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<!-- 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 | SateeshAmbesange/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"
] | 2024-02-11T15:57:41+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,
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"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 |
## Solarized-18B-truthy
Solarized-18B-dpo fine-tuned to improve truthfulness.
It is a frankenmerge model created using mergekit, alternating layers of Nous-Hermes-2-SOLAR-10.7B and SOLAR-10.7B-Instruct. Then, we applied DPO over a high-quality preference dataset.
 | {"license": "apache-2.0", "datasets": ["jondurbin/truthy-dpo-v0.1"]} | text-generation | vicgalle/solarized-18B-truthy | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:jondurbin/truthy-dpo-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | 2024-02-11T15:59:29+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #dataset-jondurbin/truthy-dpo-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us
|
## Solarized-18B-truthy
Solarized-18B-dpo fine-tuned to improve truthfulness.
It is a frankenmerge model created using mergekit, alternating layers of Nous-Hermes-2-SOLAR-10.7B and SOLAR-10.7B-Instruct. Then, we applied DPO over a high-quality preference dataset.
!image/png | [
"## Solarized-18B-truthy\n\nSolarized-18B-dpo fine-tuned to improve truthfulness.\n\nIt is a frankenmerge model created using mergekit, alternating layers of Nous-Hermes-2-SOLAR-10.7B and SOLAR-10.7B-Instruct. Then, we applied DPO over a high-quality preference dataset.\n\n!image/png"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #dataset-jondurbin/truthy-dpo-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n",
"## Solarized-18B-truthy\n\nSolarized-18B-dpo fine-tuned to improve truthfulness.\n\nIt is a frankenmerge model created using mergekit, alternating layers of Nous-Hermes-2-SOLAR-10.7B and SOLAR-10.7B-Instruct. Then, we applied DPO over a high-quality preference dataset.\n\n!image/png"
] | [
79,
85
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #dataset-jondurbin/truthy-dpo-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n## Solarized-18B-truthy\n\nSolarized-18B-dpo fine-tuned to improve truthfulness.\n\nIt is a frankenmerge model created using mergekit, alternating layers of Nous-Hermes-2-SOLAR-10.7B and SOLAR-10.7B-Instruct. Then, we applied DPO over a high-quality preference dataset.\n\n!image/png"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | longcule123/adapter_vistral_book_merge | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T16:00:24+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
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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
#### 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 Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"## Model Card Contact"
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"## Model Details",
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"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Out-of-Scope Use",
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"### 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",
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"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
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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"
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"passage: TAGS\n#transformers #safetensors #mistral #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 | transformers |
# Model Summary
> GritLM is a generative representational instruction tuned language model. It unifies text representation (embedding) and text generation into a single model achieving state-of-the-art performance on both types of tasks.
- **Repository:** [ContextualAI/gritlm](https://github.com/ContextualAI/gritlm)
- **Paper:** https://arxiv.org/abs/2402.09906
- **Logs:** https://wandb.ai/muennighoff/gritlm/runs/id130s1m/overview
- **Script:** https://github.com/ContextualAI/gritlm/blob/main/scripts/training/train_gritlm_8x7b.sh
| Model | Description |
|-------|-------------|
| [GritLM 7B](https://hf.co/GritLM/GritLM-7B) | Mistral 7B finetuned using GRIT |
| [GritLM 8x7B](https://hf.co/GritLM/GritLM-8x7B) | Mixtral 8x7B finetuned using GRIT |
# Use
The model usage is documented [here](https://github.com/ContextualAI/gritlm?tab=readme-ov-file#inference).
# Citation
```bibtex
@misc{muennighoff2024generative,
title={Generative Representational Instruction Tuning},
author={Niklas Muennighoff and Hongjin Su and Liang Wang and Nan Yang and Furu Wei and Tao Yu and Amanpreet Singh and Douwe Kiela},
year={2024},
eprint={2402.09906},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | {"license": "apache-2.0", "tags": ["mteb"], "datasets": ["GritLM/tulu2"], "pipeline_tag": "text-generation", "inference": true, "model-index": [{"name": "GritLM-8x7B", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 80.47761194029852}, {"type": "ap", "value": 44.38751347932197}, {"type": "f1", "value": 74.33580162208256}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonPolarityClassification", "type": "mteb/amazon_polarity", "config": "default", "split": "test", "revision": "e2d317d38cd51312af73b3d32a06d1a08b442046"}, "metrics": [{"type": "accuracy", "value": 96.32155000000002}, {"type": "ap", "value": 94.8026654593679}, {"type": "f1", "value": 96.3209869463974}]}, {"task": {"type": "Classification"}, "dataset": {"name": 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"value": 83.9621188789652}, {"type": "manhattan_accuracy", "value": 89.34877944657896}, {"type": "manhattan_ap", "value": 86.35336214205911}, {"type": "manhattan_f1", "value": 79.20192588269623}, {"type": "manhattan_precision", "value": 75.24951483227058}, {"type": "manhattan_recall", "value": 83.59254696643055}, {"type": "max_accuracy", "value": 89.42639810610471}, {"type": "max_ap", "value": 86.45196525133669}, {"type": "max_f1", "value": 79.25172592977508}]}]}]} | text-generation | GritLM/GritLM-8x7B | [
"transformers",
"pytorch",
"safetensors",
"mixtral",
"text-generation",
"mteb",
"conversational",
"custom_code",
"dataset:GritLM/tulu2",
"arxiv:2402.09906",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T16:02:26+00:00 | [
"2402.09906"
] | [] | TAGS
#transformers #pytorch #safetensors #mixtral #text-generation #mteb #conversational #custom_code #dataset-GritLM/tulu2 #arxiv-2402.09906 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Summary
=============
>
> GritLM is a generative representational instruction tuned language model. It unifies text representation (embedding) and text generation into a single model achieving state-of-the-art performance on both types of tasks.
