hRmEXD9txmXz2GQbqDr2WQGAUq8+ncyKpuNTAm/GKfZBOpMH8QVsBUuEstuKIB+vUps3glqhtZ99pu3/Pxq78wHQ6/zZBxEV26sgrZ1QQ4zMAwL79Ch/1eAcIMsN6dJjz3iRprWj+yOkT6wruMU00BJvQTYRsefPJsz0p62rbYBwMWmNWwdvRj2S1+uT8E6VeZRy6NwPABXIga5yuYOK7xephG5Y+LmXTzB7JZVXVuTRmsf2kUHuOmSn6SB0US5YMsEkUxO70HHh+aWbLEALx7ZbuDdPLaflymzkB5EFEQR5BpPLPNABhJRPXalbscliue9zkwH+GkrdwZUbLSj6784QHizhcbjrN83RyBBKnD2cXcXFHtwcpU5TZH0om9JBoUG29oimvDklsR7pfxazB1dePj0lhgrPGkMOscXZMloQtxXYUXrujm2+TDSEayNN2ML/FuduuyA5xNXuzk1/hXk26bB3pjZI45eRJZsGKAHmp6l+m025WywniuyzIC6L3iEMBpLXoF8PKv9uU4OAHKKJ/+EARkV4aWYAAE1NACoAAAAIAAQBEgADAAAAAQABAABREAABAAAAAQEAAABREQAEAAAAAQAAAABREgAEAAAAAQAAAAAAAAAA/9sAQwACAQECAQECAgICAgICAgMFAwMDAwMGBAQDBQcGBwcHBgcHCAkLCQgICggHBwoNCgoLDAwMDAcJDg8NDA4LDAwM/9sAQwECAgIDAwMGAwMGDAgHCAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwM/8AAEQgBgAGAAwEiAAIRAQMRAf/EAB4AAAEEAwEBAQAAAAAAAAAAAAcEBQYIAAMJAgEK/8QAShAAAQMDAgQEBAMFBgUDAwIHAQIDEQQFIQYSAAcxQQgTIlEJMmFxFEKBFSORofAKUrHB0eEWJDNi8UNygjRjkheyGCUmNXOTov/EAB0BAAICAwEBAQAAAAAAAAAAAAUGBAcAAgMBCAn/xAA8EQABAwMCBAMFBgYBBAMAAAABAAIDBAUREiEGMUFREyJhMnGBkaEHFCOxwfAVM0JS0eFyFiRi8TRDgv/aAAwDAQACEQMRAD8A4H09NtZlaQWyRJ/KMT26cb0tq2oWN6QpJInGAJx3mD/LjzCwyNoIjoZwSIMR0nJMHhUwz+JlCEqUFTuSJ2wOhM4EGf5A8YvcLUyFNvKQoNpbLZThJ2mMds5xMxnrjjammV+8CUpCQFBRSsbSQDiZzGMA/wCPChmhIdb2lbRcbITkjaFHdux+UDMf6yLbVfwrrjbfhXNeKI670a7bam+GzJ08p8i4pIeDI+U7VObklflFMlG1fSeMG5ws2HNVIWyEMbEuEhtAQJVIbThSs4GZJkZxPSSVFutNTd7ghNI0p2qqCUttNq3uKJIThIzmQBJA6kA9tNW0k1bxQgK80TOTtE5MgSBmegwIjJ4lvh+54Xnw7859Ia5sKbeu/aPudPdKJq4MJqKdT7KgQhxCyApJMbhjqciOPCt1FqqiNnfdZqEqp3UOLQpK0H0LkDaQQNpAKusdDgTx5bbKU72kuJQopSFNq2CMgAnp1AOc578TjxDeIC++J/nxqnmFqk0SdQ6zrnblcG6GnTTsB50ZKGx8qIAx1J6knJhbCvN8tbgcPlkDdv6AiDJESIImft9OPVim3LPlzo2/aR1pVag1kzpi6WO0Jq7BQrtjtWdRV34hCBSJWjDCvLWpe907BsIycCGsraQ5tB3ISZCUyhKgkzujoTG7qMAHjxT0riqcpHnOBI3uKbTvAgnJIOIKhnHQdONrLKW9ydrZbQN4I3FOU9szmInBET9eMWYPNamWi+ny0oWFYwFbiI6CTO5JI3YmJEYMcOFDaFqrEoBaSdqgSJM/3s98T8pO3qCOPlv/ABDlS5TtpO5agNskFw4BTAMHvJOYERmOLH+KjxBVHjk1fo5Vj5caR5dI0zpqk085T6cpvLTdXGpH4l0CNzy5jaJOACVEDjXBcdLdypcMIx4smzRzQKoBQ2J5MNF6sc9IQhCZKjgJ2wQkTJEeomJAnglctOVGtOeV/boqChqairq2lQxRslyrqPLSVqXgblbUBSiTGASRHSVWfwqXDl3oBOpbtY7oxa6x1dC3cH6Yhj8UgJK2ypQCdwCkyO24GAFZQp17qOgq1P2S5XDTLCWnGjU0rymXglSdriSUkH1IJlMDCyOnzT/uIhAdU8+y2huclQDHQnDRzPT5pAzqjT/LTSt9tLdgtN8vt2NOmmu9Qp41VpdbUpbpp9iocU7OwlY9KUgpAUTxD3bDWXVhutvtQaOmQmWmHCAsY2wEgbUKJR7SYJMYlydqqHSr6gyyHKlTeHHEhbpJ6EJMmTP0iJ7glirkVV/qvNW6tJWYC1EzI6wR1HUykfrkDiO+WR5wB8OgUiOlhaNUjs/mT6LRdtUt20KZtqPKUpBJf+eqWDiCuJiPykRj6cR9GmlXSqU68A6+4ZWpaip1RJIlUmIx1JOD2McTOl0s1SHaUbAoztgAoIiMAwf/AJGehM9ndm2/hmYQhunQUEBWwbtogyTAHY5wcdiJ4FVdcyLYnJ9E12fheesIeW6GevNRG16FYoktufg2kK3BKlurjHulMdMHIj29jw70FgpwMArDacwkJCgATugTkZkn+MmOJDT6eLiSt8EiRKSQQn2UojGBmJ7/AK8bBTBK0bFNLJO6CQkHtMGJJgZAHf2wDnrHyeisi3cOUtJggZPc/omlmjW69KtjCQQcqIASSenWIGfaB1OOFNNRIUryxuU6QQVJEg5AGSMA5k/6cL6WhFepCZdrJXtQE7giCQBhIk5MCTOffh6p9Opo2UpuNQWkLI2UrABcBIyITIMjvk4MweIfiNbuSj7IZHZAGE1ULL7lak06lB0kLbLa9uwSBuDnpxkRtBycA8PFJpR2qQaisecWVwrc4grWfTgJ6AAAGCT1AxBI4kto0VWvUwXT0LdspSQhSn8uL6dJOQr2GBnAzDsnQrpfQlbqnnFnJdcJ3SSYjtuxI2g9sHjk6qc7ZuyltpYmfzDkqNt21q3FMBE/KVbt7y1A4BBMDM9CMZgjr7fcVUNpT+9d2gJMqJQM/NMACD1AjH/u4m9HyyLeahUpWUpIJLSGup9SiBJjMyQN2ZiQ40OlKFhgLdq2UuFtUBJLmw9d3sSZJyokkGJnjeKn1nLyskr44hpjCHtBYqmt2bKbaFdCdoySJHQ+oAGDtMEAz1hxouV9W8w2XXvMDZKClAhsH+6CSZ79ATjBzAnzVEyHHW7XQV9UtRUQ6GcmB1hUA9Ae4wJ6QfSNFXevXL7KGGkEgh55C9mdoAAmFmQJBAwczEmaeKhj3eclBZ66skOIxgKFv8urdblDzqlKXEAqaS49ucyTO4JQYA7hRn5faApGmbYwhfk2x+rLajsWphxTgUEypJKyUJAETMkbjIBMif0HL0opAk1Tz3REsJ8tKTtMoMCZkZj2PcyF9Dy1pnVpKmGycICFQSgAQlJgEgmRCQCPlPUmStPU0PJoQ6aluJ8xchqiGnNtNZaJtDw3JHnh1J2gGAYyn1HoY6SZzx8coa95tUsU7AdSFjctYWiQJ+VO8iCTKpAiBPY3WXkzdK8rNsoapllaipZaSQtwjAlUQR6R0Ix0ESOHK2+EK8XVlLtS1VO9AFLISASmPnMEGAQRHuAevBinbSybtG6GTC4M2yFW1+g8+RuqXGnipxJp2ylTm2SFDIUqRtkmEiB1kDhHU2pCH0rKa1pPVC1BIKyDG6JiSSOpPVX1ItNcPC01p1pa6tt/yvNBkVKVlZ6JnIlUScdYGO/EduPh+t1zcWilcDq/Una2tCVpEDCQkgAR1Ix1gSTJVlsiePZQievrIz5noAptX4Yuf8yd4CoKXd8wCrfKMKCYEFUDG6IIl0p6N1LDxAZVsQNgKpS3khZhRB9QM9hMkCeJ5qvk87YVLSrzXElKCsIdCWUlO0JJ+Ug5UIOcmY3cQOvVU6ceQXFuqidrqfQRClQoYkHGY9OJkxxxko3xHHRdYKqCbyzDdPNPVqZRsU2PKb8xfypSQkSreoLMBICspIO7ESrI9uimXTz5LjwS1sb8xJQreoEpSVAKkzuEJSnaVAJJJALSzqNl5lrz0hQQYS4y2VrRHdRmCcGCVKJkfYfBdKF6rU1TlttZAStRJC1n3BBMH6ghOBAIIPHPxnMGFL/hdPIOhC83+gFtJVS4QnclaPPQrcADsBTgTuVJUTuAkbk44Yfx7bdQlBU++wg7UbylCRG5KFAidhUmO4kySolW3h6dubbwSA40HGR09OFgq7iAB0ESSTmdoHEa1JRLQ2tTZSph1K1gqTvcCgDtVHclJ7jpIGJ3eGoaeaXrnYXwjXFuFovGkUB1yoYD29LimlNEq3IWVmSd6CrJIMmSIUQUgAG1/wAMf4hWhfB9S64puYnLyk1kvUFoVQUTrjLSnGnJWT8yYCF7huUnMgnuriptnuiC35Lqwwy635jZHq2gCAdzmFAzORuhEBU44W3SxLvCC+2pzzwkjCyoMkkqGVZBEA5MDeDAMgcWz6HZSbW2tk7CCN1IblcGrzcal5tX4Yu1DikNNna3TFREIiYCgCB0O336kSzlhW6KRZ9S0OrtNXG4uXO1OMWh2jqvJbt1VvTDziNqi42Etx5ZOBJyBgTW6/uUbbjdQ46H2lek7ilVQkH5ljChAIVGDKwQDuJ4mFoLFzQW14cQnaosqS2VkoSYiRPpkykCdpAJzw50NS2pb5TuqxudI6ifh/JMWvdPNsXRuo/fho5b2LEgESQUwNqe/VOUjpBmO1LJ8ko/+oS6oICSna4tQgjESidwBKTKj6RuiOCbW0jyGXaZ5FQp1n0hbidqXNgJS2qBiDIwABJMKk8RW425lFMC5tQiSFLgIM7SgkkpPp2tgZE5BABniNX0RwcIlZ7i1zgCU58tuVeqdW6Hvt8syGa+i0q2h+rXtQ4Utrc8uQlRB2iVDbIBUYUDuIDaKug1bZlvNFtxLHoqKZx1LikM99ywUlaIWkqXBJUCkAAyN7ddX6fttfSU1RV0tFXiKqnTvK9iin0FI6hJk7VAdZyDJg1nuT2ndR1IBYUyVB1TTjW5CkFRGO8jPqSesAH1AcLUjSOafGVDRgKrLISrZucSoKwB0BJAEfQfX6Hrji9fwIdLeHHVfi6LHiVuFHS6V/ZrrtuFc+ult7tcgphFQ42QUNbCrbkJKh168Und05WW610VbU09S3RVwWlh9Tag06pBG8BREKiUzGRjHGuhqPKRBUVoJCUysJ3IJ6KAk5n+BPvBFjnugQ3GFYf4n1n5QaY8cWtKXkRV/j+WdPXE2uoQ4txtYLY8wNqVClNpcKwg4JSBnM8AWludU20GEVLrlO1K1Nq9KFEp2pUUkgExPqIBgAdJ49WOxV+qq+noaSlqK2teO1qlYZUt1z/tCEg7iCCekE5zGElTRqp3nGVtIbfaWUKCkjr0IBxI/iAOnWePXHJyF61uBgr5gKVkDYZ2kj5ZJk9zETJA6jOQOPrjCFLSt5JdOyTuUPlMGDjKiD1x3zGePqArytwTMynCgZBmf1JAInHv24+fgy75yk+ajcduVEAKSEqVJIJwIzgDB6Y41Wy3lSENrRLYKN0ygnqACD+UnAgED5hMjj40ve6y+SqW1eYpRj0ztEmczg+o/SPpJeZ/J7UvI/WYsGq7FW6evKqZm4fgK5sNuhipbD7S9g6JU0tKk9ilU4HRBoi101/1HaaGquNLaWq+qZpl1j6VLZoEKWlCqhSW5UpttOTtBnarBPHhKwKZjxCUo8LCOWStF6TU9/xN/wASJ1R+HJvoQGC3+BDswaaVBZTAO8AnrxB6ChqLg80huUIQPLS4tMGN24dP0jp0mRB4k+uuX9FovmRe7RZ79R6os1srnKaivdOwunYvLKFEIqW23QHUoWmFBK8gKEgzwusNkSEpW4lxxpEQFhUKOIgD3PTsCYg540LnOOGopTUTD55dgvGjdCN1q22UIacClpbUBg5k7U4gCAVdD8pGCRJq0RdRyuVSVlpeeoq2mM079M2WngQqQUg53JiAobYGIn1JH9Jf02WnCaZpt2rCJ3YDKMgqkDA+sYjp9fNbqI0b3n1S3HnypSkJdXjptATIKoPsE9EmIyQaoZWU27Bl/foFBuERqvLL5YxyHUokXnmbqLmJam9Ou3O6V1pVU/imbOxufRU1akBHm+WSf3isJmNygAPYAX6t1LV01SaYU7tArcoN0oSUPMhJAIUoiQN0CEgeoK7weFegucGqeXvMOx6q03cqqxXnTtY3W2usYBbXQPNxtcQgzuKTHzDb3z0OrVGprnrHVFxvl7rai73661DlZWVLh/e1Dq1lS1rKY+ZaiSBiT9OINwuMbDrkOXH9/BE7HYKms8lOzTGOvRM9tsq3ypSm0sJUtSy4lUBJkEK9UdAJgndIntw60lKzSOhbaVuLlJUsY9P3MRJjH2/TVS0j1SpMoKvLMBAAhIk4wDEQegxP1PDwlhq3HzKoh12EkoC5UjBypR6CPoTjHQErNVcppfw2HS399Vatn4XoqH8WUeI/5/IL1QW0oZlKPw7LYMuBO51Ak9AqMn2xMdT2d9M2Fq73qjomBRsvXKoTRoeuDvkU7bqykSt5e0IRMkqIAGJMcRS56pLxLTQDhSTASkhIVkyAclWCZVnqRHb5QWisury5CnlLELgSEzJgnpBkjJzkfcW8MbvzTfTySvO2B2A/VPN8ffp7pUUhLVU7SPLaUttaVs7kqIJSoDaoYJkdeuZMprTb03l5CFioqVgE+RTNlWCcndnp75gfTPEn0nydqbwFOPtF1TKd/lIEhInBMfljvAHTMxM6tnLgtN+Q2hnytqVLRO0EFQOUIKirJ7qHePbjmY3lusjA9f8AakGpiiOjVv8AM/RRK0aYcUPLWGaZLYJUinT5iwDG4FZhLc4mMn744mWmtJmjUTR0rKEPN7vxMALB2/KXFSpSvqmB6fbiVW3QKqOm3PpYbT5nloCUh56okY2IHpBIHaevYZMoouVVyvAUC63Z6dZSUPVKw/UuQSYS2J2CFkDcZABH14FTV9DFvI7V7uXzXV332UYiGkeu5+X+VC3GQwz5wWCXEpQp0J8pG/cYlSyVHviY7YBgKba1VVq1U9tQupWiGgtgOBtBkR6sGOgxgkiAdsCdL03ovlwr8RdK43KtUYUKypK1Ff5gltA9MkCE4PywcCd45mvVVOpuwWGrca/6ZWG00rY6BJCjJIj6ie2M8QH3USkCBmG/L811ZRPYCZ35P77KKUHLGrrktqqm6pUFCYa/doaMCSColWYEQB8w6Azw9UHLmnpdw20TCgU5SDUFZBg+o4nBzJgwAeNdwvl+q0OKcTbaZskifMWsogTG4qCR1j5sSMRPDLc7hdKyWxXklxZCvJSC0nEk7SnpEH0jogGQJI7Nkc/YkD4rGtib6/BTb9mW2gC0KrKYkD0hcgge23APQASZ9RgYke6essbK2wblRgJSoN7ErMGJGf1UcbYgduIExpOuqXUOVT5JCSvy1O7IHRRUEdc4+/c9OFNPpOqbP7tVKzuJLBNSRIjqfQVdzB9jg54lxQU5Pnl+S3Mrx7MaMmjbHpjUSwmtvVtZS56pdKhtSMEQJET7RI6Zjgu6WsugtO04dYq7ZXKWAUCmgboJmOpkGepmR3nioCdI11SwEpuYfbwAhNQAnfOT2AjHZQz3yeG6psPkqUlC/Ic7KZqFbiR12wQNoxjAEjrJHBWntkDjmOYrhI6V3tN2VyNR+Ke3aaBZprfbggApG50AnbAMgdZj+U/Xgb618VLmoQUtOmkB9IAn0me5yYx3iO/1rk9cK+jQECreUW0eYlIeUpSQkbkxGRIJjdPQZgYQp1umld8ioShCEkqKgsuBMpEqEplRGOoUYB7DDLSB1OPKcqPJTwP2e1Ei+81K91TynEIUtzcFnapQB7Y6EHPbKsRwz1HNV3cUllaUOKUpQ3KlRAAAg5IgTtAz9ZJ4Y6S6t1ZG9R8tSUqUpLmAowAOhTgj1dQADkHjWKFis/duH0q9UISV7UeknMyJ9cYPf6AFReKiPBPJQJrBSTDDTupzauZzt42pqQthIKWQFrK0pIwQoiVR6QduSQU5PCa8aH/byEI8pDqXk7liVKEqKkgewJAgRAGDiSBmgKLTNu5e6ht1ys9bW6kqXKZdlujNeG6eiQFq/EB1qD5hWlMJ/ulJxmeJXoKjS5XooaxtUrIUyogTugBQCSYjt0kYmCQOD9HcmVAAPVJV0sM9Ll7OQQjvnLe6WBa1hlTjZMJKkgpa6CYSSAO8xkgfKQOInqKwvU4adbbWh5lYQ2EJI3yeqlHcQcntMfX1G3l8at7VCmlcp2WnmEiS7hPsIBBGCDmO5mTB4F+t9BU9wbcLZ8hodF7e4VEjMgTOZTOZmABLmtrXeZiXHXeWLyHbCAVf+/p/MZVuUVQptDcEE9JgFJMx1EY6AyOPDVM5d2ko8k1AUoMnH4iVbySZGSokkx1zOCY4m995UuwssL8t9e8nIASvaREqO04URMgg5mThjunL26aTWzV1FDVU9JVJK2lOMnynEoISqJAlMhQgHE5JieBE9A8cgitLxHFIzw5Xc1G7houronAlXmMu0z8pcUlaA0CdkZSRAASSZlISoAnHBc5DcmblzOU1T0jDrymQViEuK2FO3aACNqkztMEfQ54nPIvw/wBZzopEsW6ldqapgraUlpkykgg+YpM4MkAZIj+JtD8P6tpPAl4i6N3VlA4advzCWHkbTvM5g+kEQrb2MduPI7VO9viuadPdArlUxxsdJCQSqSc+PCNdeXVNUViqR2kS0sJqGlpU25S7p2QDlUBM+3pyJIkMaavNVaakhZLJZPljakgoVIj2JAEZTAgAeoxx0t+MD4tdLeIzmm1dLFRJom22m6Z1p5fluVK5j1pR1klIwT0H145tavs4RfatbRSX0tytyQsqbAKR0EgGRP0I9G7HHtLL9zqcA9shK91oTX0Ilc3BR2qeUl2oeSdj1y+3TfsDU9ZUW2hcarkuLQ8wE7g40DKAJQQcAwM/lIvvdtVSvuJBCgo+WlGFKxgIBCSBBUev/biCOPHKu/Oqo6hl5biUEHzSUHf0T7YAyDug4Ik5jh11NStqXUtKTKVkqKULB2naRMgfbMdiYERxYxayenDlSkTpaWqLHnkVH7NVpYuAQHUtE7mlOOGPLWVdYB65T6hmFE9AE8NvOPkVqCxcpaTmGikpxpM3T9g+Yl9LjqH1t+aELbJCtxCHDJEEglIECNrla7R3ZtbUuKCkk7AFgplRJHypQjcQCJgkZHzHj1qQp1HpN6gR5raS835jaTu8xwgHfs/KrJQkbQR6j2PCNXQhhLVa9uqTLEDnkq4aq8UWtOY/InRnLK7XyorNG6DdrKnT9uU20G6FypWFVCypI3LUqCJcJhOAIPEERUqWhtSVhuTKilUDBAnMdSBj69gOE9PSKShtwqb2qMkHqARicdJA6HEn6nhbb2UpqFlbZWoLQlQCsznI+mBgnv8AXhaJyu4GOSJng18W2qfA34jNPc0NFKtKtT6YLxo0XOlFXSFTjSmylTaiCYCgPSQpJSCkgieIjzE5h1vOHmFftVXkJevF9uD11r3GW0stuVFQ8t10oQnCQVLwlPRI9hAj7TZqKZHlILq1J2JgKyI9RB94nr2H3ixHKn4ZXNvnV4OtY88tN6a/aXLrQFQ7SXer85tp4oQG1urbbnc4GkKQVkTtmZPTjwDKwnCryw0HSinQpKglsAq3AhU5UB1G337yPoeNiKZTziCtLzyOu1YzmQMdZJmBPf3k8bHEkupR5qvMYWImTtUUjOCTOP1gdJx6dZ8wy/8AuS6lKj5gKVEESDke+T7gmJ7YsSysra3V1Y6/Vvv17qkwuoq3S44oISEJlSiTtSlITGdohIwI4c6CjZpaqAp1+rXCAEJlZO0QkADH5oz9Zzw1jz31oaTuBUCodA2kREe394SZ/meJhoHRjlVWkNFdQp9flNPBtQS8d0pCSQPVmfp+ZMSRq4KbTjfDV9abTQpClqbeqVDaNhG1CCMCB+bI9OM4MiYeaZurcbWtx5bZUnKSfS2nAACo6T1jCsATPBv5xfDj5u+HXlRZteap0fdrNp28pBo7hUM7WxuAKJJ+QGMYBVIE9OBFoLWTOhNeWq7G1W68NWWuZq1UVa159JcEtqSotVAkFSFhJSoJBGwxOQRuHBo32CntBkGIzk8vimq4XVu1JcVQpddIBS2tYS0qBt3GASpCcjOBMgEmRw3t0ZQFKqVuPVLytx9KQVEDBySVZHfsme/C7Wmr29a67u91oLZb7WLrWO1CaSlQRRW5LjhIZaSpSoaQFQgEnAA492izraPoc8tYMrdWPWgHAzMx2gdMe+B9XcSweVM1k4ZE0w8cavT/AGvlO2UwncQpyIbCoKgAcqPt1gGBjsBlwoLMlKC+75acDYlXqJ/9o/MSe+QM8bqWmaogA3tST1Uo+t0YH2SR9M4PQ8THk7yg1Bzm1O3S2CzV18dbW27Vttfuwwz5qW/Ncz6WzuA3E4BPtPC7LK4+c9eqtSKnp6ZmkkANHIbBRKtuYtiXGk+Y3vO0AuFKlnAzH+EgA+3dvaoqq+ugpGxlAMLUNqQRjI+wPUni4Pis+FZdeTqHL3o9ys1FYFPluoplNbqu3jO1S1pPrY/KVgJKQPV808C/SvLu12F5O8m41iSnzUobJRTr/wC0dSSPzEznAxxyY8HfO3cryKugnj1R7g8gOZ9/YIeaO5UvXctw044nCkkiNxjEf6kiOn3KukuTSGtodipWHAhKG07khX5RtAxJJ6GR2nrxL9L6fuN5bLVNRIaLkJcWr0jckkgpPzK9UAxJGfUI4n1s0Hb7OkG4PVlxeXDrVBRgpNUkemVrUdwbIBJmO5gCeBFZxBS0x8nnd++iJQ2+sqBpcBG305/NRSw6Odvj6qemS6psDzixTEEpn8xWfSnEzv2zuEDqOJKrRNt0VTvLr62nYKAElqleCnFKgbdzsQIJQSEJ/KfaeJZbdM3zV7H4G309NbrY0ZTT0DRCXEKxlzqqAQSUz8sSDMP1h8MlJQu+bc3ad6qWIKnxIIV0Vt3GDgmVqMz9xwk3PieSodiokwOwRiitlLS+wMnuhczqLctw6dtFUopQpAcy2FDcTlZlS8AzMTuyIzw+0HKjUurnQ7cLkqholqCiwykpFQhWAACd6iAUnOFEZCuxitejLLZyip3t1amgmXXwUM08dAlJyU4OBjGcmQ9LulNb0KdbNLTobJC6h5shTmIgDBjESo9cd8r096I/kN+J5ojoLx5QhRpvw7UVkWsUFqLy1IIU++lJedk4mcJzuI7kK/huvXLQ0aCqtdZKtgCdgS6omJGFQkCI+VI+buAeCkzcF3LezQMVtw8knaUU8JUOm3smIMHqMkGeHBnS9bQpUtyno6RtsFe0uJU6omFbiYMEdcDv3jge68VOcyOXjadjDy3QGXyzqbvUlVBZ1LdSA35y2h5aR0O5S9qZEwNoO2B068bKPw8Vcf8APahapEpAAabk7AkCQB6QfboRkZkcGq8WOsvDYapA/VEplKWMCffpAIG7MJjpkSQP9fcsdQ291zzmKSjQEpkurndgQr2MiCPT3nuOJdNdpX7B2lSGwtdtsE1WzkhpZloLXUGsUj8x2qhQJgjMSciZkwB1HC53lfp9tLpSxO+FgthtJIifyjrJnsPtGIXdbddaOoT/AM4wtwpKQVpdkKwkJKpHXpB9z0meG1l65VLqUNugKSQlLagtGyesbsAAGSRESnME8T2id24lJC6/dhyKfNZcsKByjW/TwpyJCSoSFR6QYnHQ49h9QQzrrTn7GacBQwpsp2oVEQnoenQifoR2MEknGgrnUkCtW4Gyn1OOqKkoIxuEE9fsT94HHjVXLWh1wyWgrcpUoKkqkoxBV8og9sR1M9DwQob1NTPAe4kLm+lj6hVJul6cpalQDi2ticBThSFKEAGSBjHQd8Y4Y6u8v1TyQ4QtWOu0FZAwIEkYj/CenBW5nci6zTq3D5Ydp+iXSjy1tpmNoJEQI6E4HtwM+ZNmtdDq+5LsFPd6XT4XNG3cnULq207Ukh0ohJO4Kx7Dix6K6GePUyTIUGSliDgA1JKDUlVbKplcuqStW/blKkmSn0zJ3GO3tHY8FDlq89zBp009KXqyoQkvqbSgb2xHYEEAAdenQEdOBHY6dbq0spSAhSPm2kJUqMAp74xnA+3BZ5RcwLjyXvLF9tzTSq1ttQdC6ZKkOT2OTI29egBgD6NVmnZJI2OqdhnU+iA3mmnigfNRtBeBs07AlPdJdH6GoCVoc8tBle0kAHpEHsFQSSqMj7cE3RWuKDU1OF1KG2q4He4pKSkOZEEAHpJOIg5/QB33mSrV+pauvfZha1HenCQg9jkgdlSZ+kHhzsup3KWubcQ8Q4FQFob+XBMgDqo7hkAwEkzjgu1zIpiYTludvcoLS+aACcYJAyPXqFZHVa2qhht9DmzzEwlUpJQD6SkzJyDEfXvw209GtxpLLqXHEOKSsb25QgEAbhBG0gYx7+4Ewex6se1DaU04UW3G/W3uG4KkEGOmPT2Hv1zMr5Ra1p699NtuLbb7bihtKVb/ACifckbcwO2ZBntw322t14aeSrbim0DQZmc19es7TPnE/vErAW1uJUGnEj07vVBG0wZnak4Bzw7ahv8AceaXL21WjUF0uFw03o1CqO2sOKT5dsD7kqSkH1BK1okROAcpiDOrty7apKFa1O7miCrzIEAQdoVjsfuMdAeog5hsVFlvaXG2VENhRgtDeQRknA2wDO4gDpIPDTLSZjEgGVSM1w8OfQ47o7fDo552zwv8wnn61Ka+ndSlDSSAFGQYgfTPfse3Cj4i/ihtXOHXbFfb6b8KhKdqhAbUpO7IA6lXQYOJiOpFdqYrojvSCslIBaQnzSRJg7cExMwqMp7RmIc5L7VXuyU6hLpZp1FRA3B6MlJ2gzk9zAKI6K2gVV1b44PB07nbPojlNViQblaubLBu9ubqGlOICVKR5jbiSoBIUkLlGSJT8ySBJVG6AQIbvVPPUdKtbe2pYCXG9qtn7xJIxHqSZPeDjsVYO+mtK3Gs0fQpvVBcaOjuyPMtlxqaZSW6ptHpc8pRML2qJlQ3RAk7lhKQ7zT08dJ3StblJcpHgttYmV7idxSlQAB3SATk/aRwrXCkwBUN5Hn70VtVzbK40zuab9M1oermKtCFoc/6dSEJKPKJ3H0JECTEx0CiBOczHWtLU0TLDtU09TuujzWC6VNyDEEThQO4+oAgboGdpI1UtVtuHmNhltSFkbN37pB+SQAVSoR23Yx2gFnmTzkv3N7Qen13y5GvRpi3NWShDoQHqejaJUGSlAG4BThhRkkDr6Y4ZeHq/VCYnKu+MbToqmzxjY80N77tr6CXUkqQ2CmUiUknAnEmMzntAiVHXbXx5b7a4dpnyFuEIcIlJ9XYx6VK7gACSlSZPCpbIcDW0oQEon1SsNAFKyrpuSM5GTgGZ4aKtTVnrGXXAoJfJ/dhuVIJUCpHWd6imPUSMzMDiPdoyCHKTYpsN0lVasNhe1GqlYaQ47U1bqW2QlJU6VJj0R+YkREj8wgz1eeZHKi/8or4uz6psV605eW9rzlDc6JdI8ErTLSti0gkKTkH5epB68b+SfMa5ckuZWnNYWd5K7vpW6MXOhKkB5CX2XUrRKchSSUkGRJAgd4L/wASv4kGufid87WNf8wKawW+5Ulup7YzTWemcYp2Wm/XHrUpZUpxRJKiY3CAAOEpMe6r28nc+6jepCm0kLS5CCCfftiQMRJPtwZuSfi15o6a5S3bk1ZuYN003y01/Vss3q3OOgUC0KWlJW8QgqQgAhTgQYUlJkKiOA44goW6d6QUEqhUpChIyZOO3aTOY4ymSplxCfMJbcWuF7PVEkpUmcIJgd5jrgce5XuB1T3rmztaT1Nc7Y3cLbd/2bWO0qa+hUXKSvSlZQHmlGCtDgRuSoonaoGOg4QJZU47t3gJUghG4hIj04AHfrMR9T3410sLAca2wJW4pUdQPZJ6GT0+okzxY3xI665Ocy+XnKum5W6BvGkb1pywJptbV1ZWJebvlx3JIqGEpUdiI3dkfMkFv0bjqdhldI2F7w1o5oFWKxJqXyspUW0D39SfaD1InqB1j68S7S+uqzReq7dcKdKVO26pbqUsqPoltW4JMYCZ2giZgd+GW4X1FKXGWlEMNIMrSdqRu/KCB8o9x1IEEdeNthtjlWPxFUC02YPkKASJmUqXJz2wZ/WZ45FzQCSUapoHOeIIW5J5rp78Qf8AtCdx8dfgioOWb2jmdO1DjjBvFybqt7dSWQAnyRA2BRGZMwSAB145q01ucvaEpbaU3TI3EnJW4Pm2z7ZnuTHTh0tNjeuS0lxtW8JPqJytOeojAj6gmOwxxvq69ptXk0aZUjLj5UDtHzECIH3iZzGAYFVFY+TZg2HVPto4ep6Nuag4J6dT/pa6OzMUikNJaKtoO3arKBJx09JyQTP29yueeCQaen2peMlIII3E4Kj2H3PuI7njQw0WKVCUqWFqWNxSQVbkiAkZx1z0k/QHjegpp0KZSgPVDp9SU/IgCMlXWYTknrJ/UTI4A6n7lPFLESPDpxpb1PVeyAKjaD57klSCgHcCCDCR2yfc9vaeOhvwaOV34TQertTuMJSLpcGLfTL2ypKGGytyDAkb3QDjqgfc0k5Eck7xzw5j23TNmNO5drutSGEur2NrKGy4sqVEYSgkT3A9+O1fJrk/Z+T+gbZpyxU34a12lnymEE7jJMrUpUepSlEkqP8AIQBDmmL+aE8TyRUlIKVg8z8H4DqfeU92q1+Vn5SBhQOR/X04h+uvBJy95nXN6vrtPsMXKrcL7tTRrUypxcj1LSPQs9zuSZJ4LFstEQQnJ6RiOH+isxaSmEySPaI/24jPYHDS5IUFVNA/XC4tPoqg374a9xonyrT+oaBTClSfxtMpL6Ujp60gpOcZnqYjhpR4KLvoF8OXmnfuaVKK0MWlhb6KpSgDtUojcsk59QxBxEDi9FDb5QkJBKVGQY/jw6U9tIwNyU7cgHEe3AGs4ehmaQwlpPUJrpuOLhGAJSHAfD8lRK6aS1Zp+1MOI09cbHaCAEufhl+YsEdFr6JAgH1EdMcJbToKpr6kD8MtbzagnzFOCEEACZUUgyZHQn1fbi/tXpdi60D9JVspqKeoaUy+2uSl1BBBB+/+/XPAU1d4TK7TrS1aVZo6ppCg40mseU5UJ9wkKwsjAkkE49uE288LTUzA+kGrv3TdZONIJyWVQDHdDvhA5nku8+DVVN1orfSNkOFanAoYGdogCPv79M8NqnOX+k60jz6zU9YTtAWpamlY7AYj0n2iPboYVeD28aieDuqK51tidxQHEq2T12pRICumCZH6Ykdh8P1h0mhTFDanFKAh1bqMmI9RKsn69OuI4Sah01P/APIDgfdj5lMv8apyNLJNX/HkgjQa6cuLATS2k0LAMJKleQFbQREDP+Jzg4B4S1epqK3r3uVVpZKDErVvUiAAQSowIjMiPtA4sHfOSFsv1OWLhUGibWn/AKTCRviI+b8uO2OgMGOGD/8Agw5b3bLi7gl9aRDv4hwKVGR3jBHSB3gZIMeGeBxzJ5fqfqVqb1SsG4PyVfLjzZoqB+V3aocSkb1BpHpGAQcflIgHPfocw8ac5waPuqVU1+bubS1p3qWSCW0j3HUDr2AiOszwRdXfDns1MhVRpm9KfbC1H8NWEHbJKvSomR1z1EcBzWvhKu+lt7iKMlTBDhOxO76kJOFGc4GZg8Eo30Dhp1EKRDcKWoH4bt/kVKta8g9N68sy67S+oqdwqRuU2AjAEGCjqIG3BIj37cV05haAqNJVLia5hhKmhtbW0Skox85IgwR1IBwOh68O1z0rcrBXOKoK6ooqtJkpU75clOYM4GCQIicfQ8R6/wDNu+W6GrykVrSRG9afKdSJwe4nJ9JkCR9uDVvp5mn8J+tvY80SgDmDBdq/NNNkvlTbKpLK1OVFOo7UKdSQsn5QCoHIzHZUqk5jh8qLrUWSrDKtqadtJBMqScHb6REfmEbYJntg8Qq/UlPdnRcLa4UrGNo2gq9UKCgPoOkdes9iTY9OPcytIU9Q0giqZTsUNm0pxk/5de4yOD4oHykFjc91lRVxxbyHAWV1xa1BQ7XlF1hfyArKnEiDHUAK75/7T+oc11yiYu77pp4plAFf7vKVAkR3Ek+5/wC7qBkzI0nddNIUy+y60pfpncpLaCB0HYwIPQxJmIniLuU7i9UN0ppXKvz1JbbQE/vC5JAAzOen16z34yFtRQy45ei5wzxStOgghAGn0KnT1/aU4xtSh8r8tBUVKGfTMBJnPqkqMGBnJQ50an0rcdA0LdsaaYr9sPhBIJxMRHYfxMdOPnOfQlZpCrqqV5ioaXTK8txDjcKZ9Q3NqThUgnKe3Q9+BLdbgp6nXucgIOwkkbkpIO4be8RmBAHt04f7Pe3+CRjOVpJStk0vzsord2VIqFPtuJ3QAoTIUoGSk5/mT/sr07qFbitqt6vMbgEE/MD1nKSDAHXp3HTj5cHUu0ziVktuIVtQqSpBKcCB1n9ScgCTBEXVVOWe5oKlpSkOgfMQFHbG77pyJHQkyZzw2UlSDhB62EtycIr6Q1YLZem1JcDKCEkp7kFI/ugSnKJ6EkiRmOCfp1tylvSPLLhbfh1pCSCDJIMicHMGe8/Wa82+7KqJKCElhZKJBUlUnaowARPqBkCMiAVHBl5La7/HKp6N0uKbCv3RwpSSchJM9SIkkAZMGccNNtqBE8avgku80v3iBxbzwrV6PvbV/wBKOU6iwytLCijITuISmJA9Uz7TOSDwOuY2mlVda+gstKQClxK3mk+iT6UqOBGZkwRjIOOCHyY1jbeV2ubfeLvYEaktLG9FRa1uqaTVJUgwlSo9MEz2JIP6N1XamrvVF/yy21VSsBJINNOQQTmAD83QwInvbVtnEzNB5L5S4oon085lAQqs1mbqmFNL89kKbMJUSPOhcjd0I7ExORnpPA81rS/h3ENupC8khJVsDW0EDGQPSJSSDO6Jk8Hit0s7ZKlTiUv+dTghSdp83bI9QIgTAGQfbgOc27e0ivW1CilUvoG5K5JIUDk9CYPuSTkDHGt0oGhmcILbLo4vwFGrbzi1ffqTT2mrlfLncbNpBT9DZaCpeK6O3JdXvWhoEApCl7VEKJwOogxGuel6eqb/AL0rdd/EMB1RUSCkJV6kwZASk+kqV6Sn3xxso6cP3be2PxRWlRBUoFUhRMnb8w2ySowITIA7oecjaq6ltT6Ggp9wpbwhMtlSlJBR3C4QM9YJJiSrhRroP+2c0JottTiqa/Khte2irpVJX5a2dqElM7d+B6ogbSUj5YJBSBGd3DlYrspyyuNBxCiztbcB6QqdpCR6UiRsCSCUxPvwnoGdtvC9kU6IbUzKUpabKjuABOdxLgCVGSQEmMyi0+ypj8ZbXE+aUBYTLstrG0gb9sJ3BOAE4ntBI4AWmQxTj1TFf4xNTZ7KYWDStZcNM09d5Ly6MrVvfbXtGDGzeRnO7qI3LQCe3EE5huqYfq0haULAXUSjchtxYdG9Se6lbgBCsAgYJk8F7SHPC9v8qkaLFUtdgYfVXtstUyVueYQEKSFTJG3adgmCMRuIAe18FJuKytSQAtL+9I3tLBUJ6AEpEzMKACjkDPDNcz+GCk+2sax/lKrFTVlOhp0qQlJCk7CoblQD7jvABxAyeuON7TKfJTvB3gBLbaiZiMSIGM4IyTHYicoFOvtNhanZdHmK2K/6ihO1RyQQFZ7QBjtx5UtKqTO8qSkgqCZK4MEAE9DPbrkx24Q01LYw1D+9CMwQpMlW9YPUgdZgYPcEwRgbPL3PoTKVbCUQBuUYlKQkjKhETByZ9p4etO8s7zqpFU3bqCrrSwFug0zS3g37rO0EDoO4PrxiZRLtjjdQ4y8ytuoXLat52ISEpASSCCTEEnqPl9+M6ZWNGTgL1aLYbnc0rSUiIdU4FApKSpUjGAFZj6jh3qbs20W6JshpCZUtUgKECSonPsD3mYGeE7zabJRfhmStDygC4fUpRJwN2IBzMH6HhXZ7Om2ufvQH6lR9LIIKWfYqgxu6HqYEj6ccJJA0ZKOUFI97vDh5nmegC3WWylavxNQFBLUBpgblbSBAEd1D6+0mY4mmndOrqgpRKUBhPmKPzJSeoMifUTPyz1kkCSNOjNMv6hvLTbSPxNWrLaAAW2+pEzCcA9/aCPabasqaa0WxVpt7rK0tyurqVE+Wrv5fuZ6FO0Se3EOGF1Qdb9mhOIkhtLGxRNzI75n39lFLjeFv0aqOlQtKHZU6tO390MEweyhPQTAjoRxopLcG07oStQIC90QgY6npP8z6unb2mobcaDoLjVI2r0lXzPqJyEgYPRWAPYdhC+22Z+4pQt1PoaSEIaWZDY6iYwVZnaPvMY4G19XgaWnACarFaHSSCeYZkd07LwilIeQGwHql4QiJlKTIxPRPaSBI7Ykuti0w5WutoUdwyjYlO7cRnalIkwcRI+vXh50zo5dY/wCTTbXHVr/e1a8t9wSo9FGTO0SYBJPQcEPS+lGrS6KWhaU9cFK8pVQ4klW4ylKUIwdpBEjGAeuQVuerJOlnNWJFDHTN1zHkrCfCQ5G1VXzsqtRrpkooLBbXGg44lJWqoqP3aSDMpISHQR1ycAHjpnarOEg7khPeQOn04EXgV8Ob/Inkyigr2FNXq5VK6+4blpW4hRAS22Sk7ZSgR6cAqP1HFiLRYiEhJwD2mAeNmNIHm3JVN8R3JtbWulZ7I2HwSe12fahJKOp9pPbh6o7XPtB6kHH24X0VnWHUHaVJUIWem3Aj9OHyjsmAIPtPsZ43QBNVNZsSEnd14X01oGzKCrufc/bh6pLNGdu1J9skD/fhfT2ny0+rbuH5gM/bjMLEx01qAPqTuVMRH9R/nwqZtGPUkEHJxH6jh5RbfLB9KQUwnrgfT+vfj2UNszJmDMDH8eMwvQSE1NWUAY+UCNyhJ+/+XCbUWiWb5S+WrzGHGzKXBJg/XOR/pw8O3JpslMoBPtmT9fp/twjf1K0ykAqk46dP14j1VDDUxmGZuWldYp3RuD2bEIcVPKO5W+rZbUadxl9YZ81CVHygZyqTIEex7H68aGLVdLa/tYZtgdRM+acNESDIOMmMA5mDnic1+s0JTPmJzgZgGff+fEXu98becWpLjhSvO0qhIECSB9Y7+54r+5/ZzTPYPuR0nPU9Ecj4inO024SKm1fVWupDV1slMUq6lASkgDuPv7Z6DPCq80Nh17av3SQlU7ipSANqiOhPXqTE/bhurX6a+uop3X/KdUqELB9Kcd0k46dR79OMqLDVWujU9DbK0NepaFSFdfcQR9T3PCweFX0s7WSt1DIyMcx70RZVxysL4zocEJObXhlobzQVVW9+EPlDehxtuXAOmd35R1JkwenFU+dfh6XQpqA20mDJDhBVvSRI9pge5M/YkcXpqtWIcp0h4pWogpUlYwqev/jvPAl510tNX2lPkJSUtKUElSjuyJiepGCO+McM164ZFBH97oHaWtGS3JPVGOFuL53ztpKnzajgHbZc3m7G7pTUS2Er2tOShSFwCgSI3ATOREHBj26dPvg6eF7T/NwXa53lH4iltqENfhZKfMUSYK/qAIOfpxzz5y0zNo1GZTuAWCFDASROP4Ht9cmDwW/CF459ReF+5/jLPWIZUtIbdbUmWnkgHqmOgjB4sj7PbnT6tc7g1zm4BPLJ/fNT/tMtNbW20x0IycgkZwSOoC6EfE88J2luX3Ldq/WWkaoCt3ynKdBCQqRO4E9Iif045WajokVd2Q5uSpxhYV5qfSUbT79ZyDMwZ7cHLxQfE61D4o6JukudTToaaSfLZYSUNpB+YxPviZn+PAA0ddTqCufdKpCkLV64hPpzIj3UTiMDvxt9pNTTSNi8Fwe5o3I/fRCPsvtFwoqV4rgW5OzSc4Czm5aF3a2qfecXWVTgBUtwlxxSpmCo5k9ZPYzJOeK9Uuo3uXuuF19PTUF0NMle1qsY81h0KQpG4oJHQq3BROFBJz04srrghmzxKj5be2EJ3zBBPp6YKo69x0xxWzXxpKHUyTUBLrReStxIO3zUbo7fm7CZzu6Dql8NSuLi1WtU4EOB+9lCLgVU6vLhIKkBxa8wTM9QMKIjp/GOGa6oNUNu5SS4pIKtolKicECe5x365yBwbfHDq3k7rDXNre5M2i+2WyptbCK9m6u+Y5+JKR5hEHI3dwYOCAO4PdqYRJGUmVbxEZkkpBAn9IA6GZ4sqNnhP05z6hB4qptXTCXSW56HYhb6B8voS5Li1oICtwUsDMAlU5PToR8oH04KPh/19RaG13YbtcaJu5UFtr2aurplHaKtttY3NqIwCpJAxOf0gXMsgKbKEq9BUjaQTuTGD9ZEziADM9+JHYqlaaQtwYhS29ypJXAIIVOSAQcAnJPcnhs/oY7slQNDnSRu5HIXTPxteNjQHiy1BYn+XukE6XYoraGqtK2EtB9W+EyECCEgQFfMoEiRBHEU5btoqKZhDikHcvcUKG0IEe/QSOncA56cVR5MVqG0KCgnaluAkKEqWVkAEnE5EiO4zMcWF0FeF3KvoWtzbKlwy4t9zchIWQNxJykDeM9hntxZPD8oY0Hovn3jG2sjaYGDOEQNZaYoFUS3adbXmNnaUncUKIJlW4ZTCswIzHeYrxzQs34itrCkLJaG3cpuVMkkEuJgYMJA9UAZxIgnTmO4rSGqq2yuXC23BVqql0ZqKJwO0r6knbuZUSNwMDMEEH3jgQ69T+KQXkrBcdJSC1KScfKMY6mSTmcgEGG6of4se6p9tP4T9TRhBFTBTqBC1eUvzqZIUPMQGx6oJABz6oxgkznuGXmna/J0u2ypa1qZeSoBaZDoKx8/QKSVd4zHcjiZavtLluudM9ChsWfVtlSkhUeoqGcz9fUPc8R3msy28mqpiA0j8OVFJaC9hJneAkScSCnpCRnpwq3CICN3uRy3yuMzUM7NrOsprFX2OmXTppLlUIdd8xlKn0rbUvYApMrSCFOqUEgg7M5EqakuGkujDqW1BKx5ZQkhQgk7ASpJBG0picSJ2yCR9tdsXdb8pLVMmoQMKbV+8RnqCtO0AFW4kekkJQP7o4U6r04u3VC6d9sML8z9wXlJCnE4grBAURjd06LPsJR4AfEBHRPdQ/MBDuqk3K15VZVOqJQYpyqdpUCpJncNvqEFO8FPsme4EP5oUrpRlxSUwv1bZTuKYKUmJgkbQJkYAJztleiypujefd3Nmo2OJL5AUAspJJJkJ/MqAegBMQoFi5tUaW6ZcRvDjbiWwE+YiVbSBuABIABgEYyVCPSzXEk02eqT7cP+5x0Q/wDCRc+TlqqNdL51WvWd2Yq9LVg0sqw1KWnGr6VfuVPk5LEk7jBAIgggxwIm0JStKQ6HHDElJI3wcmOkE9ExPXHt4pGfNAUVO1BVErGSAPaYxOJ7AfWOPaSHUqjylJIDRO4GCdwhAyOpMdsBXbhFTdtldwP7NL4zfDXyA5A6ysvMx/TmntW1D5d/GXlpDorqJKD+4QVAkbSVenEknA45q/EU5iaH1j4steXnlvTCk0tc7s87aW07g21TqWIUP7oKhIG3rEiegFtb/wCz6QPwAkpKPQjcFwMBJgSdsZHY/XhVb6J+uq9xQj8VA2pmQ0ACIA7kgmZ+pnEnHTt0aAOamUduc6TxM7L5R21VCoJQQ5WvKIBM+gnBjtu6/bb1I4lmldIPO1LNNTIU/WOZMK9LckE+qYn1CP8A3g9p4lHhh5Jo5085NO6QbrWqAX6uYonbi8T/AMsh1zaDu7DIJ6Hp1GDdL4qnw3NP/Cgv+n7TQa3TqZvUdtU+oMtoYq2XMp2hKVGEKPqCjmB0jIhtp/FOqQ4aMpvirYqHEMAzK7lty9feqljyOXGnnaVh2jqLpVg/jNyVfu0zBnoAnEEyZJIBHUw1brd1YLrhdXaGVKKSoQqoc6yR0AMRGYn78eq6rcuzqqioJYpVOeYy2JPmECJUokFR6dDGAB78OVqoA7TpuVcCmlQQmnp9sl1XVIgROOmJmO0QOuNyBAiiGGjZN/D/AA3JI81dWcuO+/T1W60afS8Px9yA2iW2WSYmDIKj7kwYEYBmOnE50foKp1c6l1bSk0jJO6USWxmFQr0TAPzSAYJPXj5y55cuaxuLVZWNvuU6BtQlAIQ2Py7lJ6mZyCAJV3Bggts/8TPMWqjATQtqASWk7V1piQkZMyNkn5cQO3ClXveDh53/ACHr6qxaOohYwiDlyz1PuWmio6VmgX+FCmbeB5DjyJK3HMjbuImAUjEQB1meLNfDk5WW658+bLW6hpA008l5FjoXmwgKfSjch5QOSdqCE7hJKPVAA4FY0zTaOZpkP0yKqudBZprayApAUSUpISZKldD0mAR7AWO5Acu2uVmv7LqXW1U8m+PVbTlPR07almkZ3hUwSJUBggdAoz1HC7PeGRFug4BI3PN2/QdvVcLhRPqKZ7XnzEHAHTbquglh08BtEbwYJIPzH34lFnsgcCUlIAHRUY74njfa6ZpDMgpWhQ3gjOCJBB+3fhzZrG2x+UqAiOGgbqjiO6U26zgQqNnYiZBH24cqenQygEmI6T0HDI/qpmmAIXvP/uyof1/hw21uukMNEeYj0Yge/wB/6zxsGrFMl1rVMgA4KACT3BxwlqdQs0424BTJIkZj3+nA6uXMQSRvG7JP6fbpxH7rzLAblLkkdMwD7/fjbGy11BFSt1q2klJXCTOUn6Rww3TmClswFlM9IGR3x7cCm6cygSuHCYGADk8Ru68zIKti4MzGPSOPCcLM55ItXDmL5atwWUp+UAGD/H24Yq/mOHBPmA/mJT+b2j6ffgQ1/MlInIUZnr/HiO3HmSQcukFQ9R9+PC5ejKMVfzKO6AuZO4g4A4ZbjzKkQXJV+YDgN3DmXBIDoz1B6n78R2480S0iA6TOJmT9emMY/nx5qK20o0V3M8oyHSPLGNqsgz7/AOfDTWc4Kl2nW27WVa2Vq3qQXVbSZmSOhPTMcAu7c0UAD1hHvKv4frxHLnzZTlIe94JPQ8czg9FuAcKwFbzX3A7lwlWIBwQe324i+q+Ya7mwGwv0pWmQFQIg9RBgAdzwCrpziDRVLonvnpA4R6d51UlTqdlqqfbabeKpcUjclsgE5EgdoiczwMvEb5aKWNnMgorYyyOvie/kCFA+f+o/xetLgguBpDS0gDBCIAkAzJjrt9+8TxDrfXf/AMrfddDzoCF7wk5ThXpCoIIyD/8AEe08JdWX46j1VUuugpNWohSFSC2gkTMdgIBxmD9YsH4W/A9W84qe01l/eRbdN17pcrksr/5x1tJhICFAhG+CAsGRJVA7rtupn+EyJg3GPkvoC5XCmoofGqXAN/Pbsq5aPrXHK0r8xSWgtTiiFbYgekkdJgTI/NODI4udyA+HdqJXL+qumonTZrnXoSu3W9YCvKSU7pfj5d0gQCSnM+xMvgz+FJZOXfP2t1Vqa5UV5s9rq1uabtqEbm1BRKkLqdwJJaCglKdytxTuJIwboam5et1ZUpptIKpJAIgmev8AHM8HxahLl1R12x+v+FW3EPHLQWw2s9iXfoP1+S5H8++TmotCWB165Wmop6Z0bfNCQplsD0kqIkA/+6CQZEDil3Oa4JTXKlwJW0VIWXFzt3FWM9cdCZkfQjjvvrDlukIfCmvMS8ktuoWJSpJEEK9wQYg+/Ag1jyH0/darzKzTtiqXUjalT9uYWQANoTJT/dgZ7Ace0FpbSyao3bLWm+0V2jTUxZPcFcK3f3zClIVsWleEEgqQYwY9zCgJGO0kSM8tPltnqlPpUQZCRAJI6jB7icn9OOv/ADD8EHLLWtjYoKzRFnpqajW44yLcg0LiFOABZC2ikmYSYVuTKQY4p1z9+FjqXRYqa3RVX/xNbkK8xNA6Es3JtO6QADDTxT2IKVHPpnq1wVDC8ZUui4tpKhpY7yn1/wAqp6QWal1B6lO4zCkwPmM5nM5yMdJjiWaaoQ4umaKvINSQ2pxS52ElICSVCQUx1kHOeuX7T/hD5i6vr61NDo69KNqaCqoP0qqZxSCraEth2A6tIAhKZhMmOh4M3Kv4c2vNW0VM9dlW7TlFUUxdT5xLjrUqMNKaTDiZGREp2kdd0BnbWwNb5nBQZq6njcXOePmmHUGiWOUl9atlNd6W9qShDqKilSspO8boAVkEAqBBHYjr6uCHoh9NZWI8qQS6BAcDhSmSkkGe5AIKpGI6RxG+YnhM1ByGrqBVxdpbpQVbyWU1VGlS0DbkJO4ApcOfpB+b0xxOOVGl6m/31mhpWX6urqH0tJZQkKO4kbAAT6icR1I2nAkRYFkqmSRCWM5Cpnid7Hzuwc+qcq9tFE3BISCQZQobVT0GSACFZOcAYzJ4bNI64e0fqNi5Io7TXPMsuodZuVMKinUpaSlRWhUDcATGZB2HtHC/nZp658ubm9abxRrt1wpv3JaqU7VsL9jMZJj3gexOYRaroF3Bjc4rZuUNpWpIGDiBgJSnJhI+c4JiGiOqbjDlW9VQmQ+VRm+UDd0uzaAHk07yghS/LTC4BBB3TJIKesZJyR0IPxJvB1pnw0aG5d3yxa9tGsHdc2n8VVUtIlC3KRQQmBhZKkkwgSQoqQYBIgibmdranNUVoRT0qA15KlsoAyBCjO4blZ3HYo/QADgX6quTl18tx0+agoSCWxvwT2UNoyPTGe8TuEBLnUh+Qw+9dqG2OifmRK/B5zNsfKXxD6Z1DqKzU2pbHZa1D9ZROpJTUttuJVtJJ2gKHqSI2jYDCSqOCp8VbxJ6K8W3ifOpuWumEaUtNYyhkUaUNMuPuAet0pRKQs9gAZ3JjG6a62qndXWMtrThpsLUAhKUnopslMDaQnYB77TAyTw5Xy3rbQumUlSJ2LSNiWiQoBWBGUmRMn0hJjHAOmo99fVEa2vAaYwt2lFt0mkF1LBStO3YVhG4LUASZ943ScnB6CAQ0cwqn9pW0Ogne0o7lKWgpKQQoKBUTnclRAiMx7Hh/q6lyj02/TJUmoUQh9Xlo3F1UJIicEJMAgRHpwJxEr1VecHada0OI3hKCkH0HcFg+o9CSZHUwIBgQTuB8mhBrYTrLvVCDwy8qRzx57aT0gmp05SPakubVtRU6iq101sp1uApC6l1KklCEiFkg9QgZynhBrHQL+jtfXTTlRV225P2G5P252poXRVUdWth0tqcZXAStCvL3BQGU5AzxHqFhNQWoStYIlYCU7ikqIxJIMk5k9+sdZWxaV26lRTIbK3p2LW0ncBIgNpAMZjMZEqE54QnOxsnikpTI4k8hzSTy0VVwQtxSvyobKAVKSndGBnMwCYPQjMxxJLbZF0G2mKUrqlkOKCQJQIGD/eURB+g+5HGyhtzeicpDC7wv3TvRSAgiT7rBkBJ9QKiemOPbi1Wrc0G1vVbhKlNu5Wsk+olQ64weg9RwTBGaA12l/xRmGQ+GXxjPb3pwtd8esqHWqJ9DRbAUt5vKnFAkglX5SDJET2EgwOHXVOsLrzDu37Qv9yr7s82A2hVVVLddWkKwNyp2gGY7YEATwzW22IbaQ4vaURCCIBcPuB9+8CMf3Y4frfbFXdalur2UtIkF9aUghlBAwEnBUc4OTBzngDcbmXHRHs3srN4W4W0N+8VG7z1PRebRbP2m+9WVm1umZKSRlIWegQBkqBJiPYd+045e6LqNe3lNZV/8tQ0w2NhY3EjBKQB1UEiCQJxiIPHzRehXdZ1qCGzTWplSkq3H1AnBKjgKWcjMyIGAMEporQE223g/IlJT6SKdG7KSRjE9IVmQcmeBbneENR9s8h/aO/vTdUBjhoG0Y54/qPb3L5UIXeG0WKzMeUVIC33lkJab9KQoqVlO1IJAECcR19RH0za2dB21hFG29c73cEhtASDvcKj1QPb1HoBkkz0JbND6c/CoU2zTj8Oh0B5CTCalzqSEkxt6nbugbv04PnLnSFJywZbvVyKXtQV8ONKUd5pkqP5e5WTOMCDknEVzxDeAD4Y3H1cfX0Ryhoi1okeNzyHQD/KU8teWLXI62q1BfG2LrrevSttLIBVT0G8j0JzG6CJUM5EYBHBG0jp1vSFai93tSrtqevUfwlEDu/DpUJEJwJKTG6OxIB4jCLidL1dJdq6ldrb1W+m2W/CnN68+c4kYk/KlPQbQcHIsV4e+RdVohoao1KTcNZV7fmNB8/urWkZhUzJGM5iYnpFd3C4Oa0zTO39Po0dh3Umrljpo8dT8z/gImaF19d9PWa20Wpall25vspJpmGjNIMBPmEfQZUY94GeHWt5iJLJAc75z8vvxBmUi+XGvFH5tbUxNZW7dyFO4hpA95APfA4gmpNcVNlr6imqULp6mmVsW2sFKmlDtnP+szxYnAt5lraVzKh2XtP0KpfiihZBUB0YwHD6+5Fe6czNhUfMAKjhXbhgunM2ZG8qMdjmP9OA9c+ZYQpUOR16nB/r+fEbu3MsBRhyYn6En/GeHjUloNRiuXMpIWoeZ8vUkz3mD/XfhgufM9Klql3AGM9OAzd+ZgEpLpO1Mnt9hxG7rzTk5cA7ACI/n/h9+PCSVsGoy3fmXuUR5pyeyun+3EbufNBJlKVwc9e/1/r24C135qDP73A9zG7iMXbmyEKMPfm98njXJW4wEb7nzRTuUC5GCJ3e+Y4jl25pEqJ82Tu6Dof9+AXeOcCgVISpZHTr8vv9s8R6v5jVFS56Cev5fbj3TlYjfdebkgbXEhIAyVATPcff+fEZufOAbClLwIIIJBIBA/8AHAgqNQ1dcQVLM5k9Y4TP1Li1AqcUVRPWT9+PNgvBlT6782VOqJBBj3P+H68R+s5gVVSowopHsDwO9T83tNaLB/ad6t1KtP5FvAr/AEQmVH+HA21Z46NL2hKhbWLjdXRO0hsMNz/7lZ//AOTxLjpJ5PYYfyXGWphj9t2EfXbzUVGd8Jmcnpxperyj1qego9U7un1+nFONW+OvVN2KkW2lt9pbVMKCS+4AemVemfsngZaq5tak1stRut6uVaFGdi3yGx/8BCf5cEIrFM7+Y4D6qC++QsOYwSfkrzau8TGkNAVK3q66Ww1jagpLVM4F7VT1LaAScY6iI4tr4B/iG6c5uW8N22vdQ/QQy5RPDY8wjokweqY6EdfocccRfN+mPpjh75ecyrxyq1dSXyw1rtvuNEqW3EHBHdKh0Uk9wcHiSOG6ZjD4ezu68qeLq6q0sqXamN5Dsv1U8pec6K1ppYdSUrgJIOQP9f8AXg+aP1/T3elQhawo/UiQZ78cVPh1/EdoeeFibpKp9FDfqNKRWUSl+odB5jZ6qQT+qSYPYnofyr5zpqWEFT0wAJBiZwPsc8ApInxvMb+anxSNe0PZuCra3axMXyiJSgCQZEd+BnrblztBIROSRKf14eNBczBVttodckqiBuwocTsfhtR04Uk7gR6o+ZJ9p45kLpnKrJqPSCmFKPQ5MniG3WzK3GYUPzAjPFndYct/OCihG5MzuTkA/X24EurNFLZWoLbUDJAO3BPXr0/XjxYDhCh6kDh2uAKKDIBlUfXjyEhsHAg9VJPX+PEjutiUytRIIQT3yOGh+gIWRkkGOvHh5L3UVHtWaao9ZWCsttc027T1jfluJUncB/dP0KSAoHrIGeK4WJu9cg+YjNU0tygudidRVU7qCc7SSh5JjbBgnInqO2LUOUaisEH1I+v8eIjzR5QN8wWmXGahdFXUra2UOKTuQpCh6kke05kZx9Tw1cK30UMpinP4bufoe6FXWjM8eWcwqv8AiH5u3vnTra4X/UNZU1VxuLpXUVBGxK1fQfYJAA7HvHAp1NWuWcKU82thflealKyqDuSPLKSRIHQgEmB7dOLC33wWasLo/BO2mqAdCgsVXkqg7QrCkSR83pOACYzHEc5teAbmZqu4MG31FqvC30pZU6agtFiAYSd4yhJ6lMkykBMdLE/6hoD5WSj5oLS26Rpy5pVO9T31166uOpccSuSrcU+WnccBSiQk9QIBk/LIiRwhuDQfp1KVuDlTThBdWkbtsiVKGTkEScfl9I2pBkPNvk5qLk1rlqx6otT9qrqZfn7CWy24kEypLgO1UncAQdoKyJgEcNlLalXCkpkt7nz5u11TRJ2pEQRPqAmT+X1AiMAHpGfEcC3qo9czQHPK2aJ09Uag1GiipqdTrzqQgMMoIcWrcQ2Bt9JKZV8v94GRJPEq52+GzWPhv5k/8OaysVy09eQwKwU9U2lCi0oApdTt9M9iRGAUmTPHzQz7+j3xdaN9dJU09QlVK62opQy4FBYcSsqJEEKVOOoz1HEm8RniQ1p4qeYp1Pr2/PXy/IpW6Np4oQwtplsEJTsSPTElRMdTJMnhhZRuaAen1VZ1NyDpHAHl8kGdZNoZrVUqQ4oObUmYM/MRuQrocTPWZInI4hqqs012YQ6vc6+35rifUVKKSRKt3zCfSVH1SpXsCJhqZpa744sF5DVOr0tp9CFFAJz6oCQYmegiAJPEDrHCmspWqfzUlKVNKXISp4BwLK4BJCyFbSYAA9oJ4E3Ebo3bHYGShbo22olyreKGkMALbWpRgLztJPXckBI7THeMvdNqZKVBdvpmg6oeTTJ2hRkjK4OCB3EGPV3yInT1vn0qWFKCmm0CEAbZnMT0JJg9CEj7kcSLS9sS1bl3CuBCduJV6g2Mwke0nrAk/SeEQS+EfF69FY1LG6pApI9m/wBR936JxbUiy0yqyrK3n1q3NpIILkACVTn9DJHtiONlronCnz6kO71KBKE42qxjHScnBic9eEdmp3dSXVdbUpdRsJCEqSAEiMJiYyD3yJPtmTW63JuDwdP7tpOGxu/KB2/nmO/TgNX1ZY0s6nmrH4ZsjZ5Wz6fw2+z6+vuWy1UDtyV6ihTigAoEEop0bpyegGfc/wA+Jxo3SKtWVrbDQUi20n752pW2f3qpy6uMAxMdkyJyZ4aNMWZd6UostJTTIBQgGf3yyrbHfcB6p7D29zVpfTrWj7KikbR5laSXXNhkbynseoiY9I2mDJzgCMhwIGXdB6qz3PDYy0HA6n0Xtq2ilZbpqVLbNPThLanEtlJUqCIUAJI3EAyTgJ+3Ex0By5RV0qnlOfh6AkIcrIj8QSdwQmI7EkCY6+8HXy20Ku9VTVbUKShDMhKwCC6CQPlBgmdo6zG0iOnBTp6Og0/p9N9uzYTZaKW7fbFZVWvzEEd07gmc+vIzkkRxTUttsQp2u1Tv3d6egUS0H+ISmfGIWbN/8j3W+x0VHpCgpLhV03VR/ZNpAn8UNwhxYidogRIyT3EcT3RlrdpmHr7dAt2r8yGElshDrwAGxAzO3pOc7usSGLlboe5axv69Q6ggVq4TT0illYokdBt6BO1IP93omJji4Phv8P8ATuXOkvdypkKbpcW+lcGGkjq+tMCD12g+844pS73BkOznZPU/oEzVNaymjL3fBJPC74T3bdXI1bqxttV9qpfaYcAUKBs53k9A4R06RtEQBxMNUahq+ceq3dOaUU5S2mnATcrnBJZSMeWgz85yBOJz0E8PGt7xcOZWohpGwOFCFkCsq5grE+qVdgPt798cHLkjygtHLXSrPkUyTTJMoxC7i5OXCTnbPuen24X6SGa4zAuGw5dgO5SHc7x92BqajeR3Idv30XzkhyCoNH2mmdq6UIYalVPTrkpB+bzFyAVLMSZz1gcU8+KPZL1y45xp1PUs25On9Uw1bnabclRcYaQHEvBRy5kHemEFMDBSZ6Sab0i9eXw9WFYClSlsdB9Z9uvFQ/j48htRaj8KdHrG03amp7Jy2dXcbjavw3qqQ9tY/EB7djykqjy9hBDiiTgRffC9kdBTfhsw3n6uPdVHW3l09VqmdnPyC5v3bmkk7j5xhOABn3/r+PEUvnNpFPMOyYIVB4CNZr2srCoblBMxkxP1P14QVF6ef9TjuO/qzwc0jqpek9EUbvzgASUhwkgbQkdv6HfiNV/NR95UIUY+mY4HOoNb2rTCFLuNypKJI6l99KP8TPA+1L4yNHae3JpqipubyZ9NM0Sk/wDyVA/UTxKjppH+w0lcZJo2e24BG2p1RV1i43FPvJMxwkcqnHlfvHCT7SeKsaq8d9zqkrRZ7TS0oPyuVThdUP8A4pgfzPA21R4idY6tCk1N9rG2j/6VMfw6B+iI/wAeCEVlqH7vICgSXmBh8mT9FdjVHMKx6QY33S6UFEEiQH3gkx9E9T+g4GuqfG3pCxBSKH8deHRIHkMltE/+5cY+oB4p86+updUtalLWsyVKMkn9eNfBCKxQN9skofLe5XfywAj1qrx5aguZUi1W2gtrZxveJfcj+SZ/TgZaq546s1nKbhfrg62oyW23PKb/APxRA4ijaCvABJ49qYWlG4pMTBPafb78E4qOGMeRoCGy1k0ntuK8KUoGZyf58YVk8eeM4kKMs6njOM4zjFizjOM4zjFiedEa5uvLvVFJeLNWvUFxoVhbLzRgg9wR3B6EHBHHV/4eXxJaPnLa2rbc3m7fqWibAqaUq9DyRA81qeqD3SZKZ9oPHIfhx0pqmv0VfqW6WurfobhROB1h9lW1bah7f5g4IwccQq6hZUtwdiORU2jrXwOyOXVfp65S8526ttrY+mMTKsH79/6+nFhtA800uoRCxumFJUr5uOIXw9fiPU3NOnp7ReHkW/UtKkF1tJ2t1qRguNz2/vJ6pPuOnRzlZznbraZB84EqgKz1+p4T5oHxPLJBumuGZkrQ9nJXvteoWL1Spykz1B7CPb/Xhq1XoZq4NqcbQMzkD+EjgP8AL/mmQ2g+YrrGff24MOk9bsXWmShUKQrBzn9fpxwXbIQm1foRTC3ISUkHBHUE8D672PyVEEf9u0D+fDl8Rb4oPI/wE25VJrS+PV+sqiiRXUGl7Syai5VbaypKFlRhphslKvU6selMpSvE1C0x8f7w4a/09T1dzqtY6Pr3nfLet9dZF1hYn/1A7TlSVt/UAKnG09eO4pJnN1hpwuJlYDglWSdt5QegmZg9DH+HHluiWophJO8lIKcgkCY+vAkq/ifeHMWqmrjzf0iaety0nc8X05SP3jQb8xvKhhaRjceiSRzh+Jv8Q++K8Ulzc5P8waWm0vcLHRUj9y0xWONmuO3zFeeqdpfbXKUrSlKkoITuOeJFLbpJn6CNPvXKarZGMnddRuaXiZ5b8i1uNav1zpewVLSQpVJVVqfxJJyIZTuXJxAKe89OKbePT4o1Pqe02/S3JzV9tqqG60y/2vcqJuop66nUFjay26oJCAQCSpMrEFKgAQTzR5mc99Wc+dcHUGtb5cdS3lFIih/GVqwX/IbJ2NqKQmdu45OeHDRF0Cq/aFB3bsSUqcVlIUI6RAnBJIwTmeGu2WSGF7ZXnUR06IZLXvkGjp9UaW65bbzzu1dQtSyFvq2lSilMD5pBIxnd2B+XiZaXuy75Y7bSPpo1C3o8qnX5Y3uhR3FRIAKiAO5kSIAJ4gNElnyWVK2ubiA4REkAFRIkH2EqPpP0IJBd5V2hSKNLroceQSSpKnN4JHdJ+5Ewe6vpxY1kpHVFS0AJY4uucdFbnuOxIXrVTZtdC3RqS2hVOgnalzbtUoiQQBkZSMAjMd54YCHGA0FBLTq3Q4lSkOSDOCQD6gNpT7dO44MWoeVlqreR1z1k7rC2s36huzdub06pCjXvtKbKzUAiB5aYIxMkdpHAZ1g6mgWWQnbLinwlKktrR6VE5kbRBAJMn7QRw41jAwEDoqPtlUZsEnc7qFajeP4pp3yAuVKQhIbUPMBP1Akwr0qSmJ6AKTwWPBL8MrXfjw03rzUWjXLamm5f0y6q4LrKgNO1aygrLTeCg7kIiTjbuGJA4Cd+d865BKC8EpRLxcSQdqSVQcf5SJMjG7ibae1rzP8AClQVNtbuOsNAp1na2xU0bRfpDd7e4CtJWgx5jS0KUd3qBAIEEjhMqgXZ0qxKN2ho1KpunrSq6ONo89akFXmLlzATtKSrJ9p+sRkYHE3To2v1Sll1mnU1bET5bykFCFFJkkgCJkSAD39gIa9B0KHrmzSHcpt1SFVCkyRsiI+ij8xTJkdRjjuD4uPE14Rqf4Slp01pi22FOtBQMNUlK3R+XcG6tIAeddWBuKSQT1M4jiunu1Zd25K4qKmfH4cQYXB5GrHbt/lcYWKBtyq/C0+9NG0opJkAknEqPTuenYH24dWqRd2rmLZQFQStQDm1JCW0/UdTjr1I/QEo36v8OyG2m0rqXVny0jJJMnriBmO3Un34KPJvQ/8Awzbxc6gbal1MILkAKJBVM9CIAO0g469RwsBwc8vedh9VekUJhhbFGMHoOwUo0bp1nSjDSadhwO04Hko2jcSTt3EQQe6QCZBEjIjggWLSB8xDLhJfWdzy20hwpJAwoKMJz0GSYg44lWhuTVjHIp3WVVrKz0+omLg3R0+k32XPxj7C0bjWwcEA9J3GQDidpeuXFjpRVGsqUF5KvUy22lRcdA6NCSSZ3ZPUFQPchPGquDbZA6vm3cR5R2+ChSA3CX+H0xw1pw49z7+3qn3TllpdI6dp664FSWlAOUrGE+bAMqUQMJAVBMD2wZKlnL/S9RzHun/Ft/ZcRaaE/h7RT7NocWDG4IP/AKaMZEGSR2PHmzaPrOb2u1UFQshpEP3RxoAt0bKQFBiB3JwexyT24sLy25XjUmrLa0mnbp6ChQkUzCU7UstDCdwEAHrOZOcHHFEXi8uLnTyuzI/6D09eiePDjpohAwYa0bqYcjOUKaekar7iyt2rq2yppsqny2zn1dp/3nvwbNYXpzSWlGaalEXO4thDSU4KEYBP6kgD2+/D9ozlY5abIKmoYXTnKziFuLHfb/c6R9B9ojFfXP6c1c/d6os1ToE0oV/06YwQFlJEkjAAnuTiBwtxcJXetc2pMZDTyH6kJDuHEFI6U63+z+wER+RPJ+n0haFNVe1bxSl25uiZUSJDcD3JMx/HtwfNHWRVa+1WPtoCl+mkZJiBHUj7fy4qfcfGnZtJadZYbstyqKiiQCKdxxJRUvH/AKjq3BkD9CfpngHc2/ic81ajUDlXZrtT6YpkwGaejpWni1tAMFx1ClLBUCT7iE9Bm1OHeGm0mBONhjPcn/A6eqrK81U9a8vB3P7+v5Lq4/W0OkbHVV1fVU1BRUTSqirq6l1LbTLaRKlrUogJSBJkkADilHxzPH/y+5A/Dq5gW9OtbSdS6303U01lpaBCLm5WtOgJcVtQSltpxtS2w+ogJU4FJ3FMcc7vGZ8Qfmd4m6GqtF91C9TadqXQ89Z6BxTNG6sNtoJUmdykEo8wNqJSFOLIHtzt8bl0a07yYujLDbLari41TqCEBO4qWFEmBk+ji2oK6Jz2wxM2OyWHWt7IzNI7cdEGdR+OO5uBSLXaqenR+VdQ4Vq/gmB/M8QHUniT1lqcKS7eaimbV+SlhkD9R6v58QdR3GOPHByOigj3a0IZLXTye08rdV171c6XHnHHnD1UtRUT+pzxq3ZmOPhEcZxJUUlZxk8ZxnGLxfQQONjVKXlhKZJUYECST7cagJPHZz4I3wsbDyT5UWfxHc3LXSXC83ZtVbobT1wbCmaVlEkXSoQrBmCWkqEbfWe3Au83iltdG+urHYY35k9AO5PRTbdb566obTUwy537z7ghL8Pj4BtbeNJ23mJz5aqrLZbghFRadHpdVT3K6IMbXKogb6dkzOxMOKH90Z4rV8XW02LRvitqtMaXt1qs9gsVI01T0VtphT0rS1ISXClIJzuBSVH1EpMyZ47H665jXTxH+bd6y8V9i0tctzlO62tSLtqJrcP3yFDNLRKE7FyHHhJSUIhSuN/xebFbLP4uLkLIltNqDLbCEsiGmXEIR5jQ+qFEgiSZJnM8VpwbxhXXi/SxVPka1hIjA9nce07+8joNhy5qwOJOFILZZY5oxqcXbvPXY7NH9oPXqqp8ZxnGcW+qyWcZxnGcYsWcZxnGATxixZxk44wiOM4xYlljvtXpy6U9dQ1D1JWUiw6y80spW2oGQQe3HSz4evxI0a9cpNN6lfao9StJCWXD6WbikdwOyx1Kf1GJA5j8KKK4P2+safp3nWX2FhxtxtRSttQMhQIyCD0PESso2VDNLufdSqWqfA/U3l1C/SZy05vJqG2jvUptYmMYx29+CXrvxcWfw3cldR671C+8uy6XoF176GSPOqSIShhEwN7iyhA+qp6Djj/8Oz4jatVP0mk9W1TbF7QAilqVkIbuMdvYO+46HqPbg7fFI8TGmWPBHqHSVfdGVah1aKQW+gSSXnA1VNrU8R0DadhEk5JAE8KYo3sqBDIP/SaYqlkkRewrmt4pfEVffFr4hNYcxtS+U1edX3JddUMsrKmqVJAS2wgqzsbbSlA+ieIK2ol0ykBWAPpn/XjR8zipIJVPT7cex6d4/vGT7DPDYxuNguJIxuvlRVKOQopx3PT2H06ceaZYqKxIdK1JSADBlQjqMnHXjRUrB64SBkkD7f0ffjdbWytSRGSk4E9D/l/vxOhbyQioky7ATzUqp0OOKpgtLSjDYdA3SYGf6H+PC7SN8NBeWnVha0AgnGCOmf4/1g8NFTKwlKcmR1GTgwB+nSOHLTlnW9VpUlKVqJhAxBJxGccSsnOy4jnlWDf5g2S6XtpVn/HIo0spbT+LcK3VK2gqH0HYAYyOhAg4cvbszVWdsISVQZM/KowQTAPXB9o6STxXHlByouWp7uinoKZ+seA3lDDaitRAlRIGT7nOBnvxbvwScwXfDfzSterU2KyamNrDqRb7u0t2lf3oKPUIEFM+nr06GZ4s/hOGcAuazJPwVMfaTcIngMdJgD4qO3Lcbe8/uJSCooODCcSeuMZkfT78C7W1wcU+8HipSWygJC9pJVtEp3GTjcOnTd2Hq4L3NO8CoQ/VNsMsJdWpQbaTsbBcUVFCB1gAwJJj08Ae9PfjbkNgSA2N8IlJIyQFEnBgDuB1OZJBjiRxZhnVKXBf4gMp5JmaQ0mpXUPhp1taUtpS4Ey4olRXtPQxEBQ+aBGFcPXNnnXqrnS7bKrWF+uWoamzUFPZLe7WuFw01HTgpYYwR6UjeqBGTJkzwvZ0Clnl1VXdd4tIdZr00JtqlAVh2t7vPKNgCmYBRu3E7ohPQiD6keQWaZpBVuacShxRb3BIISEhQI27kkK+xMCBJ4Spm6G5Ksqmk1nZR/RFmOnLSh5/aupcJWpSkx5ajEEd4TPTB9UmO/qvrXLnXNrcUp0mIClk7ROPuSR/I+3G1Sm2mN7gPloAU2k/+pGJA6YEk+3Qe/HqgonHWzUrBNVVnykJj0mfTt+mNo+3FJVVR4jsDkvtWy2tlJCGAbnclPPK3Ri79qBCnEJGyFrLifQn1RtJOE/rgSJmOD7Y7aw6265UKU1ZLQAt71AeavCtoOMqWZgkYCeoAPEW5Y6SVZLallggPuCHnCcLUQcqKhtAE9+ySe/E6uzNOaJhChutlDucYhSQah0pUreqTIkkkf3gAYAMcRGkOdpdyHNTa+cxx/h+07ktukX1VL69RXFIDlSoClYTgJQRCUAdQcyZmUiAcgA/aS01U6P0UzW1iPOvt7/dUbMlZpkZjb2lMkkjJ+oA4F3ILSyde8yvx9zINptKBVvuAna6RMpIPSIxIwD7iOLa8ldIOa81rR6lujH7nZ5lI1GWacH0Yj80bogf5cVfx5ffOY/6W8/0H6qdYaP7tDrd7RUh5JclqTlZoM1VyTNXVk1FXvPrfJ+VJnp0E/wkA8Wx8KfI4UNtRf7uyE1lw/fNtLRCkDO0qB9gRCT3yfrB+WHKdfMW7t3S6Sm0suhVLTlO41RTiVEYCcY7kz24tDYafyaedigkGEp9zP8AvxA4M4afO/8Aidxbz3YD+ePySZxbxG4g0lO7cnzEfl/lMfMWpDFGSDtIkQex+nFe+ZVbCXfVAJwT1+304N/M+rKEuAH0px0+vFc+Y1atxbkKSczKfyif5ni1FXIQl5g3QKDhgAJnp/rwEeYd12pcAMpj054Kev6ogOKk4z7H78AzmDcCtTkZjqT3PHJ5XVqF+s60OuOmcqJnEHim/wAQvUKW7LZKAFUv1S3znshEf4rHFuNVvlSnDBMyTHFEvH1fxcOaFBQpVKaGj3qHYKcUT/8AtCeCllZqqh6b/ooN3fopj6oErMqPHzj6TPbj5w6pLWcZxnGcYsWEzx6QOvGynoXKp9DbaVrccUEpSlO5SiegAGSTxe3wef2cTxP+LzTVFqJvSlDy+0nXKSW7trGr/ZgcbPVxFPtVULTHQhsA9uOU88cEZlmcGtHMkgAfEr0AuOBzQ1+D34I2fHX42NP6bvSXUaKsLa9Q6peSkwLfTQpTUxAU8vY0P/f36cdoefvMRXPznHV6eNNR02idLULVVeaIjZSqpzuaoLQkAhKULLSnHEj/ANKnIEb+Nnw9PgiK+HHoXWdAxzp0xdr5rpump7hXN6VqF/hqdhTi/IaUatsbFLUlSipOdgEATxKLl8OOrtentS0Vv5/0VHWakuDtyqam56SO5LpbSyhKVorJbS00gJQIVGwkiVE8fPnHPFVuutwZDS10PhxDLQZBvITjJ6eQbjfmrb4FbTUEL5KuJ5e8gHDCcRjcgerjsfRV35p69qub12uNMxWP2/TNteNNdrgxLb1xfSAU0VO7EICJCnVwNghtMFRKOfvxW9MW/UWnrJdrBRU6KDTC1WupVSIKaek3KlLCT0OwlW6CopUYUQTx0o5h/Db5sUOgrVadLag5d6w0pbHTTVDGmrrUWu7KASC4w2Kz9z+IcVBUoveYC4uAFKChTbxaaC1FeLBXctr7o28aVrKKgL7dHXW4UYUoN7UopW1bi6EhJWpbSikhBVmTxO4Mtxt1XFLCdTN8kOByDzc8jmTnYdB9bK4krrXfLJUU8D2iQDIBGDlu4YxpwcY5nqfpy0ODxnG+uoF0NU8ysjeytSDBkSDB40kQBxfS+WiML5xnGDhx07pW4avvtHa7VRVdyuNwdSxTUtKyp56ocUYShCEglSiegA49wvEgbQVnAmOJZyf5Ea08QutKfTmhtL33Vt9qiA3Q2micqnzmJKUA7U/VUD68dRvBB/Zw6DQ2k6PmH4tdQP6MsSgh5rRVsqEIu76VAlH4x8ymlCgFHyk7nSBHpOB0k0FcLRyC0mvSnKDl/ZOT2hEIBOxtTFwu6QDDj0TUufd5SBk4IGUbibj63WiM/wD2PG2kd/UnYe7cpq4f4Pr7rIGxjS09T+g5n8vVcTtS/Ao5j8i+Ttw1xzj1LpDlfbbe1u/Zj1V+1Ly66RIZ8inlCF++9wQNxjEcUivKKZu5PponHnaQLUGVvICHFpnBUkEgEjsCeOjHx1vG7U8w9ZtcurTWqVabOomr2xtq3TBLpIJB67B7BKuhUocc4HFb1EnhgsNZVVdCyqq2aHPGdPYHl8cblCr1RQUlY+mgfrDNie5HPHpnZeOM4zjDwYQpbaR5dM8lxtSkLbIUlSTBSRkEHseLaae0fzT8enhgRdbdpq9apq+WVxat71VRMB96vbqk+mUj1lxoto3ESkpdBMKyqpVOje4AJJ7ACSeP0jfCN8Ko8Kng50lp99jyL3cmDeL0oABf4upSlakE/wD20Btof/4ziSeB1yqGwxh5GT0RO1Me+bDTt1X58dUaVueitR3Cz3igrLVdbW8ulq6KraUy9TPIJCm1oUJSoHqDwgqF7EnOVDMZKozx+jvxzfBy5ZfEErWb5cDWaR12ldKh7UVtbS4utpG1Qpl5lRDbqi1KEOK9SClv5kJKDQHnL/ZY+clHri5J0BqvRWo9Mbql23OXKtcoK/YmCzTvpLZbL6wop3IJbGwqJSCBxypLnA8ZccH1Rioie0YauWyh5iz0hXqHDnaqQ78pBSkAYHQ9BxcDxafAc8Qfg2pP2teNNN6r023TGpfvOlnF3OntxEFSahKUhxraT85SUKAkKxHAO0pylUUsrU2VJcG7zNnoEQSQZyZI6/z4PUZbLuw5CCyMcDhyiNo0uuqILgO6BuKkwACIAnEf114JfLrQRqbgwA0JWZBIIUFYJ/SYz7zE54X0el2mKkU7cpJwFggR8xIHsIEHv1M5jgj8q9KKdqmlhoBTywSIGxSfmAx6k9ukGftwettvM1Q1iiXeoZR0bp3lGXwt6+1F4Y9Zsaj0hWotN0ZpF0qXFtpfaWh1lSFoCVdZQSBgYIzw8uU5oKUbAnzVqJKlD1b8de39Dpwh0haUN1A/dmGUyVdAcAGZSIMgHE5jv03alqvwNvcdI2pXKQkpkpEyev1x3n2zxe1otwpoDJgDbn7l8jcVXd1bVCFpzv8AVDXnBe01QeZbw0UrEEnchMGR0yemPqSex4h9osL13Qp0JCl1J80nyEFSEKwkCBJO3b/dj6GCSFZuaNxsVBqbT9Hb7DcEawtIttS7WUKKp6kaC/MDlI6qVMOSE+tPXoQQAeNlLp9Fqs4QUwUo3kEJTj/ICe3XJ4V3Uz66pe93IbJvhrI7XRxws9oob6gojbU7Qhtax8hbE7FbsEDHtnAziR04gtyQt9xDadqEpUEhIML2kTtIHpgkDKgBKVTGRxP9enakhBUz5qISSU+nOEkmAOhP2TGMkRzTGkqmsvNoVUUdSlFWpDjClwkubfzflMyOhAPfInhbuNN+MIWpytVX/wBsahyhdFbheHypY2UjaiBPUAASo+56AdckDvxLuW9sGp9UCoS3NJRo2tAo3pMTIIESrBJJI6HPEXuaRRUrVuYIQViFKC/TnvPaf8T32jg1cttHJ01YmGXWiHEALcQU4WEAEqAI7E/b7iRx87FwDTIei/QdrcYj6nmplpS0bKNSCQ29VKKA2gAFInr9BA/UDsInLtVm56gfZpPSim2tBSQU+c4D6UqIkynuDnBOCkcO1KHbLpolDZXU1I8tpsYUpe2TA6kAwDIj0xEcHvwj+AC73ty33vVITbaBfqTT7VCrdUn8ym1DYnduOTkgkxkcDXzPEDvD3cULuNTBFOH1DsNH19wTRyp5S1qrfZtJUVKtf7UeadvNWyySlQXuWGln5cpCiUzMJETMHoNy55UU1F5P4lve2AlIQkelYCQACABjEwf5xx50HoC16OolU1uoKejS+Qt0to/ePK7FxRysj3JOOkcT+w0yUdABHXMx9uFqPhaKSZlRWHUW5OOhJ6nvjolS78YSzh0VMNLeWeuFM9IUiWktIQhIS2AAEgekdgO3QcT+kSGaIqCSkFM9MDHWPfiG6TZO5CAQUqjv/LiW3B38JbCoKG7aRPeOG1oAGyRiSdyhnzRr5W4ZPfI6cV85i1JLrkFUZk46e/8AX/g08z7lt8z1lAEkn6cALmDVkqcgSTI67v148WBCDmJWJhwbiBHUA8AbmBVS6uST2gmMf68GbmJVEFzKsDHXgF8wamQvsJwO/wBvpxxdzXdgQ31NUFCVSYVBP2P+/wDnxzq8Vd9/b3Pa/LCwtundTTI+gQhII/jPHQDW9d5VM8oq27ZlRxA/045n62u5v+r7pXHrWVbr323LJ4YuHo/M9/ogd/f+G1nqmvjOM4fOW/Ly7c1deWrTljpFVt2vNSmlpmUmNy1dyeiUgSSo4ABJIA4aErpkQncY6T3PEw5J8h9V+InmfZNG6NslfftRahqkUdDSUzSlqdcWoJE4gASJJwOP0F/2dz4Yng8unK67asOqNHc7eY+j1/8A9Ru1CVqtWnZC9pbbfbQHGiG1nzjuQdhgnqbg8vV6M0rdqrxC1GmqOxUl4oTaOXOnbZY0MOUltecCG6lLDTaVu3C5r2FCBlLKmGht3uqI273WC3Uzqmc7DYAc3E8mgdSTsFvDG+V4Y0IC/C5+B9yw+F/oi36o1jb7NrvnU6yH6i5VyA9btPT/AOlSoVKZT0U8RKjO2EwTZ7W/M6o1nXzV3FyolSdqfMKYGRhIzE+5Huew4kWnfCLzH521pu/MDWddoSnfV5iNPaYTSv1zIIEfirk824C6O6aRttCCIDrkbis1X8LSx3m1uJs/NLnJYrgElSH16iTdmVL7FynrWnWlp6ykBOFGCDBHzbxv9mHG/GUpqq2qZBF/RDl2w/8ALSCC7vzTlZ7xabc4DQXu6u2+mUF7jSsXVJpnWW10DoKahLid341JwQruUQSCJgzHvxDeXaqSt1rebRcUuKRpu7NUDXnTscpFtsPsrUT85CHQ2pUgSyQYgkxDxEo114FdZUdi5pJsT1s1C441pnmDa6ZdLbaytSJapK6lJWaOocHpIQtbTqd22CClMIrdSal0xaqXVdDS1NDX827wu33G1vrFWLSqtpVtWqoJQoplgUzfm7SUrFSpUEpkUZL9ltytE76G5uDM40Ho5wOolvcFgcPfgHfCtOjvcNSxs9Hu3fPcZ23+OPgkdFea5nkDyp1ZU1blJcL5U2+zXWpXITc6iqbX5D6uxdRUBYKiJIdWn+4UyPU3MVetdUXfTGqbNbtXaPbttK67p25tIeaD1Q68S82tQ3tOJQjYnYtJxJ6g8D+6cqaWh1DYeTbNW8NGaXbt2r7UtTZ81inYS/R+SFx/1G65IqgokH951O3iOWzmLXWS43fmPfnk0ptFQ1pK/sqaCG6ZFE+4yutAgwBVOKVAx5SzjATxY1OZo5BVW2bDsEtxts92YxjuQHDHTAHZH6Olimj8CujBbq3z6AB2T6HBVFPHZ8JOj1DRVvMbke5WXGz3AKuDmmqtRNW2wokhylUfU4kAZaX+8T7q7c7q+jcoX1NOtracbJSpKwQpJHUEcdqr5qXUOguWq9K3J543K2Uf7Ysqiv8AfW5xDoXUUylDbuKPM2DP7xLoTBhR4rZ8QrwhWbnzWM6u0ZTsUGqq1lVRU0ySltF8bSJLiEYIeQgoKxHRRGSmVfR3DHFb5AKevcCSSGvHIgcs+vy3CQeMvs1dDCbhbGnAALmds7HT/j12VE+RHIjVHiS5oWbRujbPV3zUF9qUUtLTU6CpRUpUSY6JHUk9geO9vw+fh66J+FxQUluslstvNXxLXmnC7tcS6lu26Jplg9H4V+Hax6nkAuvRtaABB4DXw5OSGn/A7yLeqdP1tqc19f7YHdTave2KotJUi0qJS0pR2uuqSYGxQSSnapQR5hJ9tdZp+1aGW3XVVzs2lnAK+uonaxVPedTvKSD+Lu1X6VU7aoSQygpcKYEsNlLZh8Z8XOhY6npSOxJzt8tzno0bnrgbpY4X4RmqXiRzSSeQx9Tnb4nYeqnlTcm7hrV69Uda5za15bXlJrtTVjotumdJudTT0c7kskSQfLD9YpJ9W0GEwDxD6vZ5Kcn9QcwNV301wtdOp1hNPR/hKU1SlHyVBC1Kce/ed3FpAAPoHTh9t2v9Q67s9E/Z9PPUtkoGtltRT0jVrtTDKiClNN5m0BsyVb2W1zkkkgnjnz8eTnrX2LSVj5dPV7VZUXB5N2rFNhakJayhgJWsAqO1LhKgEiVkQQNxpyw0tVfL9FRkaWA6nF2NZa3c+X+gHlsAd+ZVtXSWOw2WWYS6pMaQG5Lcnbd39RHPY425LmtzN1tVcxda3K81jrrz9e8pwqcUVKIJ6knuTJP1J4jyu3HwmTxnH1kAAMBfMhOTkrOM4zj1u3K4xeKynwlvDEPFL429KWurpjUWKwO/t67AiUKYpyFJbViIcdLSCDEhRjPH6UdH20sMJkSVdf8AuUcmf1zxzK/s6HhXGgvD9deYtfTFNy17VeXSKUnKbfTKUhMewce81X18pHcTx1V0pQFK2pE9usfrwo3mo8SfQOTfzTVaINEOsjcqUaUoClaQRJjaRHX6/wBe3E4tdAPKSkjdtEYHzD69+GTTVv8AIZSSANuPUeg6/wCPEpYbKWikAgDuR0+n04E9coq5an/QVAbkkgjcR0kRBHt7g9QY44c/G08PeifDx4pG2tG25u3J1Pa03i425ojyaOtcee9bSD6kIcA37EQhKjiAYT3EqiYJ9+x7/wBTwJPE/wCDLlv4vLEKPXGmqS51DLP4elujMsXO3J37/wBw+n1oG8TtykyQQQTJiyXIUdR4j8lpGCotTTCRuOq/OXp3TYulfsU6hCSNsnJjcEzgZBJ6xJjpBHFh7PyqpdOt0rrN0oruiupW6pZpyT+EcUsgsrSUg+YA2Cdu4bVIyRIB18YPwravwaagYvdkertTcv6l5tlFbVLbTWUVQ4CnyH0tpTO4oG1aEwcJO08C63WlilrENvuM0wUsIUokhttEiVKCASR3hIJABgHpx9M8CW+nqacV8bgQfz7e8L5l+1ziuop6r+G6SMDP+/il1kpaa16dfK2Q+66kpb2kpO8nqRBnEiP+76Dgfa7qnKt3yQQsKMBrIHuAQBn6D3PXHEn1TdVgNqlSW0wWxEkjdg/rjqR/KOCF4fPBpzF59MuOWe2V9bb6RxT6mQgusturSncokQCtQSiYgwlIMxw/XKqi0CHUAOpVG2aklEjqx4LnZyBzQ0sPh2csXKCz68dvViq3L7c6i3JtaagKr6ZTISrzVskHa2qYBkyRnrmOajo/w6AtqNxEpkhQVn8wz6ZSQPf9ODHzL5FXXlLf37fd6CooKpBKVpW1sUpJ+/8Al0n3OYFzR10y/wArmdOm3WptdvrHqtu5IpCmuqA4hKS0t0mPLTtCkgJBlRyePRb/ALvSgghwJ5jHL4c+2V2jvBrK3SQQRtj1/T3Ku/M15Lz7yd26kBKQp1X5PUcqJkSYMTJPTrhn0/qyoeuzFTUun8SyEtJRvO9RwrcsdeoGDIgEAxx55lXMtLdO5IWk+b8gWFEEQ2nESoSCYgJBHeOJX4OfC9qnxk85qDl7o/yarUl2feXSuVLqqdlBS3veUqZhITu6ndEEdSOKhr6sCtc89Ff9uoj/AA5sY6qE8mtMDUd+duDyZbbUtayrA2piY6eqSBJMkkwCeDbpKiVd7gpa0hLTbMgBBAS3JVuHSFYUYPecjpxB9L25rS2j6Gkwkup854LTEJE4PYerdERP3PBD0Zbi5YyFpC11SkKcEKKtoAhMATMgJJAnJyYnj5mr58N0jkv0Up4iG63cypZpDVCrBzEst4/CM1ot9Y2pukdTvbeCSFKKhHQHb75STMZ46o2DZXUzb7S0PIfSlxDqTuDgVlKp+oIP68cpdM2n9u6lpkKAhs/u9qASIUComJI3QAZ+sdBHT/k7zMs/MOzIVQXGjrq+mShFe02TvpntvqSUnIEhUdcDrngfRVjNZjccHZIXHVE8COZoyNwUQbVTkkAyT3SQMcSayMbiBBBEwY6fT68MVtp9wSY3IAg+/wBOJTYGyXQVBKQCNuevt/Offguq3Dt91NdK0x3oOFGYBnh21bUCmtcEqUQI+/1406YaG1BMgDrGccItf3A+StPQA4J6Rx7nZYQg3zJqpKzu2ggid0gjgEcwKmCrqCCTE/Tgw8xq5ThWT6SJMdZHAJ5gVilKXI2yTgfw/XvxqV4EHuYlf1ncpJ7/AF/y4CWtnt5WI/Q5A68F3mO8ApRII7Ge/twFtZPhClhWQkHA7jPHE81JYEE/EZfzpzlffqyRLNC6UmYklJA/mRxzoeI3QMxxeDx56gNr5L1lOkwqtqGWBB91bz/JBH68UdJJOeG+wMxTl/c/kla/PzMGdh+a+cWf+FzyQvHPbmxrS1acc8nUw0XcxZlpUUuCrcQlptKCBIUvcUSP7+MxxWDiw/wwvEsjwu+Lqw32pqGqO23BDlprah1oOop2XoHmFPfaQlUDOMZg8GznGyBrr18EP4cGtPCtZrhprmOw5ZNUc5WA5qexJfgWHR1tWl18vhrAqK+qcaowmY8pT3UyB1G5M64oudPjXulBX0bb6tFacpr/AESSr93Su1dTU0rSkIGJQzTuAE/L5x6EAisfgc1I5qblNqvmnUKIc5m3T9l2ICR5Wm7UpdLShufUE1FQKqpM5Jd9UkTxLvDtzcoeSnxIrA7fHk0dp5r6WXpJqqWYaau1FUqraRlap2p85h+sCSeqmkpGVDihrlxvBW/aLScOE+SEOcexlLSWj3tH1KcaOySNsU1xxucAf8cjKJ/xxvibPfCr8C1drmzW5i5awvde1YdOtVDZXTN1bqFuF50CJQ222o7Z9SikdJ45H/Bn/tM/PbmB4/ND6D5x6lpNbaR5oXpuww5aqekfsVXVOeXSrp1sISVN+cW0KQ4FelZIMpk9u/iWfDr0Z8ULwq3PlhrR+ut9NUVDVytl1otpqLTWtBXl1CEq9CxtWtCkKwpC1CUnaoUF+F9/ZatE/Dv8RNu5p685gq5k6l0s+qt0zQU9t/ZtFRupSQipdQtxxbrqJlGUoQqFGSBF+gZKSshrd1dX4wmibNzN8CGqdP31DaaK+LYoDUnL9A4txPlvs9/NS6GymCM98QeIfh78Y991tyo5fs17qVv6MurNxvrCDn8Hb0OIqnyfTvdWHhtJ9P7tWSQTxfr4x/j7tV00/W0NruS3KaxsVDC3mKgJpaBQlTlQpZw4sNpbSIAA8+ErO8AcafDNq1y16NcQ4HwjVqXaS4hKyV0lG84POdSZClFtK1AAAlSlxhU8I3HtqgqqVnisDnA7Z7Eb/RWX9mUrzPM3VhmnJ94O31XSO485UWzmzcdVvJpm9K1tkYs1nuTS1rqK55l52pWtpE7Q09+IUGNol38KDtPmI3ijVnMGsHha5nvXyiqKmvrbhcUVtnpnBUfjXayvbqaUpWcElDqIHq3KC0+4Dhz55k2i6VvL/Tool0IoLsLwloKS8KNFJTqbpAJPd1xIHpElHaDwOqu8ruviIFOXwmkobPTvVNMXUJcerC482w4Oo/dtKfBAwkutkkEA8UJZqKMxsmdFpwGuH/GJ2lnxIzkjrghfRFLZtUbhr8xdo77vGXH4HBHpkJz8QGu6Gt1ly9TS17VQ5clXK5sVBpFKbfS6yktpSRAglSVpJ9J8oRkRwL71q2psGqr/AHCjqi2jRz7tRQNqdAcO9LT1W0TO1KTG3CoClnsAOI5cr26nQlS9StU6Ljbq1lq1OO1CVMtUdPVksKnuHCt6E90JQIIE8DbmFzEqWKS+W2nrKakoa9hhyscdIcefPlhBaT3UpxQWpcGc56DixLTZ9DfCiOdPl3/5ZJ+TsKRV3ZrY9Uw2dh2239JAHzbq92FZfkpq4AVQpUsKVYrj5tGuqrEIttv8weY26pMldU8EKSEOEKSNpiDkG3Q2qbXqStbr6Wkd13d0K81C0U5qKSle3kqWnzFClQoKzvcU656vnJEmk3JvVS6jXFxLVPZBTN2ylpUIultFc0ylsqI9E70IG4wtcjcPUoGJunyT1BqvUtmpqpik0XeKVgSPwtzqKNKiQSRhh1B3RJAUPr1EL/GWumj1OOB6u08+hOM/IhCrPDDNKSwDGf7cj34yAfiCixRDVl4X+Iut5ore6oeaW6ZZuNUMTCnFgNJIJgwlQ9Ig8cf/AIz+r3tReNu/0iy+W7ShqlAWsqAUhtKDEmJISCYAEnoOOwFk11qGzvJJ0ZVOFIBCqWvoHyFEdNqnkq987MyffjlZ8cnk7c9O+Im365eoqygoNbUSXE09SEpcpn2oQ6ISSNqlDcCMeqB0ngJ9i0zGX+USaGl7DpDSD1BO+S4nHdLP2zNe60RBm7WvGdsdCBtgAD/KoxxnGdTxnH1QvmNZxKuS3Km5c7+a+nNH2dHmXPUtxZttPIwlTiwncfokEqP0B4ioE8dHv7Ov4XTzH5/3vmRXUwXb9FU/4GgUpMg1tSkhSgfdDAXPt5w9xxxqJhFGZD0XanhMsgYOq7L+HXlJbOUnLLT2mbM0GLVYKBi30ifZplsIST7zBUZySSTmeDfpe3FW0hJATAH0+n6jiJaTtstoAEiNsEdRE/19+CZpW2zskAnocZPCE5xcS49U8MaGgAKS2ei2sRmD0MZ4WrUWxG0wnAEzjv142UiA22UkwYEE9DxoqHdoOFJAke0f1/PjxZndJauo3AkZgAmfvjhMpYUuB6ldJ7/+ONlQRtCphIO0YzGP8+3CZxwECQZVggdgR/XTjFshF46+SNf4hPDVfLFafPcvLTjNxt7LJSDVvsqw0dxSAFJUrJPpMHPTjk3zs5Z6q5D379k6pslZYq5KDsS+namrSkgktLHpcSD1UkkHpPy8dvC6CqCQVewgkf19uKu/GB5f2PVfgd1Hfrt5rVx0SW7lZ3UO7CipddbYU0o/3HUKKT3BSkiCOLP4A45ntj221zQY3u59QTt8lUf2k/Z5T3cOuzTiWNvLoQN9/Xnhcr2OYRRZ0Wo01IaVdWm4B9VMDVgttqGxLnXyhO4pn5gk9Rx3i+Edq/S938Idkp7S5Ror6TcLghJCXC6TJUR1gg4J7cfnas9yXXXkJSlRB2rEgJKjMJCgZ6iYH/dAAweLBcq+d170Cyr9jXevt6lgb1U7ykFQP97aYEGQB/rxeNTZxeoDA6TQc5B/RfPcd6dw7UMqGRCTYgjOPkV0B+N7qDTV35g2elo6mkTcGmFfjHm0hZbnoDGScf1HHJ/mtdgt1bbTiVNg7FGQBnqP8sfp2kic0ucFbqB916uqah59353XnPMWoSJMkxGZkmPr7ibRXiJq+RHOTTesqGit9zq9NXNq501JcqcuUj62iTseRAJSYGAQRAMzEm6yWO02plCx+ssbjPLJ/RA7HSTXe7vuUrNHiOzgdB+qEVv1s5p3mdZbq3S26uds9e1VttVtKKqkeWlSVpadZV6XGiQNwO2QVAnMj5o/mhe6bmOjU1juFZpi6P3J2pD9n30QtwcUp0+VsJDaUBSgET6QkGew2c9OcNbz35yao17cqG2W+5auuNTdaiioKfyaWlecUSUspP5QrEZJ2kzKiAi5e2ZSqlLiQ0IAQS0BJkhRUTJzBJKhmAfuKiDXTz6upX0GZGU9NjOwRBdV+0ri6SC8XloZQpOCR0In8pxtzJ65HXgo0FEinoqSmklBBcU4AfUSRhQED5eoJJGBMEcQPSNnLtT5pQ4l2llwyNqWyEkAgxMAGZ+8dI4LPL60J1FrunZKXFpQhCSUpgLSVEgKkEgfL0EkE9ySfmS6zCOMuX6GFupwx0TzouzK0xUs1TiUtOMtqdcC1BMFIJAwOgO0wR3yMji1vw89NKt2ibheXWwhNbVKe8wABS0N4iR1AUVqE9QQOkcV/wCYun10FYhhhohx9SUn92FFRJkiCPoY9uLjeHfTqNJ8nKOnRAWpjarakeoqnMdes/ftxVF+vD4YWVEftagR/wDnf81Du8DJKYxH+rZHGzOprGkLSc9Du6p79P17e/EusLMFIAgq65z9/txENNPlavLTtKgoJ67ZIAwP66jia6eaC1HB2kiYESf/ABxbtguRr7fFVuxlwGff1VAXGmFPUvhHIHbKnenGSGU/Yyev9DiMcyq7c2sKSSlSYKZifrxKrQrZSlRwAnH3/qP48D3mZXeVuC1bZkhSRk8GCMKGgrzIr1FazIjJKZ6n6/b+p4CWvK30uDBnMESBwWuYdZuU5hQGQATAOP5cBPXlZCT1iSBBkfYcakrdiEev6oDeScqERmf04DGqKhT61ykpiZEzGeCvzAqT6oCUk9ft/hwIdWvyXOwEzHHAkZUgclTr4jV+AobBbwoy5UPPqHYhISkfzVxVU9Z4O/j8v5ufNqjogvciioQoj+6payT/ACCeAQcAcPlqj0UrB8fmkm6Sa6lx/ey+cFXwfc+qfw5c97Pqep0xo7VzNOFsLt+p7eK23KS4NqlLbIVkJKgFQSndIE8Crj0lzaoEdeJssTZGOjfyIx+8KFE/Q4O7LvHyK+Ira6mistt0vRL0Y1T06U0eg7hWJqbQ+yBO3T9xnbIklNI8qCSQkMmdx05jVmmPGbyPXSitqG2qsIqKSsYJYqrbVsqlt5Ewpt5l1JHZQUkgjJ4/Pbyi8SeoeVja6Jl1Fxsb42v2ysSHqZ0T/dUCAfqM8dBvCV8SK2DTxpWXmGk7T/ySoaekJTvUB+ZwjO7cokNgkHCR80/aL9l07Z23m0gioY4ODwd9v7jzPoTk8wSdsfQH2f3e21zDQTvDQRjQ7YfDpjuPoraaY+NXzu8KVwRpHmje65X7PTsor/Q25usbuKAISp+nCkrbWIBUW/MSds7EZBZOfXxnLTqzRtczduct3uzD7BXUUdnsVdR3J8mISpTjSW4SZG4ux+8VDaYHA85l89NI839HK/GP0FRSOIEpLYedWYmUAkyUxhSyJzEJTxWbUfIPTd0uaHbay9TWxQUadK3AfPVuPrCykySCPlG1PuZ28WJwvx7WS0obdIS2QbE42Pr6E/JRb59jdO6ozb5vKenPHoot4hPFHffFFX09ls9Cuw6OafH4el3y9cHC55gW+sHapQWpS8EgboJgRw+8vtPv6dondMUzqXr7dqFpoOhcppi688HFbkHcGkojqQVLHX1SG+7Wqh0s7VKtb7XnMMLqKl5tnc1bkJz+HVuO5biwEpOQUhU4hIU7VXM5rQ9jq6G3viovlxShb9SgBaEq2QVelIXIIhDYgI27oElSp1zuE9cWkDIPIfLn6DnjqjVj4corNA6NjvP/AFO235jA7nPy+iI2oNein0lYKqlfqqvVmpn7fVioq0fvSlsoKkpQ3hLDSCoJiSQ4clSyox/Ule/pLWFPbrdVebX3C3u/tCuckqbSXmgmpd6HdIcCEAAAFCQNqVECms1G/pu1UiaAIbqyG6eqqqxakIYDaUqSnsIjbtQJCtyxJUkkQ2+8wnHb1UqbuDrpUhbD9fUNesjqNiB9JAzABPWTMKmsrg4gct+fryHuHQIx/G4YWBxGCC3ryxz95PUp95l8wUpstdYbMkOUNPU7w4k7UoQh0LayoZ9YWP1JMdwVzW5khddVUdIpC3XPRU1KZyO7bfYJnJV1V75jhPr7mgl2lfoLctwsrqC+pZcncqACon8yj79oEcQF14uEkzJ78PlttrYmhxHr/tUrxVxY+peYYHbctuQGTsPmjL4bOe7WltYUKLrWP0TjTqfw11bcUh6njolagDKO5BSQYEgieOj3Jbm3alM01VfWnbWdgKNS2N00fm7xhx/y5CAr/wC8lbS4gED08ce0uwODn4X/ABpXTkjVs0F1TVXbT6VSGkvlL1J9Wz7dik4IJHATirhZtyhJj2djpsfhtj4HI9EX4B46ioJBSXL+WeTsZx7+uPUEELtNYL1qVmnbdor5p7VNC+PMQqqSbdW7SAU/vGUqYcGevloEdjwMvHbyHuvi88MN3047pWtF8tSnLvZ3GKynqQy82j942SNiyHWwQAEH1oRgdeB/4ZOYNu5l6ddv+gtQPs0aFE1SaJAU2ysgqUH6NYLSP/c35U/9xO7g/aS5vX+3NJLzFvr2krDf4y0O7XoIwfIeVInodjqhEfXj5hntdbY7syrp2s8SN2dwY3eowCGnI22znsvo26W6ku9rfEx7nxSDYtIkb6dC4Y9QML8/lxtz1qrXWH21NvMqKFoUNpSR7jtwnJ2k/Xi6Hxi/DHT8uudC9dWChqaXTuryHnkLpVMppa8gGoZB+VULJUCkkQtOSZ4petMAcfYNpuLK+kjq4tg8A47Ht8F8V3a3SUFXJSS82Ej/AH8QsQjcZM/px+kP4S/hXT4XPBzpDT79MKe9V7H7YvJ2wv8AF1AC1IViZbR5bWRjyo7ccUfhLeGMeKTxraXtdVTfibHp9Rv12BTKVMU6klLZkQfMeLSI9lHtPH6S9DWg01O2CMkSSnqo9eB99qMNELeu5Uyy0+SZj02CmulrZvIMbUpMgDJ6TwR9PUgQhO35QPv+v+XEX0pQEKG7H5ukjPE8trAp0hIHUYxB/wB+FtMHILetSUMkgGMGPcz2+vCOrcSggqjeD06lXbpxuqHcQcycfXhvefBUgFQIEp3A+x/yIP8AA8YvQFrfX5aFkSpWcpyeEjziUkqAhJOfr7fy/wAOPZqSQTJCgqDGRMx/p/HhKpYVux0zE9/6/wATxi9WLcHmTKSYGU8Cjxpch7h4p/DrfOXlDdaWyJ1K/SMV1yfphVKo6VFQh54tNEgKeKUBKJKQlSgqcQSiPSTgp6SOPO6SqUn2gZgxn+fv7cdIZnRSNlZzBBHwXKeBk0ZikGx2PuXFXxifDt1R4I7tR19TU0t70neKhVJRXemSsJp3sqSzVIIlDqkgqEShUKCVSOD18IHwJWHx1aw1AdQ3SoprPptltZYplDzqlS1EAiRCR7kDrMTPF+fEvyYpfEV4f9XaKrC0gaitzjFK478tJVJG+mensUPJQZ7Z+vHFblD4huZvgQ5n3hm11tz0nqu2VDluutPuO5hxEhaFAylSZIIOQU7SDBB4vbhTimrulA+DWGTN68tu6+fOLuD6O2XCOofGXwO6c9x0z+SNHxivCPZ/BjzzVYLJdDcKOqoWq1lpR2uttqO0IWBgqESCBnHfpz61/eUOIUGNi0oAHykIXgqCoEAZExnqSPSCSWvEt4kdT8+tT1uoNUXGpvV5qv8ArVDrkl8g7AMDoJERAxnoeAlbrHW621RRWi1sfjbjcKlqipmicvOuqDbYkwJUTAnoTkTjg1cquVsQjmfqIG/v7ofZqGB0pmhZpaScDsFqs1uVeHGmgylaEvwd6VQQVfMRIwJ6D+8ehzwRLXZW7VZnahSQinWmCJA80bjuCo6HP16+2OF2n+QF45Z8wrnpXUtvct1/0zWv2y50anEOGlfZXtWjehWyApJGJBKSSTAIkOuaM0tChmnn1KKvMQnO4RCoA9zJ/U8E7NbT93NU74IVxBeAKltFH8U9aZpk0dkqn1OFAqXyhhs+oqhQTuzncAe5OT9RwQ/D80A6/Vl1QDK0pSFAjcJ2lQx9Osk/URHA6vDiKHSbCioAs0pWifQJ37cyCRlPTESZ6iSlyOYFFpM71FDiUhKgo7QMggDsEnORnMY4+Kr44mmPyX6gwnz4PVF/mJQN12t9P71BtAp/PUsyNhiMx7TJMjp79bSaCq00XLyhSClKFRvJUBtAAP8AH/HiruplqpqHT9d5a2iwgMK2+k7SDKZMgjAIEY+gODbozVoodE06QQqHxOzoQoHA+n6+/txTd2jMkLB2yudbEXNbhHi23FDtuSoYU8oFtREbTM/+P58E3lxdkXylQsEhxPpcBwQsdf8ACfrxWzSuu/MtRaW6FOU7oStROQJHqOfePoJA4NPJav3a1LaV+isbUtJMhKhAUQPbI9uivrxP+z27zUFy+5u3ZKQPce4/JV5xTZw+nM3Jzd0b/N8q1FQlICQCdsGf9OBPzLrlBS4MD/t78FC9Pli1kQNxGQO/68BjmDWpU4sGREgGc/19OPoRyq5B/X1VtDhMQcHBnrwENe1SklYiD37n78F/XtV6FwQZ7JBmOAjrtz5xt2pggGf1Mx/hxzet2IS67dBK8z+nvwJdWvShZHQiff7cEzXdQS8sEgyPfPAo1lVCmpXVqI2hM/aDxxA82FIacBc6fFHfDfeemoHBBRTvppkx7NpCf8uB6oQBw5awvQ1Dqi51wn/nKt18A9YUsn/Phs4smFmiNrewVezP1PLu5WcZxnGcdFyX1BjhVbbvU2erQ/SvOMPo6LQYUPp9uEnGTHGEZGCvWuLTqacFFjR/ipvdlfQLhNalIgvNlCKjqCCFqSoAyJmOueueJbUeKFnUTDIdWwyVKR5ilsKD6EJIJQlU7BMGIAAJJiTxXrjOB0tqpXnVpwUz0vGF0gZ4fiFw9VYu58+v2m/SU7VdTfgaVW9tlDAbb3jMuL7jcd0ASSJP1fOTN+a1XzKRb2Kxq5VQbcqhTBPrqwhJLzKASPMUtsLKQTKlNgfmg1Z3H3PEr5Xaoe0/qWhqaWrcoq+hqEVNHUoVtUy6kyFT9CB/rxxfaI/DLWbHBRCLjSrfKC8dR1PToF0dofCjatXUFtRUVhZtt4Q2vTte2wX2KlJBWaV5HymckAJlxJlKt4PDFzY+Fc47ZH019dU6SbSUpRc22V1dnKsk+Y+lJcpxOQh9LYAVIUoAKVKfCp4rdN6otNVar8xSClvXrudqed2NsPk7lVFCkn1NqMOKZQQ6yrLczAuDy25iX/ltSUj9rr16206EBluo/GNtXVtHUoLqlBFUAn8qy06IyHD6lUtcuIbraajw3jkds7B3ucdg7uHbHm3nhXjHabdeqPxof6huN8j0IBzjsR8eS4j+JjwZa38Ltxb/AOIKBuptFZ6qG70LoqKCuT1BbdT6TgiR2kT1HAjXx+iHXlLyp8UOlrnYtUaXsi665BSHGallVoubTzgIDu1SUeY4BmVJWSIExxxE8cvhFu/g85z1Onq1Lr9rfBftlcUFLdYyfzJkA46QQDg8WPwdxnHeQ6CVjo5mDcOGMjuOhVG8XcHTWhwmaQ6Jx2IOce//AGgvx6AgzPHkiOM4eUkJ70XzAvfLm/M3Ww3a4We406gpupo31MuoIMjKSD1gx04t5yV+M7rHS9O1S64sds1cylJSqvYQmiuKiZ9bhALTy+mVICj3UTnilHGcQa+2Ulazw6uMPHqP3hF7Vf7jbJPFoJnRn0P5jkfiul3Pv4hnJrxa+G3UGlq643nTdTUNirpae6281CzVNhQSUOsgoTM9PSPVJOMc262mTT1S2krS6ELKQtOUqAPUcJZPvxNfDvyaufiG52aY0TZ0FVw1NcWaFCwJDKVH1un/ALUICln6IPHC0WeltcJgpBhmc4znHuXa+8QVl5qRVVxDpMAZAxnHLOOq7C/2dfwrK5feHGv17X0paufMCrCqcrEKRQU6lIaiegW6XV/UBByCOOqGk7aEAK29hIOYHv8Az4GHIHljbeWGgrPp6y06KS1WSiaoaRsCNjTTYQge07QCfqTwctKWslQwIPWRPaIj24WayczTOf6o3SQ+FC1ilGmaANpHeI6p6/78SQJ2NQSn9ex9/wCXCG1UvkMiO6Ynv7cb3FHO2M5A6j7cRl3O+y01dQQYB6HP14b3VAiI+Yjr278b6pwLRIJAwQY6cIKp2FEHKTj/AF4xerUp4lpUo2haiSJ6fc8aFwXDJAMRMYOO/Ht1SvLJ+YgbQZ+b7+3Glbgnd27mOnT9eMWLx5hO0x95MRj2/wA+PpHZU4yZwM4/hx9bhMQlIIJSABGf6HG1tuVoCZWVkAEZJVgAfr9OMWLW7ChEgk9onuc/bjmF8erkto/R+odNcxqa9Wq26xvxTRXOylyay7MIQQ1XpQJIS0EpbcUQlKgluCpTZB3fEc/tENh5I37UGhuT1tY1NquzV5t9dqK4tpesbBQIeFMhC0qqXEuDy95KWpSo+sQDyB5leI3UfOfWtz1Fqi+Vt61Be6g1Fbcq1zzXqhcRKowEJGEoSAlASAkAAcPvCNBUU1Q2te7QO3Uj17D6pB4tq6esp3UTG6ievQe71Ul