>
>
>
* Repository: ContextualAI/gritlm
* Paper: URL
* Logs: URL
* Script: URL
Use
===
The model usage is documented here.
| [] | [
"TAGS\n#transformers #pytorch #safetensors #mixtral #text-generation #mteb #conversational #custom_code #dataset-GritLM/tulu2 #arxiv-2402.09906 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
96
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #mixtral #text-generation #mteb #conversational #custom_code #dataset-GritLM/tulu2 #arxiv-2402.09906 #license-apache-2.0 #model-index #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. -->
# bert-base-banking77-pt2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3136
- F1: 0.9301
## 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: 8
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1185 | 1.0 | 626 | 0.8087 | 0.8360 |
| 0.3789 | 2.0 | 1252 | 0.3784 | 0.9206 |
| 0.1792 | 3.0 | 1878 | 0.3136 | 0.9301 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.2+cu118
- Datasets 2.13.0
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-banking77-pt2", "results": []}]} | text-classification | vvuri/bert-base-banking77-pt2 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T16:03:02+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-banking77-pt2
=======================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3136
* F1: 0.9301
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: 8
* 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 4.34.0
* Pytorch 2.1.2+cu118
* Datasets 2.13.0
* Tokenizers 0.14.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.34.0\n* Pytorch 2.1.2+cu118\n* Datasets 2.13.0\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: 5e-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",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.34.0\n* Pytorch 2.1.2+cu118\n* Datasets 2.13.0\n* Tokenizers 0.14.1"
] | [
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"passage: TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-bert-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: 5e-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### Training results### Framework versions\n\n\n* Transformers 4.34.0\n* Pytorch 2.1.2+cu118\n* Datasets 2.13.0\n* Tokenizers 0.14.1"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | tommymarto/LernnaviBERT_mcqbert1_students_answers_4096_mistral_seq_len_10 | [
"transformers",
"safetensors",
"bert",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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## 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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#### Speeds, Sizes, Times [optional]
## Evaluation
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#### Testing Data
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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 |
# Phi-2 model fine-tuned for named entity recognition task
The model was fine-tuned using one quarter of the ConLL 2012 OntoNotes v5 dataset.
- Dataset Source: [conll2012_ontonotesv5](https://huggingface.co/datasets/conll2012_ontonotesv5)
- Subset Used: English_v12
- Number of Examples: 21,817
The prompts and expected outputs were constructed as described in [1].
Example input:
```md
I am an excelent linquist. The task is to label location entities in the given sentence. Below are some examples
Input: Only France and Britain backed Fischler's proposal.
Output: Only @@France## and @@Britain## backed Fischler's proposal.
Input: Germany imported 47,000 sheeps from Britain last year, nearly half of total imports.
Output: @@Germany## imported 47,000 sheeps from @@Britain## last year, nearly half of total imports.
Input: It brought in 4275 tonnes of British mutton, some 10% of overall imports.
Output: It brought in 4275 tonnes of British mutton, some 10% of overall imports.
Input: China says Taiwan spoils atmosphere for talks.
Output:
```
Expected output:
```md
@@China## says @@Taiwan## spoils atmosphere for talks.
```
# Model Trained Using AutoTrain
This model was trained using **SFT** AutoTrain trainer. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
Hyperparameters:
```json
{
"model": "pahautelman/phi2-ner",
"train_split": "train",
"valid_split": null,
"add_eos_token": false,
"block_size": 1024,
"model_max_length": 1024,
"padding": null,
"trainer": "sft",
"use_flash_attention_2": false,
"log": "none",
"disable_gradient_checkpointing": false,
"logging_steps": -1,
"evaluation_strategy": "epoch",
"save_total_limit": 1,
"save_strategy": "epoch",
"auto_find_batch_size": false,
"mixed_precision": "fp16",
"lr": 0.0002,
"epochs": 1,
"batch_size": 1,
"warmup_ratio": 0.1,
"gradient_accumulation": 4,
"optimizer": "adamw_torch",
"scheduler": "linear",
"weight_decay": 0.01,
"max_grad_norm": 1.0,
"seed": 42,
"apply_chat_template": false,
"quantization": "int4",
"target_modules": null,
"merge_adapter": false,
"peft": true,
"lora_r": 16,
"lora_alpha": 32,
"lora_dropout": 0.05,
"model_ref": null,
"dpo_beta": 0.1,
}
```
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "pahautelman/phi2-ner-sft-v1"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path
).eval()
prompt = 'Label the person entities in the given sentence: Russian President Vladimir Putin is due to arrive in Havana a few hours from now to become the first post-Soviet leader to visit Cuba.'