1Lqv9suKIX5mwFIMBaIEEd8wAE9I3Rk4lnt77rV4SptflpWkoBPpCiEgNrIBkK3QoREKJ+U5EeRqRtdOpDjnluODJjcQIkiQZ3YH1yf1eNCuivrVVLzag2QIBABSD0k4HeJgkgACSOHaSd1RLg9UnxUzKWIkDkj1ywcXQ0UVClF/aFuLUsqWJG8kqyonr16kmck8fNc6oF1fSy0ltI8xW1S46TtVuMwrqnBPUEDBkR1vU6qC1ja6t1apWSVYHy+r5pPcGcglJnsNVvq1vOB8mdiQoAwkgRjp9BBiB1kiSRYLK3RTNpo+WFWD7YZKt1XN15IoX9aEP/h3VKCHmCyoBQbSmDhIUJISPT7kp29eC5ycbcc0r5flqUVgZ8sQralIwfynvBiCQOAvzGKkMVDiTsdYIMyYQCYz9YUOsRHYjJj8O1zTdbW6IQpTyErATtIKtqZAkQcj7kQfrx8RXuJxoXPHQhfqiHATAHsjfrCnbPLQqT5inKZttYKjs2pOYicDHbr9usl5Z1hvGkU04dAdCN2xH5T+YE9zg9v8AEjhhu7Ip6FFMobjVUwSlREbBtgdO5wCc9enDDyN5gtaWrmqOuUlCWXw2ggE7wOuVdITHt/gOKpdA6Wmdp3IOV0kHkIG6JbN+f01ekKWFuU7raGHVQJP93+G769Rnix3hX5hsV2pre0/UJLjKi1uWQB6kqnJ6dsH37Y4AHMSzsVd5aYQG/wAPcGw5TlWRkT9ZPTB6fYE8RG2avvugqw/hy+8hjdsQqSomflgmZA9p7Y6ngdRksnjqWDzMIOOWcKDW0La2mdDnBIIXTbU90bqLckNOtrABnaoK657cBrXlblwzAJMgmM+/+314B3KDx4MW+qprTfWiyw6pDRqSDsQpRA3E9SnM+wEx14GvOT4ha6zUFYzY2KY0LClpbqFNqWt4JkBZkgAH2A6d5ni6qDieKen8WRpa7kW8z7/cqjl4GuQqDAwZGM55BEzmFc2mKd9xxZSGwSo/3T9/1A/24CmuLuzV+aW1ev8AOnoR7fX/AF4GeqfE5cda14bqXXA3JQlCUwlSgPlKB3MxP8O3EXr+dSKepARVtthJkgmUrAHQHBInA+ggHHGG9yOnA8P8P6593ZFv+gXtpS50g8XmAOXuynHW7+1xUiDJx1g+3AF8TWpxpblVqCskpW1ROxHUEp2pj9TxNtceIG10bSVfu1KcI3IKpJSRMgDMR/DuBHFbvFbzVHMzlJerfa6aqW4hxlQCGypS2A4NylRMGR0HUH6Hhkt7RNO0DlkJKuduq6WmfLLGQACqXnrxhOOPTiCgkEQRg4yOPPFjKsFnGcZxnGLFnGcZxnGLFnGcZxnGLFnHtt0oWDnHtjjxxnGLESuW3Oz9ksijuiPNpwU7XdoWREABaSDuSPtPT24Odj8Zus+W+l/2roDUwYqKUeXVWyoKK1lTZHzMhwFQjumYxgY4qIhRT9uPSKhTRlCiD9OB09rp5j52gjqCMg+8HZHabiCshi8NriMciDhw9xCudZvjmc1qK1N0lwsmh7yhhBS3+Lt69qSRClbUrCTPcRHsOBV4r/iBX7xgWihpNRaW0bak2tJFIuy0SqNTZJkk+ohQPtAzngBEcZx5S2ehpna6eJrT6ABRKu8V1U3TUyuePUk/mvvU+3HzjOM4JIas4zjACo4BPG6konK2obaabW864oJShsFS1GegAzxixatu6O3HTf8As5PhVVqrmdqTmncKYKpLA3+xrQtQ61LqQqocT7FDO1E+7+PlPFH7N4KuaFy0/wDtmo0ZebNZQf8A+43lr9mUny7iQt/YFCM+me3Hen4P+kNCaH8IWjtL6Q1Pp3UlfbLczW3tVtrEvrRV1X751SwPUn1HYncBhtIMHHAu61OiA6OZ2RS10xfMHOGw3VvdF2bY03AkmBHY9z/lwT9MW/y20zA/KcjrHEY0pa0nbA3RhWPt/lwQbLR/h2oI3kZBT+ccJw9U2FLkNeSJyFEZUR0+3CWtd8segH0jcAO/fA4VuKSlKCJgGUn2PDdVukpmRvBII7q78erUd0jrHvmUEqMgnb34SOLIMzhWevQ/XhQ+AlaYVOZ65/8AHCdTQKldNgUADH1/1/z4xbLWlSnGh8xBkHt79OPi2FdBk5jEAjjalpSnEhCN6yQkJTn1GIH8eOdGuv7R3yp5Xc/OZ2kb7p++XC16NuRttgvGnlorBqBbZDdQVBxSEMpS6HNqwpQUlHQmJk01HNUEthbkhR6irigAMrsZVqPH542tMeAHw43DX2pGl19QpxNustpac8t69V60qU3ThUHYgJSpbi4OxCVEAmAePPMH+0ic9NfckNY6Yet2jbNdtSuFuh1DZWHqSssdIuQ6w0grWhSyg7UvqPmIlRyogpA/xIfiYaz+JFzbaut5bFl0hY3njprTqChaLS26ltK1OOpSkvvrDSSpw4EbUBKetbKt/wA0bUjpgD+97/r/AL8PtosMUMAfUty8/RIV3v0s0+imcQxIn6gMogJ2hOEgdEgDoJ+3CJFQpo9MpwOk4mfv2+g+vHuve3O4kJmR6ZIHCo6tYotDv2gWi3OPO1qalNxWhRq2kpQU+SnO3YSdxkTI4Kyu3wFBhj6lahfVbg2dq21TtBJJ69T1zj/DgpaQqbezYqCqZr1VNa+t1NVSrZWgUTYUA2SsHavzEk4T02wT24DdJUulBaOW1ncv0gkkHGevvAnOOH2zV5p0ADYUpEKJGTt/h/A9f8OlLKWyalrVwB7NKONPcnL1/wAs2VPKcSAkyVKV2Bk9gO3YIP8AeMze76Xq9Ns0jNdTKp1VSQ6A60tr8QJwRuj0jaqCTEhWe3An0RdQ0EKc3gEgLK1KzkCAYJ3SSQP+2PqbO+KLx7am8ZFr5eUWpLZY6Oj5cWVFioP2VSfh1VjIWlRW/mN6gEAhJ2g7iACs8NtHVEuAxz+iR7hRkdeXLbmtOomP2oghxZ3Lp1QEwRKfUPSYA9+v5YAJIPD14cNWmyXKlClLW0GhuTuJ2x2mI7xHuTPQDjbzU0dW8udb3Gx1CQupttSunK0Kn1pxIJ27pI7jGBAgcQ/SledNauK1pmmfcIW3MhWDuyIxjAI2jHeOPnGSjD4paSQbkL9FJ5CdFRGfLsr+8yU2u86JtddbXVqZLDfmLWkHaYTKDBgxMDuR1PAV1fYnWKxypQlYZSoOHae4BhU56YknIiT7F35f8xVN6JqrSSlbBG5JIBJYMK6GVAiUz0JHfM8PWir3T3NP4erbSW3xKFqGPpHbt9cR24pZ9PJQvcCM4P0ROFp8Pmkeieai7vbjp+5uF1umlykqAqHKZQ6ImdsdPuBHfEtptXXCrtATXUQujaAUpfGV7ff6jAAIzk/TgRcwbIrSGrlrSHF0yiogEA+naAcnvAgE4A29+CFyv5yU1FTKZqlNOsuelC42rI/lPc/m7kwM8eVtM17BNC3K3azGzQh7zB1wq1uS1bS827O9mFEqT1kGDPT26/zGWo+a9lFOUfs2tYUgSElRO2T3EHvnMmIJnizmq6ax6yZWhmrS2ogwjzDLhPqMdEyCD7DA6xwJNdco6Zxai4+lTPRCUKkIJPUCIzJ+5nABjgraquHAEoIKyaTAGyAep+dSKphTdHSvrCgdpLREAA9DmB74xnIkkwO66su92cUWkKbBn1DcCZ985iDiYE9COhzvvKxqlcLiWzuBIG9RSSInEnHT9Jx14gGrdFqYQreXApaQAkmVHE5nt26f4cPFA+B/sfVA6yqDG4IwhJdLLcLkrY9Ur9UpUW1SAScwZ6ZgiT1ni2/w1/hm3HxFaOu+t62oUzpkVSrWwyEhX4upbCFLcn8oQF7enqKsYB4qnqewVVKqP3hUYSElwBSSTAIgzkxA/wBJ47ofBRs1Xyt8D+mdIampX6S6rqK64VNLVwHKXz6lakojsdgQqDkFaunThkjb5cgqt+J7lmn8P+49eyplzV+BDoDW7DqqxmuoKxYxVUKw27PvkFKvsoHimHiX+AjzH5Zofr9B19Lrq3NAq/BqAobmkewQo+W73+VYUeySccfpB1lynZrGy7ToS4yoH/4+36fXgSax5XBhbifJlIkQof4HiXBc54tmnI7FVrNb4Zdy3B9Nl+TrXHL2+ct9Rv2jUFouVjulMdrtHX0y6d9BEjKFgGMHPThnUmAOvH6b/ED4OdGeIHTy7VrLS9q1JSIEsisZlxjt+7dEONn6pUn2yMcc5PFT/Z5WXnKi48qtSG3nJFmvyitoHsluqSJT9nEEZ+cdODlPe4X7SeU/RB6izyx7x+YLlSTgcZwSOf3hJ5ieGG+qoNb6Uu1iO6Gql1ndSVP1bfTLa/0VORIBxwOVt7FRwXa4OGppyEKc0tOHDBXnjOMjj6lO7j1ar5xnGxLClKAAJKsAdz+nBL5S+C3mvzzdR/wpy91beGXDtFS1bnEUoP1eWEtj9VDjwuAGSV61pdsAhgI7zx9E9uL18pv7Plzv142y7fajSujmXQCUVlaqrqWz7FunSsA/dUfXiz/J/wDs1WlLQtpesdd6hvziSFqYtlI1b2lDuklXmrP3BSfpxCkudMzYvUyO31D+TVx28sgTHD9onlhqPmVXfhtO2G8X2oKgny7fROVSpPQHYDH68foN5S/Bp5DcrFMrpeW1kulQ16kv3pblzdWfch1RbP8A+A4snpDlJb9I0bdLbaCloKVsBKWaRlNO0lIGAEpAEfTiBLfYx/LaT9FPisrzu92Pqvz1cpfgweIjmq62o6Ef01SuH/6i/wBUigCfqWjL0fUN8Wg5Tf2ay61xZc1rzHpaQKSN9PZLap8hXcea8pA/UJP247KUuiEhPyCB2gpn647cOtLo6QMYQesAQOIEl7qHbNwP36qbHZ4G+1krn5yZ+AdyH5dBh6usN31hUNq3JdvdxWttR9i0z5aCPun9eLS8p/CRoXk2yhvSmjdM6cATsBtlrZpnI9i4B5ip9io8HSn0hHpCNsnEjp9OHSg0iS4nYkIcUoJBmNqpwZ4gSVc0ntuJU+OliZ7LQF+eT4w/iPf5o+LzV+nqC7VNZYNNVhtNMkqltstbQ6huCQUh7fK+qinPQcRX4dHjpvngB5hXvUthslFqX9vWlVudoq2pXTsLX5qHUuqKElR2luIEfPJONphvxILtYbr47ubj2l7Suw6fGqq5umoC15Pk7HNi4bHybnErWB7LBgdOB7ou4Nlrasp81RIAyCfv3j7frw5U9MySNsWNsD9+9R2Frcl5X6IfCX8bDkT4hecWm+XdHdbrZ9V6noaV2mNZSBNtNc6wHV28VO7/AK7aj5fqSAtcJSZ6k34mfxU9EfDF5d2+qvdC5qnWN9qPLtWl6Otbp6p1tMKdqX1KkssISRClJJWtSUpxuI/NmNHLqXP3KFOKCgpJA2hBBEEHqD0gzIjtE8auZD911VqC4Xa8XO5Xu+3FXnVdyrKtdVV1Ss+px5xSlqMT1VPb248PDMQlDgfL1CjOrZSw5591130//a2dLVmqaGnvnJG/W2zPJZTV1dJqJqqqKZReh5SGS2gOISz6kpC0qUsFJKRB4u54bPiveHzxbM0LWj+Z+nmrzcGHXk2S9OC03RkNgFwLaeITIGfStQKQopJCTH5ZbuwqjcAgpVlJSn+Ufb+uvCYsF2G1jzBJKW1epCfrnjlPYad5wzyqK25yR891+x1FxYulIzU0r9PUU9Q15rDzDqXmqhM4UhaSUqTI+YE9OuOIvzq566J8OujRqLXuqrFo6wKqEUSa67VaaZhx9YJSykn53CErO1MmEqPQE8fmV8G3xUeeHgQtH7H5d6xVT6cVVGtXYblRtXC2vOlO1R8twbkiACQ0tAJSCQTwn8bPxGua/wAQa/Wet5kXtmopbAwlmhtltZNHbWXCCF1Ip0qKA84CApfUgQIEjiHHwzKZMF3l+q9ff4wzIHm+i62/Fy+PBpXkjywb0pyM1VprW+t9T07iXr5bKpNfb9NUqkqQpSXG1QqsVMtJkhtMuGTsSeEtO3KQFbiBBE4/U5/onjy0n8Q93IKiBPY9Zx/lj24UtN5PUqM9RBOOG612yKkbhnXmUoXW5SVL8v8AkvoOxrAjuf8AtH+nbhw0Rox/mTrOgsVLU0NHU3NzyUvV1QllhBEn1LVhPTv3j34aax4JISCOwn3zwidcCT1A3kAiPlnrI7j/AF4nyvA2Q2GM5ytN4YNuq32lLacWy4psqbO5BIJSSkjqMcNbx8xRKgBGVQZjt+v368KahwrJkqjuOsDuQP6/lx7tlvNU6CsScHb3Of8AHiAfO7ZFWkMZkrZbaFSyoKG0rMwpM9/bv/X6yG0aeqHXElxkpE+neCAOm0QY7SZ6e46xIOVGlmHdUW6orqf8XQtPoW80MF9IIJQB23dP19uLu/Fn8TXIfxN1/Lxnkny4Xoqt0/avIvZ/DoYFY8dpbbCUE+aUlKyXDBV0zJHE9lE9pBI2KGyXCNxc0O3CpzpynW0gNs+altO3a55Y24g9T1E/pCZ69ZPfLodPaWdcdSpLtShYQhRy6SdsxEqk7xGJUkgZAHHuisTNtbK1gvuqQElSCBux2OZMmJyTgdYmDc0NS/i3kBDgbYUZZCnAUgJCSRk5EDbAIB3HJKhwUncaOAvPtEbIRTNFdOGD2RzKuFqrUFVcbuLpWVDj71a4S+oKHmLUVesqx1KlEYjJGO3DPfqZNqriVIaUhMCEZBnMBXQESB9gY7g7n3vO0o+0n5qR4lKSr1BJKkgSBnHYQP4jh6asX7b0nT1aUtqQCEJJ9Ux+WN3pjOOs4VmeKlrKEuInjX3vRVTSDTvCedDanqKa2teS84XWFKRhPpJjdEYI9p7wPYy702unLU+p1CFJadPmjbkBXZM5kdMkmJ7cDWw1ZtNyW0pQAIKcIwcgnABE9J6/6yVyrWGxUNoJplubnPUCXATEDB9R9Q7jr0BgI94tOpxk05yi8L2t8pRIe5mWrXFpSxWPhdQhJKlElQT2B3Gc4P8AH643s8r2LolC6Cvf3rAkT0USIJ64BAgGcHIxwKmaS2XF1S1OoYIKlqKnCOkE+pPWZAOcQSOoBcbJVV2mlLVR17yKYepxK3ISkE9ZzEj/AAOcjhPmtpjGIiR6HkpxcCNkRqnkvfklKmKlSzuSE7mNu5IBOfVOBBkgjPbB42s8l9bUoU429TVJQCkE75X6oChnImTHTPeSOGex+JC6WB9LDzm9xMGT68dp7T1zGZJx3ea/xeXQ04SLhSpQoFKMphQjrA6jAkECI6dwPfDXZ0hoKiSmRo2TVqjSGo9NNPLq6NohMqSralIGSYP6xH/tEdeBRr/WztK04PI2KEz6AQDBETkZ75Mex4lWv/FZX1TIDlSxUhM7vVu2Kic9BETjH0nPAD11zbrb3ULUtlCgRuSENiB2ASf4Y7k8Mdnt85IMg3SncppSTqCb7pqL9t39igbo3krqnkMpVkwVKAHpT1iRhOZ+vHUzwCVWstHFmmu6qhbKFbWnFkkiMDr+nXioXwjfDXWeJvxI097q7S0/pDRhNfWvOD0Gs2j8MymCASFkOdClIQJHqE9aqDl9SadCfJbSlKTkhIjHDnGPDZpAVU8ROL5w3sjny718LhRMtvyVEQcDOOHrUGkKa+MhbYAKhBHAd07dVW2oTtUZSSBnKuCjo7V/mNJQVgpH5T3HsONkGOyg+qOXKqZxY2SRgdo4g990SmfU3EHI29v0/wDGeLL1drYvtJvRG5QgE9fsf69+IRqnQ6WStQbQPLHvtAHWST0Ed+w4wrNXVU38UuoND8j+SV91Dr922N6WpmSH6OsbRUN3JwpV5dO20oEOuuEFKU7ZzOACeOBfPPUHKzntq6uuGn9I0XKmprFjyKWjq3qi2trn/wBQL3FAVJktQhO2QiMcXI/tBnjksXiB5uad5faGvVtv+k9EtLrq6ut9QH6WtujyShSEOJO1YYaARIJ9TjkEjjnGXCqpKpSIzPvwyWynMTNWSCVCqoWyjDhlXu8LH9nzufPjQ9l1ZX809Mq09e2vxNKvT1C/XqqW9ykkBbwZShQUkggpMEHi3nKX+zo8k9HJbXfU6r1i+k5/H3H8Iyuf/tU4Sof/AOw9OHD+zl+KrS3NPkvUcmWdNsad1No1td0NZTuKUzqVt1076he8yipSQlKkj0qQgKSBtUOOoNFy7Hl/9MJIjHc54iV1fUtkLNWAuNPboAMlu6qnyd+Htyu5JhP/AAxy90pZ3EAJL7NsaW+QOhLqwpaj9Zngx0fL6QkLSpxKR6d+dvt+n04LTPL8BCjskJTtPsTPQ/y4cKbQ4Tkp3d54Evkc45ccogI2NGGjCE9LofaTsR2gyIAHDnT6I2lKdgMeoADA9v6+/twUafR6Ns7NpAAInBHCyn0gltcEAZkyPrPGi2x6oaUmjdx3QoAZwP8AP/Thwp9Ipj5UwDn6D7fw4ITGl0oVgfNhX1HCpNgQiRgjAP2j/fjFnJQKn0eCSdgA6H2iev34WM6XLYHowBiBk/TibM2ZDQEgEZyf6zx7XbG0JCjCzmPqOMXhKirGmkbAoJC0+wEkcK2LEEJTAJ2HOM/pw/pZbQSARMY7ZH0/rvxji0MjcMdp/wA/48YsBXIn+04/D205dvD7/wDr/Y7caDV+mq6ktmoHKSnSlF1oahZaRUVBEfvGXPLSFxJS7CiQkRxB0c7trUTuUNwgDoPp/n+nH7CedHLSx89+Uup9Eakp01OntW2uptNwbUmf3LrZQVAf3kkhQ+qU8flT8SHgm174M+Z2pbHqy0umm0zqBzTi7uyPMt9fUpp26gJacgBSzTusuqTG5PmQQCI4cbBWa2eG7mPyQeujLX6uhTvpxlL9vbDYSErSVKUsjJAPSBOZPQjuRjht1NZ0O0y005bKDIRtSSrpPpAzkgZV7z78L9C07lRQmSvy0gjbvIKsJ7jIxt69zjpiSOULTZLSEhbiyP8Ap+ohI6EIOQRBIJ7oyMGXuOEuZqfsEIrrk4uEMIyUC9Q6HXSVDgUhRWCQUmQEH2+sfWftieI5V2w0Z2QOskx8w/y7ffPB61BpYISfM2B9UhaCoEmMEz1HykZHbHXiF1nLh15LjimzBMbts+YfpnOfb/bjm23ucctCXq64xw7SO3Q6qEtKccLDbjLMnZvVuUMDE9/fH09uPiWd8JJIAgerpnM/+OJXdtB1DB3EEdxHbt1/UfXhmetC6VSiQdoI6ffjY0zmHcIQK6OX2SkLI2JHQH36E/1PGxx7yhEiMD0yTPt/X8eMcSULJGBGMYP9f58LdO6qa0yzc0vWiiuxrqJdK2agE/gVk/8AWRH5hGJ9xnjnJIGjAUmKEvOSmKqqEtgbiAI7d/8AX2/qeENS7O6em7aCeg69Y+38+NlUVTEdOh9x7f5/x4yho11Dg24HWB3ziP67cQXOLtgiLYwwZK+UFrXXOpwoARgCI6fy4nektDrdI8xMpyTHUjb/AOP0+3HrQ+k/xDwJbhKQSSkSBPTHtP8AHi3PgW8AWtvG3rZWntDWxusq6OmNQ+48sJYQgKAKlEwAkqEde4AHB630DQ3XJsB3Sxd7o/V4UO5PZBmz2Cm03Z1vuJRtAPRUFWZOe/369B2y20FAwbi7V1SYacG5tBTt3JGCfuAJMxjPq4KPiu5MX3w8c0blo3VVGKG5WlzyKunUQApcjsZ9OZGSIjIyOBJUA1tRDQQl2pQts+qcCB6QcmCBG49hAyCDU8zGAMaNggVBTyOLnvO7uf8Aha9QXRdXSkqKypSStwpSZ9kyeoBBORJ7ZxA2vV182vWtT+Gx5oWBjZCVBUhQBTtJyB6sHaT0leurl/yiUpCUCoTkOLKkhKgVbVhI6EBPYA9SYBPEDcU3WVyQWvxAeT5QSp7KVeXCQVJBCinbuIxuHsI4TLvOXyaScqwLPTtjjy0K6ml7+liqcQ44ksVAU0szKlE9CTA9UZ6mARHE75Jupo1XCyuhMj10zjySSQmSEZ7GFAAnOfYcQ7lXybvHMnQmv9U2+ts1NR6AoW7tcGqytSw+6hbwaT5CFZdXKspTkDHdIKvSFX+JubdY2t5LzCIdWlsrSlQJBMgj6SAeqfpHAqztEkPhvX1Neasw1PjRHONjj3A/kU8a40240866lDiCAdiiQAnP8VTB6EdMyclkpdd1WmKxKiFLS8Av0wSEpkESQMyAkq9gAJngzt6aReqVtatnneUFkLSSESJBj2A+mZycRwNeaXK521KIaTuQpRWpM4Mdz1nv79+0DjW58O5aXjkplBxLDKQ0ndJHrzR6kCXaN5DS3CS60pfloZImQIOZ9j3CZM7uPIuryHgFKJH/AKe4BS/mIwPuE9ROfmEcQWkoKq11DikJdO31KS50cVgJ9IyICsx+hmTw+Iurjill9KFHfkbNwCumCDBAB7nEEDhDqbH3TXDVNIzlO1U+i5IUhTzp8s+lM+kHGSR7wZOQZiY6R6u0g9dcmrXChCo9H0knoSZnGJnI7LqZ9mpQA064N235U9ukBJ69R1kAH+G01gbUEkuqIQHNqwAqJxGQQCTIxn7SeBTrJURguaFK+8Ru2BUSf0aKR8qcDjrQMBSFA7T3GAMZBjBj34ZrjpVVW3LQfCIkbsIAjp16A4gfY9uCM66iCp5CkpUCVHYVKKZghREScH+sceHKpFMyspS00tCSkJMbgYz/AAMg/wAI7CO0Tx82qPUUjJBqWeDHm5d/B14kdP64oGqi4U9KtVPX0CXik3GidGx5nPpST6VJVBhSUzMcdw+Vuv7bzy5Saf1laGK1m2aloEV1O3Vo2OoSvG1Y6YIIkYIgjrxwoqdu9QCUoSkBAWsylJjp7erAiB0meo47W/Deor9VeBPlw7qR2oernLe4qm8+N4ovPcFIOkx5AbInO0pHE2OR7x5gq140tccLWVDeZOPf/lTVykUw6SoylWMCCOHiw3pVO4gAgqiCmMK9uN90s4DihAVH8R34ph8TP4ht58Gt509YNGUdruWo6+ncuFeitaU43RMFSU00gEZdUl0Rn0QZEg8d42lx0hItPSyVDxFEMkq/NdztsfKvST1+1PfbPp2xUZCamvudW3SUzJV0SpxwhO49hMnMA8cwPi//ANoI5X80fDvrvlHymYv2qa3V1tXaH9VNf8hbqRpak+cllLiS9UBaAWydrYIckKxxTv4rvjia8dnMLSj1ot1ztWnNN2pLJo618bV1zii4+6EAlHo/6SFkbilJmJCRTC729TaiJCSCCo5nMmM5n+X04J0tMzIceaKfwJ7GB8/Psosthurqdj76KRsJUQrYSEwDCQB0kwB7cNPmT7yQMDrw53ZoNLJJnHv1nrw0vvAHtPbGBwwM3QKpwHbLpr/ZYGV1vxH7i2thx+nb0XcnFKFKhxDK/NpwlRWQVNEytIKSCokpJiRx+iM21pLZIIxII44Jf2TC/u2vn9zmp0IpQ27pehfU4pP74FNbtCEnptO4lXeUo47l/wDFxqQF/wAZ6jhdun83C4xuySQpEaZrzUylBUFSk/3evT9D/Dj6otNpj0iI/SMf19+IurVZxC8ggifbhO7qgqUsEhIKvTnGR/X+3A7ZdFLjVNoAgpEGInAPTjwbmhsgn1T3HSOIkrUsOKMnasyRA9PtxrXqMkbh6k9579OnGYXoUuRc0gkCB6h1PHpy5pLiVBRSDII9vrwNNf8AOGxcotA3jVWprtT2TT9gpl1dwuFTu8qkZHVagATAkYAJ+/A3uPxO+QFt5W1Gs186OXK9O0ydzlQ3eG1vLyQEpph/zClkgwkNkn7CeN2QyPGWgn3LV0jG80R/F34vNNeCzw/3zmJq1bgtFicp2iwyQl2qdffbZQ2gHqr1lX2QrhJ4UPGdoXxp8lbRr7QN2auNhu29vZUFLFZQutkJdYeaJlDiCpO7qmFoIJCgT+fz433xi3/iGa+Z0foOru1Byf007vYbdKqdWpqwK/8Ar3mSApCEglDLapKRKyApRAotbNU3CzMPtUlbV07dQ0ph5LTym0uNqKFKSYMQotokd9iZ6DhggsgfCC84cfp6Iaa0+IdPsr9S2p/jV+GHSWtq/T9bzj0wu4W2uat1T+GQ/UU7T7igmEvNoLTjaSYW42pSEQdxHB90pzKsfMS2m4advdn1Db/MWwuqtle1WsJcQtSFJ8xtShuCkqSRPVCh1B4/HrVa7uVRpijsiXUJt1vfW+w15aQpKlYMqAkiB07SeDR8Pvx5aq8BniGsmtrI5UVtJTti33O1uVK0sXG2qJ305SPSFJ3FbaiPQ5CsyrjrJYWaMsO68bXPzuF+rI1gU7ukeqN0nEjHHNn+0p82eX1P4S7Nom9XY/8AHlTemL7YLfS+WtxgNbkPvVXqCmmXGnFJSoetxaYSFJSspG3Ov+04afuPhBau2g7C/aub90rlUP7IuSfxVJZWkAKVXF0AJdSvclLbfzbt+4QgFXIPm3zk1Hz25nXjWmsbzUXvUuoapVZX1zxBcqHD0iBCEJACUoSNqEgJSABHGlptcrZfFl2AW1VUtIwN0TtEXHzmfLQCPLbCAdgEkmJI6bYkx/GI4le8UtOunpQtTz6AXVD1BQgjqCSREgDIJgyDwGNCauLA8rdCGwsEkCIgSRjt26R/IGmx01Ezb6Sqcfpq124IU44ht4l+mg7Ql3cAEqMBQifT3PTizraxsxDT0SNea4UsRewblKrRpf8AbJDjwKwtJJjptAyCegSNvQGOo+vE95N6e0baeZForddWm43vSNFUJXdqO3ueTUVjUEFttwwEq6HqPliRjhttC37mhDTaUIkCEoaO0+oDM5EHABwD2mOJ5yn5iWzkVzBs+oK7Ttm1Y3aKs1K7PdEKXR10JILbkdpIPQ524IkcOsdPGyIgD9Cfiqjmnnqqge/3oG8ydN2pd5rl2+ndorUt9xVOy64HHadpSz5aVqj5gmCSBnJ6cDO96IXWKCtnlpMmVQAM9QPacR9IHBu1zczrDU9wuDFAzQoq3nKgU7DZ8imQVqUG0SZCE4AIyPSPpxELjZ/wrhO9tK4CCSASPzHrJj0RPfAxKeA9W0P2TTQ0/gDPVB6t0d5bSitojqEpGCIGZHvkA9e3DFX6cU88EpbSqVdQMKJjP+P8uCzdLQt7e4pBPkYG4AFcxCVQcQMkzI/WRGF2wVr0NNoUmZEAAqKfyzORGMzPbrwvVUIB0t5php5dtTioAnTa3FICE7wqQmFyFmYGe3frMj3zxIbFoRaUNfukqLyQoH3BJGD9ZH6QZI4nlj5fOUbfnKStVQ4CjdtymACECY98gduCdyF5aaZvnMC1o1ld6iwaaqalLNyudPTmpet7KgoechoH17fT6UnAPEqhsxzrk/JBrrxCxrdMZ9ENbBpj9mttpUElQxPXZHSYGD9O/frxazwAePHWXw+9U1mpdJCkqXrjSimqKB9nzG6hoGU+YAQEkKBjacZ6HHAMGmWbpfax5Ky5bad1TTZKQ2pxtMknr0IxkmFGM9eG2/3E17pU4FinQs7UIQf3YBjKQIEmAMEgdJggnJYG6Cxw27d0txyFzw9rvN37KVeMPxNai8XvPi7631O9SqvV2cLz/lbUJ2hP7tCBAIbCIgnsR9eBnarXUOUq3mE1inW9qEfuzucCUmD7hM9FT6YUdxMDiWaT5U12obTU1cOspYaipWUhJQFKgqV3MqgGPoOp4uP8GPwsaG8Vfi5tOk9XNpXZWWHq9VCl0oFctsAhuTmFdSnrASOo4gXKP7pSmplG3Qd+yK2yVlbVikidv1PbG5XMTXdoradSi4yWfwju5W/OSlSyVEYMwlMxtPqM+mRGax1TD8uLWpSW5G9RDkeakp3QohMwckR6e8if0K/2i74a3JXk14Q6TXWlrFa9G3uirUW9LVKShuubUlRMozuUnYk4GQADHH58r22ikujTRbbbceWg+pwgpIASU4OVBJSAQIBB6ySK8dK6Rxc/mrNbEyNgDOStk7pOot9/rrPW079LVKaJLTjam1gSSAoKKYE9AqIwMGOHXR7irXeGFOJUoPN+W7Hq2GIJAOAPYwIIgR14d+dHiJvviB551Wu9VKp6m/3mpLta8wyltp5cJgACIT2KQTgmPfh35ycxaXmnrk3hmx2rTbNQlDS6GhbhDRQhKSsTkFZyT23ZxI44wSCF+tp6r6fbGaqmEb24ON9+R7eqsF4ZtG/8aaauQ/aVBTfsSnRUIpnH9jlYS4EwykYUYztGOpnB4W849F0zlBTgDZ5o2kJAGxUgGTMxP2n9eBz4eb04zXU5Q6ht5kgt7HPSR3CcH056kmVdTwYNe3KlqqaHBKakBQXkhC+gBg94VjrgdAOLIonRzw5VN3KKooapxaUDrbyhTc72djJeUFiBClQD+YxkEQTu3YxmOJVe/CY3dbck06gl1KAlKkypCpwEp9oMffqYPSXaUQhirc8wJekENK3lKR8sCPeIzA6nGJ4l3/Ei2G2yydkrCTEqJMwD9TPbE/y40ksdPKCCFtBxlWRuBB2CqJzD8Ntz0sXalhsutA+ptRKSg5xumCf9sRgj20Wm/XW5tW+jp362tUS0ilbZLi3zMGEpzk/cycdJ46BMst6vpFoqUuB1aMqURCZPdWAn8xgkjPUCBxH+Xmjr34f+a9o1vpJqmpr9p+oFTTLfpEPIQSCn1oPUkE+0YiDwu1vDOn+WSQmqj44MkZJHmHLoFSKuuz1kuamK2hXR1TDhbUghaFoUkGUZMpIOCD0gn5shM5WsvtBRP7o/lKtoG4dOsQcgAD7zxZ7m7ykuvOjmJetT3ZlK77qKpcq615hISgPLJUopSkSPUOsCAIkzwNtY+G+rs9OVtUj7IUCjrt6iFfKBP8wTERJ4ES8LTvGprdkwUfGtN5WSOAceYyhPT0H7WcLKNzqnAWA0hsrW+tXpgYg5URAEnoOp471+B3Tev7L4VtI23mUyhGq6CmNO4ylfmPMUzYCadD5GC+lsDcU4+UEbgeOa/wAKXkZYH/EDdNba6RTUemeVtr/4gerKp0CmpqlLqU0/miCVlK1FSUpglSERu+Utvjx+KVrbxF62uNq0dfLvpfl4y4yaSlp1inrK0sqUU1DjrZ8wFaxuDYUANiJkgyqVVpkMpYBhc73M66ytpIAAG7lx9eg+HNWe+Lt44m+Ta9NaR0jqZyl1QxcBdrwLbVAqp6ZoQildUk+lTiiSpBztTkdOOYPOvmzeedWua7UeqLs9dbxXqBfcc3Qlv/02UemQ2iVAAACBJkk8MVXRrStSkoWSSqXPNlaiSSSSATvmCcwSep4a6yzvhaN+5ZTnegFM5yAY+43dBPuY41bbHjyhFLXb6aiiAG7up6qOagdRtUVKAgElIhJUJxBV6oyevv3xwPtULTBSnoSQdvUfr9B7x29uCRe7QhhtQUlPpG8pTChEEH0jAzg5kwfpwP8AVSUpAMCRCUkwe8kiBHt0+v0HE+ChcweZDrtOwtOEN76j1kwR3AjGc/58NCmt7hzw+35I3rhJBOMQIz/t/XZrZYU4/EZkp+hPEwNxsqxqW5k2XWH+y7cl9TUPMnmJzFbfoGdKizosDjSqgKq6qqU8h9tQaSfQ2lLa/WsQoqARJSuOyKLqSJBycj69JB4/OH8HDnvU+Gzx4aHublwrqHTl9rRZL61T1IbYqGalKmWi+D6XENPradjr6PSQTx+iV5a2S42sKStpRQtA6gg5/hnhbuzSJQ48isEJYMHqnRd1UFGTuSoEAzmPtx4VefMUUpWN6oOD/P8AThv84qWQTJ6gz04+CFLnuoZnr+vAtbBqcRcySMkdlDrxsTX7YAUrarBHb7/z4bWG3KhQSlLi1kQQASSOv+/EF8RHia0Z4XuU1+1hq29W2koLFROVRpE1rYq61YBCKZludynXFgIAjBJJgJPGzGF5AbutXEDmVRH+04+J6z6b8Pej+V1NcaZ/VF/vDeoKm3SSuloGGX2m3nABAC33VBAJBJZKoITPHEBxP7zfsQFp/NthX34JHih8R2p/F9z41JzE1Y6h696jqA6ttrFPQspSEM0zQJ9LTTYShPUmJJJUeB082tIAAGcDM/x/hw+0NJ93gEZ59UtzzeI8uXlf7sAH1CZ+o48IQpxQJTEdc8bmqIrKsn0CfofcwOFztjeoassPJ8tSYmFBQAIB6j6EYnvniYAtQeiTUzG3b2kyN2P4nrHCxhlLRCnCpSk9j16dPrP+XGM7W1Ao6yMz/wDt7fT+saHKoIAKd0kelSjJP6Dj1bZAStyrCI3ET7R8oye2ONKVqq3gEpSRPTt+o40NsqqCCsKCTlKT39v8e3DpRMBCJMJEQBjpx2jiLyh1ZWhgx1S+x1KretsoUvcj5FBW1QwYiPafr/hwUOWuqEtrSStIQgAq3kpEE+wPuev/AI4F9MhTywkJMq+k8SCw3E0DqYXtkpx7/wBR269ODtC7wSMpQryajIKuXzGXo7l27ZBpjVStTprLG1W3N1ujLX7OrXEr8ylUlWCUmAVjIkkAdOB6m9LrHj5soITMOboUdw++4YA7Z+3A2suoVLphC3VKCt5ROCRISE4nImOkR1PQF7kbyH1RzkrbizpbTl41NU2ikdudY3RMuKFJTsplx1wpMwJEq6FS8EzAOtr3OGMofR2+KJ2rC6DeDj4RulvEd4JbxzCuWp00NxpmnnGGUbFoY2IBlwnA/rpxzi1/aGLRX1DSw0tNIqAtDgAcKiNuxMndAiSACAqSenE5s3ii1do3l3Vadst3rKSxVwmop26hXluABIVuSD8smNqhJj3BPEFprVV6vr3amq3eUV9VLVK1E4CTJTAnp7HpMnjvBBK7LSckntyU6+Xei8Nojbp081EqyyP6jrFuwkUnslIdLqQVYEjpHeJGJPs/WPQSLczTrISl1YSQkqlaI7gzMxtHWSB7DibWjSDYfU4SXX0GQ4tuFJ+iQYgYjABEe2RJrNpFLSEuuypRKlrEgYAEkxnqR16wr2gMNDw6NQfJzVV3jjA6SyM7LX4ZvDdT86ebumdNXG9WrTLF8q00qq+uhFLR4MFZxJMiOmSn9N3N3kqxonmne9OUNxobxTWGufoW6+lWPw9YGlFPmtLPRJgkdR7ZyXJu2mueKVN7mxuKkbDtg9x39sT7E9+GTX2qGdJ06vJUPOIKEBIKcZzjpicgxAPuYYJbfHTxmR58uPr+aSIrtPVTiKP2j9AkHOnkjqrltoG03q4WC423Tl8So2+vU0UM3HaU7g0uJUd2T9B0iQA9SMftCrbUGg4htaVJQlsALMGClOITJEjrAOc4NvO7x98x/EFyM0ny11Fc2rno/RhAs1L+CQh0CFN7nVp9T0AqQOkD368Bu6PI0vZlmohwIO4Heny1L9XynsSMzB/mTwlySOlcXyDAGfl0VnU8AhjbHGck4+fVK9QcyHbFY2aBrym1g+gh0n09ZicwARiCPb5QYty28RWouS+u6W+aduj9ruiXErQ+w8pDiV7UwoqHdMjIiSRISCRxGLzcF1fnvrS6VPAqCAknYArAwD1KcCcRiAMsValVTVKX+/KFrUv5inaVCAo+lI2gqVIMSpYyY4VL1cXVX4Tt2jkE6WC1spPxWDDjzKM3it+I1za8ZlLbmNf6vu+oKa2ILlM2/Vq2toAhS0JQkx6gApUnEzAI4gPIbwxM8/dMcyLw9rPRmk6rl/ppV/pqO8rLb+oQlYCaWlQgq3vwR6fUEbfUOpEBulUuqpUIQ6vy3WULUkJIIMn8o2mAdwIjJSqCCUkxq4bnHWGtop23tym1yQVJkq3iBCv7uD2TJESFF7Gt9lOoe52NSti60uqs6XGSVPU5MJnDQATKIIEYIPScdo4eqe/oqLNQ1O0NGfKWsSopUI6f3ZHZMkQT78NFpS48hTPlmpgggmSJwIOR1J/XcehJ4+K3W+oXSOKJSXEraWsFPl5jp+VPpEyZxPXji+IBxaV9D2q5amCUFHHkPqiltOoVmsqhTIJ3JKAFyqYg9iTnPbcQB1JO2pta2HUWolm3IrWrW4pCtlQ4HakNSgORgCcqInoMSDJ4pnoy8uN1Bplr3ONyWvmAcA6TkmYk+8k9uCbpvWpNQyipdVJbhveohxaTPzAHpA6yMDIABgzaa7wsRu5LnxLZW1sf3iPnhWYTo2lpa41NsW+7Yn3lPUFXUsJS48yJSArJAUUwCkEpBA691DrbCCUggAOl0grHpGE5Bge3XOCPpwM9Mc3nWbSzSJr/ADKRClbGFrISwV+o7RgAHoSB2mOFl25kOPU61BAcWgSSr0oKhgxIGSRjPsIEwXWOraQCqentrmZaQjto7VmnrRoA21yzuft9Nal5Fx/EENt04RlotQQVlXfrtPcEcSGzU9Nd9pUykJScFO1cqMmQTgARP+WRxWGk5hor2FrDqnVjKSHAU4k+3pyCYHuBjiTaZ5+v2UJQXHGlpABWrcUAexHWMdQDiTExMiKSFx3KDVEE8LC5i6JeCrweaf5v11bVXhpTlDSJT5iEHYt1R6SeoBAnBmBHbiS+PD4fOj7Dyiqr7YqZdGq3gFxrzCUlJOYnoeKyeDj4lyOQ99dcqkC4UVdDdUwp1KVgjoQRgHJwe3fideMX4vFo5v8ALxdisducoaOr9dQ888lSlAdRAwB1mT+o4kupa91wY6F4+77Z3GMdcg9VWUkkzQ4uY81OrynfvtvyxjmufnNfSVwp7BdLBQrUxZ7pXM1ldTCA3VutBYZ3EkBYbLjigBPrIOYECh7lwmnKvU8W4WslLcl8g4UEomRkZPWMkEYJmteblJerk6ufKgglK1AKWnP5Vd+pEiZAwOvEI1RzIapVSlX40pCVp/eQmQQdxz0gRBxAkdchbhRUonc9gG5X0LYrtcTTta8b4UZq9AMstrGxKFIlKwSncAAdogHcmMCMYMe44jGqLZS2fDigysnosBSlEEdhmJJgR7Zxx41ZzLqnEoQ3UtwmFtJY+VB2hKSCD6gAYMenPUHPA3vd3qLsSEqU4D6tqDlafcGCCAfbpGTHC5UzUsR0tCb6c1kgy8rVrC9U6gpDSQuDgtqkdYBlRkqOBgjP8COrxNWsqbb80rUcqyTJ9x1wPc9veOJ0dD1t2dUU0617VbRCTtT8wMrIABmJyfyxnHGxXLIJcPnPNrWRuIEq2n82foc4Hb6cAZp3PPkC3npzp85QXuVgVUICgkyepEGfv279Ppwjt2ng5UhO3G4gbcnt0+33/T3LWo9OMUowkubfSDv+UDp1wP1iJGeGG22eKlISkpgSYMQY6T1H8uMjpnPO6VawRRyZB5J65d6I8xTaxClBSVDPQ9ck/YfwxMcdqPhG+Je+c6OUV405qm7Vd8vej321s3GufU9V1lE/u2eYpXqUWloWgE9UKR145H6Hs7qw2UI8wyFbidpRMRnpAkd+/wDC53wpufdn5B8+69eprixa7Fqu2G3uVzy4pad9LqXW1rVPpbkFG8zt3ZIkx3uVkL6Jxa3LgM+qF1V4pyQ3K6tJSU9R16KAyT0/x4a9f66tnK7QV51NfKlNHZdP0Ttxrn9m4ssto3LUEjJOIAHUqHA85reOTl1yr1HbbAq6r1HqS7+SaO22cIfU8HQC2VPKUhhtKkncNzmE56QeKceOn4vGj+bfhm1loqwaf1Zbr9eA1Qtv1IpnKbamoSt07krMyhsgQFAhXWRPCbR2Ctm0uMZDCcZ5e9RKi8UsZcwSAuA5Ku3j/wDi98w/EZWXqyaSq6nRXLSvpzSGgQhCLncmgQpbj9SkkoKikfu2VAITKSVlU8c+L5TNrfU88sqd3KUSVFTiyeuVGZOcnJ6kk8EnVlPdb+8pbqS2Y3BCpjaREkQTtwfYYJnIBg14tlPbaglxzzFqwkBXb2+v6Dh3jo4qduiMYS6KmSRxc481GXaZTii22ghAVjqCfof58fRZShne4JEjamMAdvr9uHI16W5DKEIn8wP17d+3CKuqgidyj9ACSY65Pt/pxjgOa7brQpCkLGyYSc4AGcRHfhMt0JG1IPpx6TP8+nvxrqazzMJISB7Hr/DhTZLu1Qpr/OoKevTVUymWlOqI/CqUcOpiPUAI9uOWcnAXrnhoyUicdU7BBTkYAEgzxtYodz0ncFHvPT6E8bqelhcJgrOJmRPsP5dOHO327ecgjd1AzxLgpi85KEV1wEY2K0U1HsJTECOh6DHX78KW2ZEyBAyomeNymwlWY9/m4Q1tb+72pkDqRuM/rj69+nBAhsbUFYXzuyVvfuQpyQgmDjr1/wAuv+PCm01ZXUNZ2K3RMYT2M/TIGfY+x4YWlFdTJVAhQJ7Z/o/5cPtlShtLfllCirJlM9ziD0EE/rP3EXxi4qcIGsaiFpZ8W5tDQTUbkr2kj0qwDIQesSRIj78GTkb4j9bcha25VmjNS3XT1fd7cu1V7tE5sU9TOJAcZXIKYJIIMggztjrwDrI8VF5ISIdcRvSckzJHt/eInoSB168ErRNrVd/KcWVOF3BBUpWf+4kkmZA7D6nHDFbmiTAS5dZvBaXZwpFpzT/7Rq4KSloOGG0EKAglIknCgJAwcT1HE+sNgLTrLbIQXm8l2ApM+wJ+u72Gf4J9L21TqErMJBSYCfT+VOfftEdY6RJHE4tVm8gNpPoLchRgREA56Z956Y4tG0WwNbqwqO4hv7nuLCUmt1gSy00qJVEJkSCZ3Awe0R1yY9uHFu1l59SQColUkYCcd56dCPoP8HJdEmip0egyEgbSCCnrjpiD7e/XHCC6XhFgoy+VtoViCTlJyAf1k9epmIyeGcxRxMy/kkHx5ah+iMZJSLWOqaXSenV+tKFuDzFrkAIGRHbB6yDn7cCnmXobUgoLHcLjS+TQaop1VdtW3UJWaxlK1NFxLaSSIWmBIhUpiRu4Rax1XV6xva2WFK2NEhSk5nMGAYwQDkwMEDhsrLoq1Ujeyrc8xoBTSt0hlICjuBAmRP8AAyADHCFebi6qk0tPkCtjhyyR0EWs7yuSMOJtdN5ywpLphotFpSgtAwoZkxIj2xkg8QbU9bVXmC3O1SJTuJUpCAqYEEKB6kqwkCehVl9TUN3q+0tOpYRSpcKEQoJSgAyT0Pqgz3gk9+p7+KRyq5D8oLNoRPJXWdfqw3SzIqL8h4pP4WrMIIV6QUrUSRsztgZPCrXSF0RIOB+qdrcAyoaxw8x+n+VULU9yDtQ4tpDiEOSUBKZiDsO2QSrCU4EEArBIHVpSUs1CXyny22kLWlTQDhlIOE9QrrkmUknMAceKx4PPvFLiHVuqELKvMWgLAUpJgQomTMDMQSkE8aq65KZp3yhXzrSspDigt3bJKgIOEJbBM+qTkkdEuZ+2VYkEeCAm281CWlklJQlhYkFQ3IlJDfqAHoBAkHHzn8ySWNttNS7Spa2pcQ6ASEYUsEJ3ExBImSeme/QrawrFclCylpDywle9ClKbT6UlYGFKnIxOERmJKFa1NtbgyyXKn0KSVFQb3DcCBJSYgFWARtIwZ4FOdlFGq19GULo2Slxhbji1llrchSpGxEQMqjpBJBxKogiRrsabpscT+K8kvKAUYbIhRmVZSOsyJA78RfRN8S7TN07j9RteLcjzAougH+4ImAFQYgncVEgRxbr4fXgvu3jI5u09gs79PSKqWhVuVji1OhoJHqUkmQYJEHJmc+8mVoJVgWG6lrC0nAAVYam1P2dpipdQpryklQIWQnacmRAO0ycf/jjiQ2y6JqqJSAotOpBS4JTCUkQQRIAEHOCOkzPFo/Hx4Eb34O9eKsl8eRcRXNmpYrky4p9BMq64CiQQVKySoRxTK/tvaIvK0ZU15hTvTkoMA9YgRuKgR6v048FMfaarCor7EIQHnIPI9FPLPfaj5FvFK0YeAWFKViRnuYJxHeM9OJBqbVzabxVN2x2tqKMHaz+KWhNTG0TIk5yQB/dH3HA5pb05W+elRAcpWEnadxGAIiSQPcDp8sYE8SO06iU4wW6gJS6WikPEFSXRuSNpzhJV2Ez7HEFoZZGswhFcaSV+chOy9bCpeC1oJUVSkKA2ORtOFJzCZwMJO7oMDh6ptbI1A3RttNUDQp0JbCkKBVVrKioLWZIKlExJ27toG0HPCO3N2q6PIKvLaUuf3bioCjlRIJTIwR0gyTHsH1nQ9luiN4rmm3FwVNpJdKIk/LM7oExOBu7jiSyQu5HdBJqBrTvuEhGrTS0u1K3m/SpCkpTvUUyCkKVHcpmCYkdY4Zr7zGfdfV1DhUCtRQDKwQBlWAUzkgEkDpIw/wBfoS0UTCUftTzNxPpCxKQRlQITtAmE/afoSxXfT9pokEIeYc2wSlgnd0lQBGEgyrEY3cd9VQ4YD1xaKCJwcWclELxq2pUpNOULKEynYYlK49SiCDnb7SQSc5A4jdfeaivIHmKLa1nYtCtqVKBMlSlYBHbEdepPE9XpW1vEJWhLbY9SUxtwFRCVLHuDJ2/MP1H2k0vahUbkOU7jajv85DYEkFMbSogQZEEAmQQMTEF9vmfsXIoziajhadLAhqxZKm7PrKW1FKlpVJ3KkHr82SEzGI+4jh9t+i6ilaUpSEKTO1tRUlKSoJMiCAlUSoz6sj3PE2rnKG0UYCEMhUeW+FhLhBhM5nvlUTiehHEO1DWVl5q3kp3SswtBhITBIiJBIBSSQrHUn341/hMMQydyoc3GT5Dpi2HuX2kt6bxeqa3suGrq33UstNhyBuMJQkrUAkCTGfb9eGXXdur9MXeutdYymnrLW84y8224HEU7qFbVgbRCvUCJJORjoOFFBp119bbaVqcSte5DSBswBknqCSJPUwSJMRMhsPJ1qutyqmsXVoDbIcZKGVKC1ASEzAgkkgkYkdenHpgYwbDCiurKipOS5BW60jr7xSrcAlJCQMewkYg9T/Gfrw46OsbCKtKqtZCSsDYAqSR1iSAIPbrkZHXjZrK21FFWODyQwEuQlsNgLT0wB1GB/nPbhrttmr6twlhsulBASveClAORk4GJzMe3vxzZOyN+ojOFBq7W9zCC7BVibzetKU2j6NVA35dWw0ZbCwJPYZODj37HoCeIxcubv4BhSqVxlhMy0WhCdu5MnfAMSfy9MicZjunOVd1uOz8R5gZZKWlhCQNqiFdFrhKCMdZEicKE8Smm5SN6baTUVrlMisUNohAUpJyZEkDp0k5HU4I4OV96mrJQ6JgYAANvRI1Dw1T2+ItnlLySTlx33OfkOiZGK+46gU6KJh8oX6gYLaEpMYUTlSfUkQY94JEhlvDVPaadT1Q+XXylKUICpeWraJCQMAHtEjBg44ll4ti31hlpC6dBVKVrWlbpXgewAkAdNvzdcwY9dNKsUdMXVu+W7ghRQoOQTkEnpEScgAjv2FzSycnlSYqWJ7vwW7d0M9X3x6qQUNJdQyYKEGEmI646kD7x2zkDm/ocaCifVkhQ/vmepPTJ9z7cE3WtdT2111LJRugFaSfUknOQSBJye+MnJngVX1T1xdUPLIJUQoBIJBHacdgR0/04DSPLnbKbI2OJuCd0z1dyS0kgKBOQVbsf0ONf7Hqaiwm5hKPwfn/hiouDcF7d0FPzEQOsRx9fsrzpUkqWokwYPy/fHGpNuU04ScAyoR269D7Z+/Ggie7oh0tW1oSRthSwYT37YHf+v04X0lt2rJGVHIkEdv44+n+/CiioiYSlIBBgR2/zP68PlBYlKbCyN3ZIGZPtjp1j+PE+npN0Fq7h2SCjoNpSYmehERw6Jb/DsmY3EEgEwTH+PC6jsYp/UspKUpJkyEgAT/D/AB4QXVRW4pKQkkTAIyn+GOh+vQcEyxsbMlA8unkGybLjVlaylJICvQAcDp0PX/zHvwo0fqShsH7T/HWC2ag/aVseoKYVq3EmgeVGyqb2qEutjoFSn3B4TOUS33QI2lQI6fMABIHvj+OI+u2ntvp3JCvKWSn0rk4kn9I9v4Znga8l5RuLTG3CR0dKWfKnaAFASVxI6wYyT+nY8PFrpv3KJOwuKCJ29dwEGfuekZk+/C2zaddqllhhtBUoRAiSoZgHp16nv/LiX0nLattT6nHmKimG/elC1SsddqnI/n0kjHfjpFSvfyC4z10TdnFadH2NTq1FaSGwtKCmSAuUqT3ndEdSoRJAHUA1aD0+XgQsrBGXdxG87v730JIAyTHuOInpDRooyhIbSFBfqGwkkxOCInGOvX9eDVpLUdksXLq4W+pspqLvW1DJpbgH1b6RCCsLR5eQvzCQN3UbcdTw+8PUAZ55Oiq7iy8Of+FD1Sq33NFqAS0hK1IykhQMQmSe5IjvPtjsH2wXRx2uZStzaCrcgIXhIMAkQTBEASf7o6xxBrG887ucdSFvvrG1sNJKnSo9NoJKs/TqQMDiY21//hukNW+pZqCZSVLAQADJJwdyuhj1RuyDMh9irQ0ZJ2VWVVqfISGjfuuufw6/hqcsecHhNpNQ6iYRcbxekuFxwOwbeQSAI7KHUz78ckfGzY2NKc8L7pm31jL1vs9a7T+aj1JWhCoUspAgfKPlESesHiT2rxicwuVOn6+0WjUd4sNK+Ciopm6ksI3QYBBPQpjvMH3jiunMPmE5U3RdSuoZffW6XFKdTA3T7L6gkT9CcjB4WKqaoYZnzzFzXnIB/pHYfkm210EB8GOCANdGCHOByXHueyn+n/CfzB1jyNvet9OaTu1forTJT+1rq2gq8lfpEJGZ2pJMAqCRBJMjiuV81S9cXnEIKoVO5sBKipUmYSM5VECIwT8pjiyXJr4wXNnw7+FPUfJnTjtic0tqtp9p9yspCqvpi6CHShcpELbHqJSqAcGTitVgs6E0z1S+gimQ6FMoIiRvA6j0jPyqAGcwCY4UZK2WokMYGGg7e71Vjw0ENLGJHHLj+foldO83p+nFc6Q+4lKtiJSCmTJndEmZkgZnCRIJgOobs5WPONrceDgBVsSolSlFKkHaMiUz3lXYdZDtrTUQr9zXmJbC0ltkAypcHaencj8wGCe8nhitWlrpq4VdbbqKtrRRMqfrk0TKyzRU4MFwqR8qQoknaNo7kTt4DXOqyfDYdgmC0URaPGkG5SOhWmoDqxKfJRCkyVNrCikGDJAAyrPcTkiOGW73NDtI6gKeU6rYlajJwEK9QJAzComJAChkcLq2qabom0s7CsKGwlG/zITnB9MJO07QDBM4IHEcK2qirbLSQd6wFKcCnCkmcDochPuJPWIgA55PLgJhhZg5K8tVaXqcFwsBxQ29JPpklUGSpJJSQfoYzwnfrWm22UOJUolIbUlJTlJgwVZn0xmB1VkRn29dSEpWhb6kpSGwCqCiSYAPQJnIgY68JH0ht55AdZbCHTsG4EJO4BRmDMdZnHUexglS1Z+mqjaUbypwIUSpKg8tsKCZAAyqPyhGcFKgojcscWQ8Gni71H4U9f2++6dudLRXWlchJC/NSsKSApsj/wBRJkSB7/ljgGNWlTrLW1twJeUtLZSolYUAgByff0wFA5XJMJ+ZvYZrLLtSlDrexRJW2YjcVESYJgbgokzO36JPDHPQOaMrLbeA04CuR4uvG7qzxW67/wCIdZ1TQfQwGGSxLDTTeSNqSNwAJTMiYVPcEA/U1pZ1FTrlTcQpLpMrS0okgEmAVbSCqeg6SIjiMcwOfd65x3Rusv1ciormKVm2pU2whr8O1TJS0hASjb6wlABVgkkrBKscI9L8wm7bdAKh0KQstqcQFD1gKBKFJTvMmQAJOdvckiEZPD2TnS3ESRhp2C9nQ7lFVoVSPq2bFJR6wlZ7AQpXy/XEwDtOYIGgNLPXhva+CpYQnGw7QkErKvUJjI7EAA4kmZHy+uemNbWKrqf20TfKYNCmpfJSoVExvBUkwjan5ZGZEwRPE65Va3tvLXU7NzrrHZdS25lCgbY8tSaWqCmylKiU7T6FKQcADAmQSOJ8Dm+1nK0mkcMlqgOoOXbdjcDDTyUIW4krCVlxJkwEgwkKIJA3AxITt68Ru43Fy2sqVDzLAKgUqUN6IkkSDtKon05mJxOSNcL7RVBIUo7Q6skEgt7lEJ3ZgDdBECZVtOR8zRVaFotRMJS04PLUpxsLYAVtBBMyBkJG7MyANuYHEvwwd2ryOrkaMZQxuWs2kLKU/im2JhYUQorG4xO1RmUyJmevSeGw6wZYfSlSqplQTI3oKNhEnA6kSIJM/oDHE/u/htuaWEPp3uMLJCiCdySZxBlMyoCZG0Z9iYpceSz9AFKWxWMqeKzJUSEAABAidylYJED7Y6do9bNsIHXvmc4kOSKj1tQobOXXlLCVje3lJk42kSfdIAxCczkSHTd/OoKlKGXXnVFKVJKQlPlyc7TKQcqiUxIIz1HDVYOV5VUswyEOpKXN+5aRuJABURtEkJ2pVOd4wOoNHJ/l5bdMUrYddUmoUvchS0klO1JI27uqc9ComBJPXgtbKZtRMIn+UHqlG9V1RSQOmblxHQcymuzcp6nUVG665TVSA8lKqby1EpJk/M4pUpAkxsGTOEkjhwo/C9XlgrqEIQwAhCQ4VJTsG4wCpPp6YA29THQ8GG2a7sui6XznXGFLSkOEIdBKslJgAhWPf2IBgQDDNd+KJNwqagISy8XdywhsJHf1EkZMKBx1iMQDBG822mpX+Gx2pReF75VVkHjTxlh7FR5PI2ktFKX6hbGzzIO9SR2AUIGRCdpI7AHHqE9Ofh6aa8OT/g+vCdX/APCa7sF1Bua7mUoqUtx+72boUAE9CMzMknjlLdOZlfe6tTg8wqUN6FJfK1OKiPV6QQN0GMbQFCPZwtd3fTSjzzVKQYUoEk+mQFbcEyDPQDM/qoVUHiYA2Vhw