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors='pt')
outputs = model.generate(
inputs.to(model.device),
max_new_tokens=9,
do_sample=False,
)
output = tokenizer.batch_decode(outputs)[0]
# Model response: "Output: Russian President, Vladimir Putin"
print(output)
```
# References:
[1] Wang et al., GPT-NER: Named entity recognition via large language models 2023 | {"language": ["en"], "license": "mit", "tags": ["autotrain", "text-generation"], "datasets": ["conll2012_ontonotesv5"], "widget": [{"text": "I love AutoTrain because "}]} | text-generation | pahautelman/phi2-ner-sft-v1 | [
"transformers",
"safetensors",
"phi",
"text-generation",
"autotrain",
"conversational",
"custom_code",
"en",
"dataset:conll2012_ontonotesv5",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
] | 2024-02-11T16:07:19+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #phi #text-generation #autotrain #conversational #custom_code #en #dataset-conll2012_ontonotesv5 #license-mit #autotrain_compatible #endpoints_compatible #4-bit #region-us
|
# Phi-2 model fine-tuned for named entity recognition task
The model was fine-tuned using one quarter of the ConLL 2012 OntoNotes v5 dataset.
- Dataset Source: conll2012_ontonotesv5
- Subset Used: English_v12
- Number of Examples: 21,817
The prompts and expected outputs were constructed as described in [1].
Example input:
Expected output:
# Model Trained Using AutoTrain
This model was trained using SFT AutoTrain trainer. For more information, please visit AutoTrain.
Hyperparameters:
# Usage
# References:
[1] Wang et al., GPT-NER: Named entity recognition via large language models 2023 | [
"# Phi-2 model fine-tuned for named entity recognition task\nThe model was fine-tuned using one quarter of the ConLL 2012 OntoNotes v5 dataset.\n- Dataset Source: conll2012_ontonotesv5\n- Subset Used: English_v12\n- Number of Examples: 21,817\n \nThe prompts and expected outputs were constructed as described in [1].\n\nExample input:\n\nExpected output:",
"# Model Trained Using AutoTrain\n\nThis model was trained using SFT AutoTrain trainer. For more information, please visit AutoTrain.\n\nHyperparameters:",
"# Usage",
"# References:\n[1] Wang et al., GPT-NER: Named entity recognition via large language models 2023"
] | [
"TAGS\n#transformers #safetensors #phi #text-generation #autotrain #conversational #custom_code #en #dataset-conll2012_ontonotesv5 #license-mit #autotrain_compatible #endpoints_compatible #4-bit #region-us \n",
"# Phi-2 model fine-tuned for named entity recognition task\nThe model was fine-tuned using one quarter of the ConLL 2012 OntoNotes v5 dataset.\n- Dataset Source: conll2012_ontonotesv5\n- Subset Used: English_v12\n- Number of Examples: 21,817\n \nThe prompts and expected outputs were constructed as described in [1].\n\nExample input:\n\nExpected output:",
"# Model Trained Using AutoTrain\n\nThis model was trained using SFT AutoTrain trainer. For more information, please visit AutoTrain.\n\nHyperparameters:",
"# Usage",
"# References:\n[1] Wang et al., GPT-NER: Named entity recognition via large language models 2023"
] | [
72,
95,
37,
3,
25
] | [
"passage: TAGS\n#transformers #safetensors #phi #text-generation #autotrain #conversational #custom_code #en #dataset-conll2012_ontonotesv5 #license-mit #autotrain_compatible #endpoints_compatible #4-bit #region-us \n# Phi-2 model fine-tuned for named entity recognition task\nThe model was fine-tuned using one quarter of the ConLL 2012 OntoNotes v5 dataset.\n- Dataset Source: conll2012_ontonotesv5\n- Subset Used: English_v12\n- Number of Examples: 21,817\n \nThe prompts and expected outputs were constructed as described in [1].\n\nExample input:\n\nExpected output:# Model Trained Using AutoTrain\n\nThis model was trained using SFT AutoTrain trainer. For more information, please visit AutoTrain.\n\nHyperparameters:# Usage# References:\n[1] Wang et al., GPT-NER: Named entity recognition via large language models 2023"
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null | null | diffusers |
# DreamBooth trained by AutoTrain
Text encoder was not trained.
| {"tags": ["text-to-image", "diffusers", "autotrain"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "plants versus zombies 2 character", "inference": true} | text-to-image | calypso604/Gen-AI-Challenge-Info | [
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"has_space",
"region:us"
] | 2024-02-11T16:10:20+00:00 | [] | [] | TAGS
#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us
|
# DreamBooth trained by AutoTrain
Text encoder was not trained.
| [
"# DreamBooth trained by AutoTrain\n\nText encoder was not trained."
] | [
"TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n",
"# DreamBooth trained by AutoTrain\n\nText encoder was not trained."
] | [
45,
19
] | [
"passage: TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n# DreamBooth trained by AutoTrain\n\nText encoder was not trained."
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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. -->
# mBART_try_v1
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.179 | 0.79 | 500 | 0.0000 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "facebook/mbart-large-50", "model-index": [{"name": "mBART_try_v1", "results": []}]} | text2text-generation | houdini001/mBART_try_v1 | [
"transformers",
"tensorboard",
"safetensors",
"mbart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/mbart-large-50",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T16:16:44+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #mbart #text2text-generation #generated_from_trainer #base_model-facebook/mbart-large-50 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| mBART\_try\_v1
==============
This model is a fine-tuned version of facebook/mbart-large-50 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
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: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.37.0
* Pytorch 2.1.2
* Datasets 2.1.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\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\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #mbart #text2text-generation #generated_from_trainer #base_model-facebook/mbart-large-50 #license-mit #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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\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\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1"
] | [
69,
98,
4,
30
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #mbart #text2text-generation #generated_from_trainer #base_model-facebook/mbart-large-50 #license-mit #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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\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\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1"
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null | null | transformers | # Konstanta-V2-Gamma
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 passthrough merge method.
### Models Merged
The following models were included in the merge:
* [Inv/Konstanta-7B](https://huggingface.co/Inv/Konstanta-7B)
* [Inv/Konstanta-Alpha-V2-7B](https://huggingface.co/Inv/Konstanta-Alpha-V2-7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Inv/Konstanta-Alpha-V2-7B
layer_range: [0, 12]
- sources:
- model: Inv/Konstanta-7B
layer_range: [6, 18]
- sources:
- model: Inv/Konstanta-Alpha-V2-7B
layer_range: [13, 25]
- sources:
- model: Inv/Konstanta-7B
layer_range: [19, 32]
merge_method: passthrough
dtype: float16
``` | {"language": ["en", "ru"], "license": "apache-2.0", "library_name": "transformers", "tags": ["mergekit", "merge", "upscaled", "rp", "roleplay", "not-for-all-audiences"], "base_model": ["Inv/Konstanta-7B", "Inv/Konstanta-Alpha-V2-7B"]} | text-generation | Inv/Konstanta-Gamma-10.9B | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"upscaled",
"rp",
"roleplay",
"not-for-all-audiences",
"en",
"ru",
"base_model:Inv/Konstanta-7B",
"base_model:Inv/Konstanta-Alpha-V2-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T16:19:33+00:00 | [] | [
"en",
"ru"
] | TAGS
#transformers #safetensors #mistral #text-generation #mergekit #merge #upscaled #rp #roleplay #not-for-all-audiences #en #ru #base_model-Inv/Konstanta-7B #base_model-Inv/Konstanta-Alpha-V2-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Konstanta-V2-Gamma
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the passthrough merge method.
### Models Merged
The following models were included in the merge:
* Inv/Konstanta-7B
* Inv/Konstanta-Alpha-V2-7B
### Configuration
The following YAML configuration was used to produce this model:
| [
"# Konstanta-V2-Gamma\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the passthrough merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Inv/Konstanta-7B\n* Inv/Konstanta-Alpha-V2-7B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #upscaled #rp #roleplay #not-for-all-audiences #en #ru #base_model-Inv/Konstanta-7B #base_model-Inv/Konstanta-Alpha-V2-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Konstanta-V2-Gamma\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the passthrough merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Inv/Konstanta-7B\n* Inv/Konstanta-Alpha-V2-7B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
117,
24,
4,
17,
39,
17
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #upscaled #rp #roleplay #not-for-all-audiences #en #ru #base_model-Inv/Konstanta-7B #base_model-Inv/Konstanta-Alpha-V2-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Konstanta-V2-Gamma\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the passthrough merge method.### Models Merged\n\nThe following models were included in the merge:\n* Inv/Konstanta-7B\n* Inv/Konstanta-Alpha-V2-7B### Configuration\n\nThe following YAML configuration was used to produce this model:"
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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. -->
# finetuning-b2b
This model is a fine-tuned version of [dura-garage/nepberta2nepberta](https://huggingface.co/dura-garage/nepberta2nepberta) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.0038 | 0.5 | 1000 | 0.0012 |
| 0.0009 | 1.0 | 2000 | 0.0007 |
| 0.0052 | 1.5 | 3000 | 0.0002 |
| 0.0069 | 2.0 | 4000 | 0.0002 |
| 0.0011 | 2.5 | 5000 | 0.0003 |
| 0.0045 | 3.0 | 6000 | 0.0007 |
| 0.0012 | 3.5 | 7000 | 0.0002 |
| 0.0028 | 4.0 | 8000 | 0.0001 |
| 0.0001 | 4.5 | 9000 | 0.0004 |
| 0.0001 | 5.0 | 10000 | 0.0000 |
| 0.0092 | 5.5 | 11000 | 0.0001 |
| 0.0006 | 6.0 | 12000 | 0.0002 |
| 0.0003 | 6.5 | 13000 | 0.0000 |
| 0.0057 | 7.0 | 14000 | 0.0000 |
| 0.0 | 7.5 | 15000 | 0.0000 |
| 0.0093 | 8.0 | 16000 | 0.0000 |
| 0.03 | 8.5 | 17000 | 0.0002 |
| 0.0144 | 9.0 | 18000 | 0.0004 |
| 0.0018 | 9.5 | 19000 | 0.0000 |
| 0.0024 | 10.0 | 20000 | 0.0000 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "base_model": "dura-garage/nepberta2nepberta", "model-index": [{"name": "finetuning-b2b", "results": []}]} | text2text-generation | duraad/finetuning-b2b | [
"transformers",
"safetensors",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"base_model:dura-garage/nepberta2nepberta",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T16:24:55+00:00 | [] | [] | TAGS
#transformers #safetensors #encoder-decoder #text2text-generation #generated_from_trainer #base_model-dura-garage/nepberta2nepberta #autotrain_compatible #endpoints_compatible #region-us
| finetuning-b2b
==============
This model is a fine-tuned version of dura-garage/nepberta2nepberta on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
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: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.37.0
* Pytorch 2.1.2
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\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: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
69,
98,
4,
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] | [
"passage: TAGS\n#transformers #safetensors #encoder-decoder #text2text-generation #generated_from_trainer #base_model-dura-garage/nepberta2nepberta #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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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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. -->
[<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: codellama/CodeLlama-7b-hf
base_model_config: codellama/CodeLlama-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true
hub_model_id: EvolCodeLlama-7b
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: mlabonne/Evol-Instruct-Python-1k
type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.02
output_dir: ./qlora-out
adapter: qlora
lora_model_dir:
sequence_len: 2048
sample_packing: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: FTCodeLlama-2
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 2
micro_batch_size: 4
num_epochs: 3
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
eval_steps: 0.01
save_strategy: epoch
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
```
</details><br>
# EvolCodeLlama-7b
This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3754
## 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: 4
- eval_batch_size: 4
- 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: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3686 | 0.01 | 1 | 0.5015 |
| 0.4397 | 0.03 | 3 | 0.5013 |
| 0.4919 | 0.06 | 6 | 0.5013 |
| 0.3191 | 0.09 | 9 | 0.5011 |
| 0.2514 | 0.12 | 12 | 0.5003 |
| 0.3379 | 0.15 | 15 | 0.4992 |
| 0.4712 | 0.19 | 18 | 0.4969 |
| 0.3801 | 0.22 | 21 | 0.4922 |
| 0.3482 | 0.25 | 24 | 0.4833 |
| 0.4113 | 0.28 | 27 | 0.4702 |
| 0.2524 | 0.31 | 30 | 0.4552 |
| 0.2641 | 0.34 | 33 | 0.4415 |
| 0.3554 | 0.37 | 36 | 0.4302 |
| 0.2384 | 0.4 | 39 | 0.4213 |
| 0.2131 | 0.43 | 42 | 0.4153 |
| 0.2308 | 0.46 | 45 | 0.4105 |
| 0.3478 | 0.49 | 48 | 0.4053 |
| 0.2913 | 0.53 | 51 | 0.4003 |
| 0.2909 | 0.56 | 54 | 0.3956 |
| 0.2032 | 0.59 | 57 | 0.3928 |
| 0.2479 | 0.62 | 60 | 0.3906 |
| 0.2145 | 0.65 | 63 | 0.3890 |
| 0.2447 | 0.68 | 66 | 0.3882 |
| 0.2928 | 0.71 | 69 | 0.3876 |
| 0.384 | 0.74 | 72 | 0.3854 |
| 0.1751 | 0.77 | 75 | 0.3835 |
| 0.352 | 0.8 | 78 | 0.3818 |
| 0.2443 | 0.84 | 81 | 0.3808 |
| 0.3211 | 0.87 | 84 | 0.3798 |
| 0.3026 | 0.9 | 87 | 0.3788 |
| 0.2357 | 0.93 | 90 | 0.3776 |
| 0.2661 | 0.96 | 93 | 0.3755 |
| 0.3314 | 0.99 | 96 | 0.3751 |
| 0.2789 | 1.02 | 99 | 0.3742 |
| 0.1734 | 1.03 | 102 | 0.3744 |
| 0.1928 | 1.06 | 105 | 0.3761 |
| 0.2681 | 1.09 | 108 | 0.3753 |
| 0.4148 | 1.12 | 111 | 0.3750 |
| 0.1977 | 1.15 | 114 | 0.3744 |
| 0.1977 | 1.19 | 117 | 0.3740 |
| 0.2499 | 1.22 | 120 | 0.3742 |
| 0.2192 | 1.25 | 123 | 0.3730 |
| 0.2207 | 1.28 | 126 | 0.3723 |
| 0.2179 | 1.31 | 129 | 0.3718 |
| 0.2843 | 1.34 | 132 | 0.3734 |
| 0.2614 | 1.37 | 135 | 0.3721 |
| 0.2033 | 1.4 | 138 | 0.3705 |
| 0.212 | 1.43 | 141 | 0.3705 |
| 0.2307 | 1.46 | 144 | 0.3712 |
| 0.3182 | 1.49 | 147 | 0.3698 |
| 0.2467 | 1.53 | 150 | 0.3664 |
| 0.1909 | 1.56 | 153 | 0.3665 |
| 0.3286 | 1.59 | 156 | 0.3655 |
| 0.2195 | 1.62 | 159 | 0.3648 |
| 0.3231 | 1.65 | 162 | 0.3650 |
| 0.2922 | 1.68 | 165 | 0.3631 |
| 0.1956 | 1.71 | 168 | 0.3627 |
| 0.2299 | 1.74 | 171 | 0.3639 |
| 0.1585 | 1.77 | 174 | 0.3649 |
| 0.2289 | 1.8 | 177 | 0.3650 |
| 0.189 | 1.84 | 180 | 0.3643 |
| 0.2736 | 1.87 | 183 | 0.3628 |
| 0.3591 | 1.9 | 186 | 0.3614 |
| 0.3181 | 1.93 | 189 | 0.3612 |
| 0.1994 | 1.96 | 192 | 0.3612 |
| 0.2499 | 1.99 | 195 | 0.3618 |
| 0.1659 | 2.01 | 198 | 0.3627 |
| 0.231 | 2.04 | 201 | 0.3665 |
| 0.169 | 2.07 | 204 | 0.3744 |
| 0.2082 | 2.1 | 207 | 0.3800 |
| 0.1755 | 2.13 | 210 | 0.3770 |
| 0.1959 | 2.16 | 213 | 0.3721 |
| 0.1933 | 2.19 | 216 | 0.3705 |
| 0.1213 | 2.22 | 219 | 0.3712 |
| 0.237 | 2.25 | 222 | 0.3738 |
| 0.2277 | 2.28 | 225 | 0.3771 |
| 0.2832 | 2.31 | 228 | 0.3789 |
| 0.2039 | 2.35 | 231 | 0.3783 |
| 0.2302 | 2.38 | 234 | 0.3764 |
| 0.1562 | 2.41 | 237 | 0.3750 |
| 0.1688 | 2.44 | 240 | 0.3742 |
| 0.126 | 2.47 | 243 | 0.3741 |
| 0.1846 | 2.5 | 246 | 0.3746 |
| 0.2195 | 2.53 | 249 | 0.3745 |
| 0.2335 | 2.56 | 252 | 0.3749 |
| 0.1542 | 2.59 | 255 | 0.3750 |
| 0.1783 | 2.62 | 258 | 0.3755 |
| 0.2409 | 2.65 | 261 | 0.3762 |
| 0.1777 | 2.69 | 264 | 0.3762 |
| 0.2591 | 2.72 | 267 | 0.3761 |
| 0.2092 | 2.75 | 270 | 0.3757 |
| 0.2256 | 2.78 | 273 | 0.3757 |
| 0.1923 | 2.81 | 276 | 0.3756 |
| 0.156 | 2.84 | 279 | 0.3755 |
| 0.2036 | 2.87 | 282 | 0.3754 |
| 0.2254 | 2.9 | 285 | 0.3753 |
| 0.1683 | 2.93 | 288 | 0.3753 |