3EOj0OJHLcc1Fue+i9Pr5lXYWdDtVa231uUyluAJCPMMEgpJJg/ZJH68Ri32UUDi/LRTJcA8kKKQ44PUUlCt2E9VKiM5T2AE3vd7tqHVOvVFKhCnNihuLu0kJ+nqVgSZIjGBwq0NetI3S2X5Vw1NTWJ222pyqt5cpis3qpb2g0yFI+UqCyRuMYIPXjyOiYN3lZdb65zNMXNP3hY5d6Z1Pzf0+xrnUruk9PtvFqovD1MKgUaUI3pV5WT6lBAP5fUJEQVQbmFR2+l1Rdm6K5Lr6ZmuWG6hLSg4tALgQsFIlCSACU7knIGSOIDqXm4urU47vrHN6hMCQ4QjJlUx17FPzI6CIi72ta68PJRSvVIp24Lfl7lLcgRMEjatRxmCIVlREK8lqo2jRFuUuQxSF3i1B2Uu1BrCksTADSUpWlG1IQkFwjduMJBGyTJyT0x2BgGp77W6geS0t3y1Pq2BlB3rRBIOI3bZCsjJj8wnhyt2lap5JePniUpWVEJAgKBz9jsMDbE4gSOHeh5fihUsLStbjhLZO1JXGATBwe0iDBg9N3HkVpqandwwFyq+KqalboYc+5C3/hAV9G2t5wKDilLiNziQZUJBEwSSMxGZj8zbU6SQpRS2hKhugQqEnbkyCM+0GB/Lg1q0Gq4+S08ryt6kjy5UPKBOwKCeqiOwPsY6ji2nh9+B1zB8TfIir19ZmEN28oWumYeqEocrggGQgH5ozHST+o4kzWuOlALygAvctYSGBcy7np4qDiS2dpJCEnsJ+0DH8YM+3EarbEfMWopED5s4n+vpweuZ2hHNDXmpoHabynqVam6gLSIQpKoOf0VPTp2zxDtM6Ho9Q6ytlLeK5Vpt9XUIZqazyS+aJoqhbvlJ9S9o9UdTHvjjnLTBq5MncUPrbZ8oSlKyVAwQY3exMxg8GjkHz61D4ebNrKmsdHYqlGuLG9YLgbjQN1SqdhwgqUwpf/ScBAheD34ilTbbbZ7nUIo3/wAcy08tLNSoBsPpCvSoggwNoBg9N4HGmofcq1qaBUlskBIIMKkCAR1VP0/w47RRNaMqJM9z3YTNdkOOqUC6hKimTKjIIEgGYjPvgduGty1CQlQDhWnfsg5T1BGe8AicfwB4kX7Dfq9oSHBuSSEmVAnInGTkHqInEcOlLoV1sphEKW6pCTnKZ7jGP1zH6caGmfIchdBWxwjBKgqLPtBU4kGSApK0EoSJggRjt2z/AIcOFHYgppBQkqIkSBAGYAgTkwc9ZBAAjiY0OhgKlpKw4qFbwABvT6pKiMSraMDvuxmOHG1acYolFSkLSUgKQoHaE7e5mB7dTH0njvDbTncKNPdgRscqQ+GZdu5ac0rFeb1bG7tbrZXNVVbQOK2IqmkupK0KMwQtMJmeihxcv4uXi/5YeNzmfpS6ctNH/wDC1NaLb+Gq3HKRqlXULUQoBSWzG1tIKQr/AE4plpehbu1XTNLUmmYWr1vJRv8AJABlQSYO0TkSCMntl5FWGGypltG0jY1CQpSjIIMdTJBzkkkxjbwepaOEFr3ZyP3ySxWVU79QaR5v0/fxUw5SafsdVrixUF3vKLHb6+sZpqq4lpTrdqZcdCVulIyoNo9UgmUgjHaUc6OWFg0Vzz1JYtHaiRrXTVtqVtW29tNbGri3tBKwAduCFCRg7ZE44GdDSppW3Ha0wlKUjy43z/8AEyJ9HzHtgAdONrWp6ispSzQn8FQEhK1KIH4lUwIT3j5gEiQYMgxwabKWbM7cv1KXXUeo6nHOTz/QKcV17t2hmkJQ4hypebP735incBCW0pCcT2AG4pncEkAsdw1Mupfcq64OM7UoV5O2EgfMoiIOTJATgE4joYpUXxizredSFv1LeHXFwAVBJAnOMmf06cNPMjmDZXdF0VLRpqG9RNVjrdXUIfSqlcaVCadDKQNylT5ilqIgEpiAJ4jTVohGuU7qZTW505EcIwDzPVbde8zn7hV1C0uBThlW70kJjad2O8GDGY+44gztS7dSqpdD61OKAS0JK90DaBnHUTImDPc8Rw3tC3TUrUhakIWEhbwKwrrIgdBn8xAmJnPF3vge+GnRXio8ZFksvMCqpkWQtVB/CuOhCnnEJBbZCiBlZmTGYMRPCvU3UzAud0TjS2ZtOGtZzKqTZtGOVDwqKoFTSE7w33fCgr1pBPqSSAJwOwzE/NVPvGiQltva03IASIUCqMjdJk9AfqImAB1w+Pf4GOU/hWpNJ1Whaduy193LgqremoKw2hMBLmTuAVJEf9v345Ya2oWG6FLrSw3uQdqkp8vYAARmTA749jHaJFFC2WlMse2QodXVmKtbDJvgoTVlCp1Zd3tsMuOOJSVStG6VAkiUgSMgq29Mg9OJ54dPHHr/AMIlt17TaDuVtoUcyNPuaXvyn6NNQ5UUbiVFwMqXJS4QpSt6SQNwiYBA+1RWtU9xdbQ+2ryX/MDiD5beEkbkKE+mCSInuT3mKVVxcS3uKVkJSlpxJTLaiVZIPQgbgAlXcjuSAlVbhqIVi0bSWgrTVVSVtJSGkObdq0ArKwpCUhQEDpuEGTklMCIJGtuxu1d3dt1CuouhL5ZZ/DMEOVCSIKdplad2PSATgkgTBQ1z6U7vKBKyPJTKxuV+UAQSZOSe24xPfh/5M84NQeHrmhY9aaYqzaNRafqzW2+saOKR4IKUrAUkjaRIgzuJ7dOBxKIAKPsuOW+6OI2JbcCvKIU0FhCyTIUnJ+UEJ25Bjpw3u1RaaR6i2W0oASViU5MSB1gT1yMdwTwoutTUXq6OVDzrztU+tTjriQVKU6pQK5KcZKiSR2P24SfiF1oZ85SkturK1An05hMwcAnAn2A6wZ0Kz1XT3x9+B+7/AA/ufC9B3i+W+9LRQs1Ka+lSoSl1EbFGNycJI9WAAekiAdXpbr6Mo8unUtpIHlpaKEtBRkJSMnpJABgwDKtxHD4ddVevWU1V3qX6p14FtbtQ+HnSoN7Q2A5J2gHA3TCSokbEgtVfTpTWhLTjJp9wH4cLAQ6CsBaACTIkpJ69IAJE8WDE7U3D90rVFOY362bBRS5MOUoJQ8EUyFbVIUJlM4BHqgQUwYGR3JMIKy4ipS8yVKS/6/JKCoBBUZIM4iSQCCFSJkpB4k9PTKeo0pab85lCSpaXQkkwJCcYACRMAYBUenDfUaVUXgFNoUVuKaKUO5CVeiEk+lUkYxB2me5Aiut59toRi3XQtIa8poqb7UtqU6HFOFsJcQqJgJQAApBkbSUCEkhJ3AJTEw40XNG50byqeqXvSkHakqV5iwYSoJXtMEwCZ9MA9zw1CzPOpaQW1IUhzalQktnamSd0GVbjG4iMx1HpS/sU+WoBThp2l7mkqUk7TJKk5BAXG0bT1gkiIhf8KRvspmiuDe6k55x1LzjPlvVCCpSQk7pKFFJhZJkiAE9twkQZHD3p/n+7RvB92oWghRCm0oLi0K/LshQCSAIzBhGI6AXP2MIBCP3LmUBQchvOIJEYMmEiTkGIBlN+x1lzY4hKVESjzDKNpAEzMT9gAM5kYz71LHyUqORsmDlWd0V4ylaatpaefBYXgjaE7IHUyYCZ2gT7kdJPDoeeln1PUOLcqXApa9iwl1ptCyBISteYylcqnG38wA4CV48NGttHcsqHWNVa7hbtOXFXlIqmWFFDK0kHMqyodj0//KeID+9aSChbYcbIhPmrSUjACdxBkxnEYSTI2xxJjus4ADlu9oI55CvTyncteu01q3rmxTtopws7lqQREH0oT+ZJJJhMgnrIkxrUevk2CqXT0qm0KYX5aVIaUreJQBB6boiZmeh6EGplr1VXCrCqd8tIcT/1QA2kpKoRtCSJEpUTnqZJ6wutdZXXBKDv/dqhspMrUuT8oKiQkkqJntkDrgrFepS3DBuhVXDTkZcOSOt45oU1Y8lTtZU1K94UttpQndIEiJO6SSQBMkwesxuu5p01O2tLBR5iUStJCYQdwweyDJSZwqScSeItTaWdvDLanAypLY3O1BJcSNqOoBJAGDnr6QCT1Hi4cunUKaR5LLK0FSAkErcaPTZuIBWqYmIOFEyAeMlqqlx1HdQ2eByZhOV45xVyKZxJcNOlO8k7FHCSd0JMCTJM5BLsCD0YrxzZr6h5QVU53BtsqJI3xhKBPqTAgbfSO27B4dnfC1qWm5dta1ftdYxpsXAWxNzCUltqt27koJPyqEqJgCQCR8oAhzWji04lNHTEu+UtBUoEqTtmVKxIAAVtEYKR6sZjGeY7LdzGtGcpS3rKqutXDZeUtwBPr9RUqD3TgmffqQYMHj5+KqrygtNuKdStahtQk5B9iqRHo/L7xuE8OVl5cVVwec8xUlW9S2RkdYTu2EFQgHCj+WYJIkh6K5NUvmoNQ6K1xJCyhO4oKQBAEAST1BIBH96eJ9FZqqreNkGuN+paNmXHdQrS2jm7ptqAyp9zeHSt9BAAEQcSYEgQFQSTAjgjWTQYpaBK1JbJWhMQkBpRIG70gZJBgACDAIz0L/KDkhp/Utm1M9XaksWnntN2Z2606LhUFtV2W0oJFKykAfvlQQBP5cTmB9cOY9FTIBp0NvLd3ALWRCxnAUDJ9KQCEnqSI4daGz0dICZCNQ/VVpcOIq2uOmAYaUpoNLCjdDiQ2Gqckh95SUhCRISRgkExEEjAAjvw7aeotFq05flXTVSbLW2m3Kq7ewihW4LvUhaQKZKgSEFQUsgr6BJmOnA3vGsay8kuuv7Q2gLQpRAbWvegle0TIkKzIUQmOs8MNUlKyhRadeJAUHcpDaYPqQkndGTJPXAIzB3nrmnaMYXCmtrtnVLs+ieKzVKqi4FSEtBQUtKoSPUYn0jBkwnJyYVIwAb6eFT432rPDP4alaBatdnrkU9O4igfckLogo+ojbAWmVAgDIM9ccc5F3sUrCg2xSMlTkKCmgVISEqKjBVA6xCifpA6OXKHSi+b/M6wWCqvFLZaW81rFE5X1rxbapm3nAkKWs/KhJUZk9jkxPASsLZR+MM4R+mzDnwjhe+b/MVjUeqrlXuFl6prqlx5SgkDbkkuSrO0kkwBIkZE4YdN8jtVc1ba9XWyzVVwpGE+a8aZpag2M/N3iBGR24JfxHvB/Q+BbxIVmhrdq21a1pKWmZqkVlEUjaVoK/JUEqgKEdAYIInPF6Pgj/Fb5R+Dbkhq7T+v6ByjuNwqE1TD7FKahVchLYQWFEiYkSJ/vHvwNqpHOGYm6s4RODS04kOFycv+n16ZuQp3kKdqW0pGxKSBuIg7Se5xJEAmdoAgcJKO2ru1UhsNEpcWCEIAUpeBEbesx0j6nJwUvFPru2c8ef2oNR2W3G02+63B55mjaAKadtTpKUo2kDoY2nAM+5ATaT0ApTqXHVpNQ6opASkLIgABUYgkd5z+s8EKC3vmeGgIJc7tHTtLiQpd4ZvDVWc5dW26z2+mFXX3F9FPTtoTAqFLMgfQDPTp17Rxfnm38AXWfKjlK7qPfQXD8HT+fVUtLhTQgFRAjMZmOvfgOfD95jU3h2506Y1JXMB6ms9Wl19JysJMhX2I3GO478deufHxVuU6eRFyftt7buVyudCtqnoW0HzCpSYhQ7DPDNWUdwo3wR0dPra/2jjPX6bdVWtPdbfX/en1tWY3MHkAOM7fXfovzx8wtIv2S6+TsKvKWUKlMBJzmT0Oc5MfXA4i9DpOpu1YEpaWjyyQtcFKUAfLIPc9P4ZweD/zBoWL9qh6qO1G98vKKsATJEDsAJz37e3EbvVytWnrK4EIUrYFbpwlZKSDuI75Eye0z6eC9RZACXyENaotu4kcWNijaXOKg9LokUTLUK8pttsqQt0wlSgQSe5JEiCDORJmeEVXdqS1vw1tdcKlNFwNlSAgETtAJUT2+p9xI4W6juFw1Ql5Sj+FpEggNghGZCJBGQIWemOmZnaz195pbKk+Ult1xKlOIVI3LEiJVHSEnA/7pJJkhZjE04ZyHVNVO2Vzcy7nsP1WxNmNaymruZUimIKW0AfMTMIxgYMnMxuJ6EjRW3ldzqHktKShptMvHyyskZ9IPUAZBznvPThhcvbt7fW4VhIKStb64KWkwJSPr1BzjiL655koprYKWl3oo2kFRQVDJ7E46n0iDI6AlJE8RJayGFhceX1KmxW6eolDBufoP9pXrLWdNbWjToCkra2oXvPrWSCBtI9x9gBkkdRBa6sqa4oeqAGE7fM8wKiIAgj2EKmB6vVkAAwjrKx78Q8p9SkrQVl0mQSZBKZx1UQJPUkSIAPG6lt4tri3Nhc/DuqbKXUh3zFgHaYxE5IBONoKpHCDX3B9TMTyCsy22qKkhxjLuqdqOiCHVObS0mnDTTqAlXpAIykHqkekCQJMDHeXaa5qVvL64U1Ta3naaqQUvMqaUptwGEhOxSTKiAMiYwcxgD126ob8kqhKVPqc9RCggSFKVuIABKSTMdgCO/H2kqg3TF55xlRXueLJUd7YETAIiNpAA7dZSN3G9POGclzqKXxOYRh1hz71DzJqUVupL3X3N0qS0l+sqlvLkpxC1yI6GBGCDgSOBrrrmA/W1DiGtwLSf3ZCCQIPuAYE5yIxI3do9e9Rfgmi048ErUCVo3StQBhRKpPZSSI/VOI4i1Zc6httaAlbKlpkpCQ2CoiJIkZxIPaE53CeNq2+O8IwxnmuVDYImzCd45Jdcn3rhUPpLakBLa1OiFGUneohSVKjckAHMRBMZVLReFr/ABLjKXijzVo8zcd/ykwdwJOSATEjIHYHgweA/Tug9eeLHQdn5o3den+XlyurVNeaxDiYZYKVbVKgEJBVsTChCdyjETNqv7RP4a/Dj4bOcWkKPkBe6CtZu1p82/UduuouVHblBSUtOBxKjtccQDuQqRASY+UBaOT5imgYBAC5x1G5x5RcVKTlaZICTgEkKEKyDj6gdIHHSDwh+BDwwc2PhNc0+Y+seZJsPNSw1FS1arZ+PaS6HGm9zLYpzKny+Soen5ds8c5326r8SmrSy8lCyIWBtTuT6iEGACQDgAYCUnthE/dnkUzO6EhnaiVD1FMkgAg5BxMZxOO+rTg5Wzhnkpbr7mBbdcaO0nbqfS1utN4sjD7Vxu1E8pTt9StaVM+Y38jamW5SCkkqCgSeEnPTS1i0DzJuVq03q2k1xp+kDCqa90lvco26vzGmnFANPBLiFtrUplU9S0YJTxEFn8QraooCm4bG31JjPTMDoAD0j9Txo9T0ubdnZXqyepjrnH+A9+MJysyrsczfEvaOcHMav1DatHaf0Ja7jSMUyrPaajyaVry2EoW4CtSzvcHqcELkqA2y4Bw4ou72pqNbVS4relDrqA4yE7nV7IKVhJVuCl/KpKgMbVEE7a6aW1Ahyrp3SpQRUtlCoQUNGBBQCDjC/USTPpySsgE+vtSNGaT0veXLtpm4q1JRPOop6GtS9V24NOqaWKxmUeS47s3hoqUFhZODkscFW7n2UJ7G8ip7bLax+3aQVLiGwry1Ph353uikrKcbpTtMHMe3Bg1Fyz0zedLKdRUodUtMHykIHRCQobzhQAMEkHrMQSDXzT9+WpgslldQlL/lOB5twqB2j0yCCdwXjPzKRABnbOKjm4bly8t2naez0VA7YXqmoVXMU5FbWodDY2PSY2t7DAMBJdUCZJh0snEVPA18U0evUPkkfiPhuoqpI5aaYx6TnA6+iIvOnwCcxOSfLDT2t9WaVq7dpjWIQmjuKsl1WxSkJVA3pCh6hv2lXq9QngTVegEVIUp5a3nIUttJQklwHqorBBiSB+bpH5uCx4h/iR82PEp4fdJ8uNS3+gqdL6R2LoVt04Q44lCNjbjqmgoubWVKCYSkggmM8Aml1BcLCkofSpKvMPqQ5LYViTMztGBuB2wrHvwPgNO/aZmN+i71JqogDE75pxqOUzyWQGfMZ2JTiVgGSem7cP8AtJAA7yTB400XK1yjeSUISg7/ADAjeQCBE+n2lAKsCRuwDHE95Aau0TqHU71HzCut80/Zk0FSW6m3UYq3V1nlqNO2tE/9NTiUoUfyzMpT6uG2jvpqnEOPKbYcSsKKkJMqJI/MkCYIjqJ6j34kNs1HLks9FBHEddD7aInMrxe61154TLPylrW0N6YstZ+JZWlKXDG6QnHSDt6K7bsDHFdajlgKdCXVIDOQWlOjZ5UZ2gbTkkyOvQAk9OCFW1i0KS22ttalpOTASkSCoqgSAEiCOvXAxw3UGoaCxakp66so2qyjYeQ5UMqUsofQIUpO/sCAUkyFRmSRPEaexQMy7CIU/FVTJ5cqMUmjNjKFlaghKgpSA4dqVknaQFerqBMAnIEz0lul9Aodd2rbSmG0ndMg9BEd8AnBBkBXRIgicw9RNeLfxDX1/lLyzqrLT1AcraTTdtLlW7RsMU8vrxBUJDqzEABXzTgOPh61JYk3FX49sJWhxKFbVbiIGAmIBSEwAPvOOk6yWqCombANie6HcR8RVFFTvqXAuDeg5plodIooqR5RKw6zMJU4CVbk9ciCogpSDuT1SACY4ba6wuVbnUFS1QFMoCd53JgknI/LBRlI6nqeC1zJvFjav77lndFRSqCgkqZ2K2QEpXGIVJmZMT78Rmr1S21TLh9xaCVJVtAAdG443dDI6iTIjIM8H6nhtjHlpPJLdBxxJJE1wB3781B1aVvFz08baaypRbVLFT+DL6gwl4D/AKgRO3zClJzM/N177aDkq0w6pS3VKbSopU4kJkCIVG8JEqAmIIjcO44fKzWdCl4JWoguQ4oU5IU3KfmGJPXoTjaDBOSyXbUNRdg6UMU6AILinlFtpsp9KgqfUScnsRuVEZ4iG2U0Z5ZKmniWslHtYCckUdnsjSE/MtKgQ0D5ra0gnBmMED7iSmTPDbeuZaUUv4WnpVAiQVuulanTtMkZGCE9OhIEqGOGt2203nbqm4gtok7W/SYxClKx0ke84k9uNC9Z2jTrS26SnogYkIEOEHAAkjpP92AYBnIjsC5o2OgIbNUCR4JaXu+iT3mvvWsStbqXVNbhmPQ0DnuAJCY/KN0caG9KKSy47VVakectSpEEOgHHX1KIG3MSYUJMbuGi981Kisd3NNhtIJ3VDxJISZEJQI9IAkTkQfbhLqyx6itOkbPqCrdZVb9Qh80ZYqWlv7miG3C41uLjY3KTAWhO5MlMgSB01RTt5kuKIwwVbwAAGD6p5uNVTUdSVIaUQJhdSvZB3dSSSRInIJMHBE4YL3qAKWtJdCHQsNKQlA2LV/dM4lWMEHOfdPDPT0z90c8xwvKQg+YlO+VqH1OYMdDAJOI6J4dKbRRQlC1+YzTwdqXG43JEhR6QTux9SJI7cRw58v8ALauznxQfzXZKa13AMuIUhourKlQVLEuJCjncRtAOe3QTk8am6qrr/MCFKebUoqcUBO8kzJHyicH69568ShvS9tpadYJW69O1aHQAhgHaUGAI/vEg4iPm4V1KGrXTpKklTu1C/LCTAB+UlShJmTnJg/fjYUJzl68fdWYwwKM1Gj6hawalCgNkpLiyklEY6diM47YzMl0suiXq4LlBQ0mCpayUBXQ4yEnJGZzEAGOJHY7Ebw75r6EBCyklJgKcEzBIMzgZxOegJ4mFo0+0H3F1I2q3iCvcFSTAMYkmT0jr24K0llD3ZIwPVL9z4j8JuAd/T9FHNJ8tUpZBUgBxKtylLJBQTM4jHvhMwD0kcEiz0NDpy3pS6yhbjigpZBA2pzJ+oyM46TnoH/RnJ+5aq5aas1LbBQVFs0Wyy7cQ7UtNPoQ6vYkpbJCljcIJSDtj7HgXXjUKHEEVD4bpwobokFZIzmDIPt1jMcNFP4NID4YyRsfRItQ+ouTsOJ0/mpQNXVFSSGUFKSsALSoIC4zHvP8A3fSCOnE35Cc+9OcteZ7FdqjRrevbKhh5ldncqlU6X1qbKUuFaQSCgkKg9oMjB4DaKev1KyNjAo6FtJ8x4pgARClDsrp3Kj6pMdvtRqe36etqkMrWt11vzHSuApQCyEpVgERBJAI3bs9xxCqb2NDmHJz6kfUbhFaPhUPcHEAY9M/PKfrle6u8PbWB5QSvcYWkpBzAKp9xBI9u8kcRO+XujsxK11FHV1TcBkBY8hoA/UErJHsOgSSep4jGseb9RVOJpKVQNIQAIbW0l5sFSZ2rKlAjPXcTOZMAQ+vrCismoKnKhTaZUtsDaT0ABEgQSQJzEnsCs1l4mndlyd7fw/T0rQ1v/tPV/wBf1lY44lsuLWAStSVGDEAkGCTnbg4x2nEZW+usfacfLjhXKdrhBSoASIyBsAnqQOo77uNdK6h5QK0hpICU7lrCUpQSYPtjBJIkwB7wjuFf57bhDaPIQrzl78hcLgFQ+aDunqRJABgmA8tTpGuQpjp6MuIZGEmump3a8ww4UsFQKwkkAp3EeoiNxicDunIAJPEVHlqblAWlZaClbUNkNgpPcknJwcA/NOYHC66ViHLgoy3UshxI3rbWTUtJ/IITuSFETJ27SoCAUcM1UVuLaZQSp9W2nQC0loOLBUFLBCgEeooBJEQTKsAlQr690rtinW325kDduaVOsihaCnUrZQEqWUrBKS3sSUqAUTBVuc9RTHyx88hHX3NxRcpw2ZbXKEJRKUOGUyNpAwUExEQ2MQI4SVr6nN623AG3CopLbYKXCAolyPYSlIPcJkiAY1vLUKJ5e90tpIQBEpG5BAWkKTJK5UoElJwkEdJGiXCI6M7JYh5TCg88pxPkblpb8zzFJVu3+YU4KmiASNpyUJz04SXS6O07boSahJaP76HE7Wdx2qSpSTCjCkgHPUpPRQKC5VqXANxSkJCH/wDo4dkJ3Ix1CSRAxuCZx3bbm+l6pUt9aklSNiFJWHFEDYCd0dQEnviAYHfwzu6LBCMp0oLZW3x5VPQMVVatphbpaoWFvOIS0hS1OKCc7EphaiISkbj2KeGRTrTTbLhbS4pSDKSUFtQBKj83ypmT0g7o6Agyrlhzu1RyNvNzuej75WadrLza6vT9Y5TqG6ooKtHl1DCpGQ62SVGJGIAUBxDK15KEtqa/d7AXQVJgxO1PXB2AACIwD7ccMldgMJVR3B2ibDoUlgtKMlELQDGSqAPTBSIknr7gceaqpaq1uLDyVBLhg7yd0jr2OcSoAElPQmeEVe8tK1rG6Hwpzes/NBIEKAz0juCQT7cfKja1WLCiFlFQoJJAEwCJKuv6TGCep48WKa3fxAapv/Ja08tnryv/AIO07d6q/wBBb1tso/DV1Uhtp51LoHmHclCRtUspGyREq4YtIctr1r+k1BUWWyV17p9OW1d2ujlHSKWi10aVoaVUPFKf3bYU42lSyAAVYIJkMjZkZS2D+Yx8qRGenYDMZMx7jj7b71WW2iqksVD9MiuQaZ4NOKbTUtSFKaUUwCkHaSk+nCcSAeMXmegW+0WOr1FWoYpqZysqXHEBLbTe8vAdCAnqPYAdBGSDCW+WOssFc5S1yV0dUw8ULQ6Cktmckp/rp9eJFR1N95K6xttexUs092pm6e5UjrLiXsOJStsSCU7gDlJEgjj7q6/ap8S3My9XutS9fNS3RL10r1ssoQVJbRvdXAACdqETjsDkk8YsIX//2Q==" />
These are GGUF quantized versions of Envoid/Fish-8x7B.
The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using 'URL'.
The IQ2_XXS and IQ2_XS versions are compatible with URL, version '147b17a' or later. The IQ3_XXS requires version 'f4d7e54' or later.
Some model files above 50GB are split into smaller files. To concatenate them, use the 'cat' command (on Windows, use PowerShell): 'cat foo-Q6_K.gguf.* > foo-Q6_K.gguf' | [] | [
"TAGS\n#gguf #not-for-all-audiences #en #license-cc-by-nc-4.0 #region-us \n"
] | [
31
] | [
"passage: TAGS\n#gguf #not-for-all-audiences #en #license-cc-by-nc-4.0 #region-us \n"
] | [
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] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "225.40 +/- 73.06", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | smcaleese/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-08T18:24:13+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | transformers |
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.4244471490383148
f1: 0.0
precision: 0.0
recall: 0.0
auc: 0.7067708333333333
accuracy: 0.8275862068965517
| {"tags": ["autotrain", "text-classification"], "datasets": ["autotrain-wewh0-x81n8/autotrain-data"], "widget": [{"text": "I love AutoTrain"}]} | text-classification | fecia/admissionBERT | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"autotrain",
"dataset:autotrain-wewh0-x81n8/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2024-02-08T18:28:04+00:00 | [] | [] | TAGS
#transformers #safetensors #roberta #text-classification #autotrain #dataset-autotrain-wewh0-x81n8/autotrain-data #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.4244471490383148
f1: 0.0
precision: 0.0
recall: 0.0
auc: 0.7067708333333333
accuracy: 0.8275862068965517
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Text Classification",