| 0.1528 | 2.96 | 291 | 0.3754 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.17.0
- Tokenizers 0.15.0 | {"license": "llama2", "library_name": "peft", "tags": ["axolotl", "generated_from_trainer"], "base_model": "codellama/CodeLlama-7b-hf", "model-index": [{"name": "EvolCodeLlama-7b", "results": []}]} | null | peterhung/EvolCodeLlama-7b | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:codellama/CodeLlama-7b-hf",
"license:llama2",
"region:us"
] | 2024-02-11T16:26:21+00:00 | [] | [] | TAGS
#peft #safetensors #llama #axolotl #generated_from_trainer #base_model-codellama/CodeLlama-7b-hf #license-llama2 #region-us
| <img src="URL alt="Built with Axolotl" width="200" height="32"/>
See axolotl config
axolotl version: '0.4.0'
EvolCodeLlama-7b
================
This model is a fine-tuned version of codellama/CodeLlama-7b-hf on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3754
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: 4
* eval\_batch\_size: 4
* 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: cosine
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 3
### Training results
### Framework versions
* PEFT 0.8.2
* Transformers 4.38.0.dev0
* Pytorch 2.1.2+cu118
* Datasets 2.17.0
* Tokenizers 0.15.0
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"### Training results",
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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
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
- PEFT 0.4.0
| {"library_name": "peft"} | null | wesley7137/tiny_llama_shopper_adapter | [
"peft",
"region:us"
] | 2024-02-11T16:26:42+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
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
- 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\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- PEFT 0.4.0\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\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- PEFT 0.4.0\n\n- PEFT 0.4.0"
] | [
9,
305,
17
] | [
"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\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- PEFT 0.4.0\n\n- PEFT 0.4.0"
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null | null | transformers |
# Model Card for Model ID
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"transformers",
"safetensors",
"bert",
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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:
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- License:
- Finetuned from model [optional]:
### Model Sources [optional]
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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
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APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
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null | null | transformers | <center><img src='https://i.imgur.com/0xFTuAX.png' width='450px'></center>
# Pearl-7B-0211-ties, an xtraordinary 7B model
Pearl-7B-0211-ties is a merge of the following models:
* [louisbrulenaudet/Pearl-7B-slerp](https://huggingface.co/louisbrulenaudet/Pearl-7B-slerp)
* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
* [cognitivecomputations/WestLake-7B-v2-laser](https://huggingface.co/cognitivecomputations/WestLake-7B-v2-laser)
* [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo)
## Evaluation
The evaluation was performed using the HuggingFace Open LLM Leaderboard.
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | #Params (B) |
|--------------------------------------------------|---------|-------|-----------|-------|------------|------------|-------|--------------|
| **louisbrulenaudet/Pearl-34B-ties** | **75.48** | 70.99 | 84.83 | **76.63** | 70.32 | 82.64 | 67.48 | 34.39 |
| **louisbrulenaudet/Pearl-7B-0211-ties** | **75.11** | **71.42** | **88.86** | 63.91 | **71.46** | **84.37** | 70.66 | 7.24 |
| NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO | 73.35 | 71.08 | 87.29 | 72.17 | 54.83 | 83.11 | 71.65 | 46.7 |
| argilla/notus-8x7b-experiment | 73.18 | 70.99 | 87.73 | 71.33 | 65.79 | 81.61 | 61.64 | 46.7 |
| **louisbrulenaudet/Pearl-7B-slerp** | 72.75 | 68.00 | 87.16 | 64.04 | 62.35 | 81.29 | **73.62** | 7.24 |
| mistralai/Mixtral-8x7B-Instruct-v0.1 | 72.7 | 70.14 | 87.55 | 71.4 | 64.98 | 81.06 | 61.11 | 46.7 |
| microsoft/Orca-2-13b | 61.98 | 60.92 | 79.85 | 60.3 | 56.42 | 76.56 | 37.83 | 13 |
| microsoft/phi-2 | 61.33 | 61.09 | 75.11 | 58.11 | 44.47 | 74.35 | 54.81 | 2.78 |
### Ties merging
TIES-Merging is a method designed to facilitate the efficient merging of multiple task-specific models into a consolidated multitask model. It addresses two primary challenges encountered in the process of model merging with a focus on maintaining objectivity.