"## Validation Metrics\nloss: 0.4244471490383148\n\nf1: 0.0\n\nprecision: 0.0\n\nrecall: 0.0\n\nauc: 0.7067708333333333\n\naccuracy: 0.8275862068965517"
] | [
"TAGS\n#transformers #safetensors #roberta #text-classification #autotrain #dataset-autotrain-wewh0-x81n8/autotrain-data #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Text Classification",
"## Validation Metrics\nloss: 0.4244471490383148\n\nf1: 0.0\n\nprecision: 0.0\n\nrecall: 0.0\n\nauc: 0.7067708333333333\n\naccuracy: 0.8275862068965517"
] | [
68,
16,
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"passage: TAGS\n#transformers #safetensors #roberta #text-classification #autotrain #dataset-autotrain-wewh0-x81n8/autotrain-data #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Model Trained Using AutoTrain\n\n- Problem type: Text Classification## Validation Metrics\nloss: 0.4244471490383148\n\nf1: 0.0\n\nprecision: 0.0\n\nrecall: 0.0\n\nauc: 0.7067708333333333\n\naccuracy: 0.8275862068965517"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text2text-generation | language-plus-molecules/molt5-large-caption2smiles-LPM24 | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T18:32:55+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #t5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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## Evaluation
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## Environmental Impact
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[optional]
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## Model Card Contact
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"## Model Details",
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"passage: TAGS\n#transformers #safetensors #t5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | null | Distilbert trained on deepfaketextdetect | {"metrics": ["accuracy"]} | null | lyon0210/distilbert-deepfake-text | [
"region:us"
] | 2024-02-08T18:33:39+00:00 | [] | [] | TAGS
#region-us
| Distilbert trained on deepfaketextdetect | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
] | [
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null | null | peft | ## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
| {"library_name": "peft"} | null | Sakil/llama2-chat-hub-new-model-finetuned | [
"peft",
"region:us"
] | 2024-02-08T18:34:27+00:00 | [] | [] | TAGS
#peft #region-us
| ## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
| [
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16",
"### Framework versions\n\n\n- PEFT 0.4.0"
] | [
"TAGS\n#peft #region-us \n",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16",
"### Framework versions\n\n\n- PEFT 0.4.0"
] | [
9,
154,
11
] | [
"passage: TAGS\n#peft #region-us \n## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16### Framework versions\n\n\n- PEFT 0.4.0"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# perioli_vgm_v8
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0120
- Precision: 0.9340
- Recall: 0.9274
- F1: 0.9307
- Accuracy: 0.9981
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.32 | 100 | 0.0817 | 0.4667 | 0.2787 | 0.3490 | 0.9800 |
| No log | 0.64 | 200 | 0.0494 | 0.5981 | 0.5925 | 0.5953 | 0.9864 |
| No log | 0.96 | 300 | 0.0351 | 0.7578 | 0.7400 | 0.7488 | 0.9909 |
| No log | 1.29 | 400 | 0.0296 | 0.8333 | 0.7260 | 0.7760 | 0.9924 |
| 0.0728 | 1.61 | 500 | 0.0242 | 0.8480 | 0.8103 | 0.8287 | 0.9940 |
| 0.0728 | 1.93 | 600 | 0.0237 | 0.7564 | 0.7564 | 0.7564 | 0.9929 |
| 0.0728 | 2.25 | 700 | 0.0196 | 0.8067 | 0.8407 | 0.8234 | 0.9949 |
| 0.0728 | 2.57 | 800 | 0.0192 | 0.7982 | 0.8337 | 0.8156 | 0.9946 |
| 0.0728 | 2.89 | 900 | 0.0149 | 0.8642 | 0.8642 | 0.8642 | 0.9961 |
| 0.015 | 3.22 | 1000 | 0.0146 | 0.8695 | 0.8735 | 0.8715 | 0.9964 |
| 0.015 | 3.54 | 1100 | 0.0140 | 0.9346 | 0.9040 | 0.9190 | 0.9973 |
| 0.015 | 3.86 | 1200 | 0.0149 | 0.8886 | 0.9157 | 0.9020 | 0.9969 |
| 0.015 | 4.18 | 1300 | 0.0147 | 0.8924 | 0.9133 | 0.9028 | 0.9971 |
| 0.015 | 4.5 | 1400 | 0.0153 | 0.8628 | 0.9133 | 0.8874 | 0.9964 |
| 0.0075 | 4.82 | 1500 | 0.0155 | 0.9007 | 0.9133 | 0.9070 | 0.9971 |
| 0.0075 | 5.14 | 1600 | 0.0135 | 0.9190 | 0.9297 | 0.9243 | 0.9977 |
| 0.0075 | 5.47 | 1700 | 0.0153 | 0.8970 | 0.8970 | 0.8970 | 0.9970 |
| 0.0075 | 5.79 | 1800 | 0.0148 | 0.9348 | 0.9063 | 0.9203 | 0.9975 |
| 0.0075 | 6.11 | 1900 | 0.0158 | 0.8857 | 0.9251 | 0.9049 | 0.9970 |
| 0.0034 | 6.43 | 2000 | 0.0145 | 0.9189 | 0.9016 | 0.9102 | 0.9972 |
| 0.0034 | 6.75 | 2100 | 0.0140 | 0.9303 | 0.9063 | 0.9181 | 0.9975 |
| 0.0034 | 7.07 | 2200 | 0.0137 | 0.9062 | 0.8829 | 0.8944 | 0.9971 |
| 0.0034 | 7.4 | 2300 | 0.0150 | 0.9099 | 0.9227 | 0.9163 | 0.9971 |
| 0.0034 | 7.72 | 2400 | 0.0124 | 0.9195 | 0.9368 | 0.9281 | 0.9979 |
| 0.0022 | 8.04 | 2500 | 0.0119 | 0.9329 | 0.9438 | 0.9383 | 0.9980 |
| 0.0022 | 8.36 | 2600 | 0.0124 | 0.9370 | 0.9063 | 0.9214 | 0.9976 |
| 0.0022 | 8.68 | 2700 | 0.0124 | 0.9261 | 0.9391 | 0.9326 | 0.9979 |
| 0.0022 | 9.0 | 2800 | 0.0120 | 0.9409 | 0.9321 | 0.9365 | 0.9981 |
| 0.0022 | 9.32 | 2900 | 0.0122 | 0.9340 | 0.9274 | 0.9307 | 0.9981 |
| 0.0017 | 9.65 | 3000 | 0.0120 | 0.9340 | 0.9274 | 0.9307 | 0.9981 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu121
- Datasets 2.2.2
- Tokenizers 0.13.3
| {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["sroie"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "perioli_vgm_v8", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.9339622641509434, "name": "Precision"}, {"type": "recall", "value": 0.927400468384075, "name": "Recall"}, {"type": "f1", "value": 0.9306698002350177, "name": "F1"}, {"type": "accuracy", "value": 0.9981452418319304, "name": "Accuracy"}]}]}]} | token-classification | atatavana/vgm_v8 | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:sroie",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T18:40:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| perioli\_vgm\_v8
================
This model is a fine-tuned version of microsoft/layoutlmv3-base on the sroie dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0120
* Precision: 0.9340
* Recall: 0.9274
* F1: 0.9307
* Accuracy: 0.9981
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 3000
### Training results
### Framework versions
* Transformers 4.28.0
* Pytorch 2.1.0+cu121
* Datasets 2.2.2
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 3000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.2.2\n* Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 3000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.2.2\n* Tokenizers 0.13.3"
] | [
76,
97,
4,
35
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 3000### Training results### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.2.2\n* Tokenizers 0.13.3"
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null | null | null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Jarles/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Jarles/q-FrozenLake-v1-4x4-noSlippery | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-08T18:44:44+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
40,
39
] | [
"passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
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null | null | null |
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Jarles/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.48 +/- 2.73", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Jarles/Taxi-v3 | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-08T18:47:20+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
32,
33
] | [
"passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
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null | null | peft |
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## Model Details
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
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## Evaluation
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "mistralai/Mistral-7B-Instruct-v0.2"} | null | pikaduck/Mistral-7B-Instruct-v0.2-cv-editor | [
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# Model Card for Model ID
## Model Details
### Model Description
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- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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- Carbon Emitted:
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | aidonuts/corgy-001 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T18:52:42+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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## Uses
### Direct Use
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## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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### Training Procedure
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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[optional]
BibTeX:
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## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## Training Details",
"### Training Data",
"### Training Procedure",
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"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
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"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BOLETIN_4bit_13
This model is a fine-tuned version of [bertin-project/BOLETIN](https://huggingface.co/bertin-project/BOLETIN) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.41e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.1 | {"license": "openrail", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "bertin-project/BOLETIN", "model-index": [{"name": "BOLETIN_4bit_13", "results": []}]} | null | versae/BOLETIN_4bit_13 | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:bertin-project/BOLETIN",
"license:openrail",
"region:us"
] | 2024-02-08T18:53:32+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-bertin-project/BOLETIN #license-openrail #region-us
|
# BOLETIN_4bit_13
This model is a fine-tuned version of bertin-project/BOLETIN on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.41e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.1 | [
"# BOLETIN_4bit_13\n\nThis model is a fine-tuned version of bertin-project/BOLETIN on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1.41e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-bertin-project/BOLETIN #license-openrail #region-us \n",
"# BOLETIN_4bit_13\n\nThis model is a fine-tuned version of bertin-project/BOLETIN on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1.41e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.1"
] | [
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39
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"passage: TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-bertin-project/BOLETIN #license-openrail #region-us \n# BOLETIN_4bit_13\n\nThis model is a fine-tuned version of bertin-project/BOLETIN on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1.41e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5819
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.1515 |
| 2.6574 | 2.0 | 500 | 1.6337 |
| 2.6574 | 3.0 | 750 | 1.5819 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "my_awesome_qa_model", "results": []}]} | question-answering | DanielAvelar09/my_awesome_qa_model | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-08T18:53:46+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #distilbert #question-answering #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us
| my\_awesome\_qa\_model
======================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5819
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
65,
98,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #question-answering #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-bn-adapter-895K-squad-model1
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 76
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-base-cased-bn-adapter-895K-squad-model1", "results": []}]} | null | varun-v-rao/bert-base-cased-bn-adapter-895K-squad-model1 | [
"tensorboard",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:bert-base-cased",
"license:apache-2.0",
"region:us"
] | 2024-02-08T18:54:27+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-bert-base-cased #license-apache-2.0 #region-us
|
# bert-base-cased-bn-adapter-895K-squad-model1
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 76
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"# bert-base-cased-bn-adapter-895K-squad-model1\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 76\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-bert-base-cased #license-apache-2.0 #region-us \n",
"# bert-base-cased-bn-adapter-895K-squad-model1\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 76\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
49,
45,
6,
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8,
3,
90,
4,
33
] | [
"passage: TAGS\n#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-bert-base-cased #license-apache-2.0 #region-us \n# bert-base-cased-bn-adapter-895K-squad-model1\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 76\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
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