One key challenge tackled by TIES-Merging involves addressing redundancy in model parameters. This is achieved by identifying and eliminating redundant parameters within task-specific models, emphasizing the changes made during fine-tuning and selectively retaining the top-k% most significant changes while discarding the rest.
Another challenge pertains to conflicts arising from disagreements between parameter signs across different models. TIES-Merging resolves these conflicts by creating a unified sign vector representing the most dominant direction of change across all models.
The TIES-Merging process consists of three steps:
- Trim: Reduces redundancy in task-specific models by retaining a fraction of the most significant parameters (density parameter) and resetting the remaining parameters to zero.
- Elect Sign: Resolves sign conflicts across different models by creating a unified sign vector based on the most dominant direction (positive or negative) in terms of cumulative magnitude.
- Disjoint Merge: Averages parameter values aligned with the unified sign vector, excluding zero values.
## Configuration
```yaml
models:
- model: OpenPipe/mistral-ft-optimized-1227
- model: louisbrulenaudet/Pearl-7B-slerp
parameters:
density: 0.6
weight: 0.3
- model: WizardLM/WizardMath-7B-V1.1
parameters:
density: 0.55
weight: 0.2
- model: cognitivecomputations/WestLake-7B-v2-laser
parameters:
density: 0.55
weight: 0.25
- model: CultriX/NeuralTrix-7B-dpo
parameters:
density: 0.6
weight: 0.25
merge_method: ties
base_model: OpenPipe/mistral-ft-optimized-1227
parameters:
normalize: true
int8_mask: true
dtype: float16
```
## Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "louisbrulenaudet/Pearl-7B-0211-ties"
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"])
```
## Citing & Authors
If you use this code in your research, please use the following BibTeX entry.
```BibTeX
@misc{louisbrulenaudet2023,
author = {Louis Brulé Naudet},
title = {Pearl-7B-0211-ties, an xtraordinary 7B model},
year = {2023}
howpublished = {\url{https://huggingface.co/louisbrulenaudet/Pearl-7B-0211-ties}},
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["merge", "mergekit", "louisbrulenaudet/Pearl-7B-slerp", "WizardLM/WizardMath-7B-V1.1", "cognitivecomputations/WestLake-7B-v2-laser", "CultriX/NeuralTrix-7B-dpo", "chemistry", "biology", "math"], "base_model": ["louisbrulenaudet/Pearl-7B-slerp", "WizardLM/WizardMath-7B-V1.1", "cognitivecomputations/WestLake-7B-v2-laser", "CultriX/NeuralTrix-7B-dpo"], "pipeline_tag": "text-generation"} | text-generation | louisbrulenaudet/Pearl-7B-0211-ties | [
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"safetensors",
"mistral",
"text-generation",
"merge",
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"WizardLM/WizardMath-7B-V1.1",
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"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T16:32:07+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #louisbrulenaudet/Pearl-7B-slerp #WizardLM/WizardMath-7B-V1.1 #cognitivecomputations/WestLake-7B-v2-laser #CultriX/NeuralTrix-7B-dpo #chemistry #biology #math #en #base_model-louisbrulenaudet/Pearl-7B-slerp #base_model-WizardLM/WizardMath-7B-V1.1 #base_model-cognitivecomputations/WestLake-7B-v2-laser #base_model-CultriX/NeuralTrix-7B-dpo #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| 
Pearl-7B-0211-ties, an xtraordinary 7B model
============================================
Pearl-7B-0211-ties is a merge of the following models:
* louisbrulenaudet/Pearl-7B-slerp
* WizardLM/WizardMath-7B-V1.1
* cognitivecomputations/WestLake-7B-v2-laser
* CultriX/NeuralTrix-7B-dpo
Evaluation
----------
The evaluation was performed using the HuggingFace Open LLM Leaderboard.
### Ties merging
TIES-Merging is a method designed to facilitate the efficient merging of multiple task-specific models into a consolidated multitask model. It addresses two primary challenges encountered in the process of model merging with a focus on maintaining objectivity.
One key challenge tackled by TIES-Merging involves addressing redundancy in model parameters. This is achieved by identifying and eliminating redundant parameters within task-specific models, emphasizing the changes made during fine-tuning and selectively retaining the top-k% most significant changes while discarding the rest.
Another challenge pertains to conflicts arising from disagreements between parameter signs across different models. TIES-Merging resolves these conflicts by creating a unified sign vector representing the most dominant direction of change across all models.
The TIES-Merging process consists of three steps:
* Trim: Reduces redundancy in task-specific models by retaining a fraction of the most significant parameters (density parameter) and resetting the remaining parameters to zero.
* Elect Sign: Resolves sign conflicts across different models by creating a unified sign vector based on the most dominant direction (positive or negative) in terms of cumulative magnitude.
* Disjoint Merge: Averages parameter values aligned with the unified sign vector, excluding zero values.
Configuration
-------------
Usage
-----
Citing & Authors
----------------
If you use this code in your research, please use the following BibTeX entry.
Feedback
--------
If you have any feedback, please reach out at louisbrulenaudet@URL.
| [
"### Ties merging\n\n\nTIES-Merging is a method designed to facilitate the efficient merging of multiple task-specific models into a consolidated multitask model. It addresses two primary challenges encountered in the process of model merging with a focus on maintaining objectivity.\n\n\nOne key challenge tackled by TIES-Merging involves addressing redundancy in model parameters. This is achieved by identifying and eliminating redundant parameters within task-specific models, emphasizing the changes made during fine-tuning and selectively retaining the top-k% most significant changes while discarding the rest.\n\n\nAnother challenge pertains to conflicts arising from disagreements between parameter signs across different models. TIES-Merging resolves these conflicts by creating a unified sign vector representing the most dominant direction of change across all models.\n\n\nThe TIES-Merging process consists of three steps:\n\n\n* Trim: Reduces redundancy in task-specific models by retaining a fraction of the most significant parameters (density parameter) and resetting the remaining parameters to zero.\n* Elect Sign: Resolves sign conflicts across different models by creating a unified sign vector based on the most dominant direction (positive or negative) in terms of cumulative magnitude.\n* Disjoint Merge: Averages parameter values aligned with the unified sign vector, excluding zero values.\n\n\nConfiguration\n-------------\n\n\nUsage\n-----\n\n\nCiting & Authors\n----------------\n\n\nIf you use this code in your research, please use the following BibTeX entry.\n\n\nFeedback\n--------\n\n\nIf you have any feedback, please reach out at louisbrulenaudet@URL."
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #louisbrulenaudet/Pearl-7B-slerp #WizardLM/WizardMath-7B-V1.1 #cognitivecomputations/WestLake-7B-v2-laser #CultriX/NeuralTrix-7B-dpo #chemistry #biology #math #en #base_model-louisbrulenaudet/Pearl-7B-slerp #base_model-WizardLM/WizardMath-7B-V1.1 #base_model-cognitivecomputations/WestLake-7B-v2-laser #base_model-CultriX/NeuralTrix-7B-dpo #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Ties merging\n\n\nTIES-Merging is a method designed to facilitate the efficient merging of multiple task-specific models into a consolidated multitask model. It addresses two primary challenges encountered in the process of model merging with a focus on maintaining objectivity.\n\n\nOne key challenge tackled by TIES-Merging involves addressing redundancy in model parameters. This is achieved by identifying and eliminating redundant parameters within task-specific models, emphasizing the changes made during fine-tuning and selectively retaining the top-k% most significant changes while discarding the rest.\n\n\nAnother challenge pertains to conflicts arising from disagreements between parameter signs across different models. TIES-Merging resolves these conflicts by creating a unified sign vector representing the most dominant direction of change across all models.\n\n\nThe TIES-Merging process consists of three steps:\n\n\n* Trim: Reduces redundancy in task-specific models by retaining a fraction of the most significant parameters (density parameter) and resetting the remaining parameters to zero.\n* Elect Sign: Resolves sign conflicts across different models by creating a unified sign vector based on the most dominant direction (positive or negative) in terms of cumulative magnitude.\n* Disjoint Merge: Averages parameter values aligned with the unified sign vector, excluding zero values.\n\n\nConfiguration\n-------------\n\n\nUsage\n-----\n\n\nCiting & Authors\n----------------\n\n\nIf you use this code in your research, please use the following BibTeX entry.\n\n\nFeedback\n--------\n\n\nIf you have any feedback, please reach out at louisbrulenaudet@URL."
] | [
219,
370
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #louisbrulenaudet/Pearl-7B-slerp #WizardLM/WizardMath-7B-V1.1 #cognitivecomputations/WestLake-7B-v2-laser #CultriX/NeuralTrix-7B-dpo #chemistry #biology #math #en #base_model-louisbrulenaudet/Pearl-7B-slerp #base_model-WizardLM/WizardMath-7B-V1.1 #base_model-cognitivecomputations/WestLake-7B-v2-laser #base_model-CultriX/NeuralTrix-7B-dpo #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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null | null | transformers |
# Model Card for Model ID
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[More Information Needed] | {"license": "apache-2.0", "library_name": "transformers"} | text-generation | yam-peleg/Experiment7-7B | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T16:34:23+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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BibTeX:
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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",
"#### 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 #mistral #text-generation #arxiv-1910.09700 #license-apache-2.0 #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 #mistral #text-generation #arxiv-1910.09700 #license-apache-2.0 #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 | Model description:
Model: pgajo/mbert-xlwa-en-it
Dataset: TASTEset
Unshuffled ratio: ['0']
Shuffled ratio: ['1']
Best exact match epoch: 9
Best exact match: 76.24
Best epoch: 9
Drop duplicates: ['1']
Max epochs = 10
Optimizer lr = 3e-05
Optimizer eps = 1e-08
Batch size = 32
Dataset path = pgajo/EW-TT-PE_U0_S1_DROP1_mbert
Results
| epoch | train_loss | train_f1 | train_exact | dev_loss | dev_f1 | dev_exact | test_loss | test_f1 | test_exact |
|--------:|-------------:|-----------:|--------------:|-----------:|---------:|------------:|------------:|----------:|-------------:|
| 1 | 1.87 | 47.36 | 33.17 | 1.14 | 63.28 | 52.76 | 0 | 0 | 0 |
| 2 | 0.76 | 75.67 | 66.76 | 0.9 | 74.01 | 66.85 | 0 | 0 | 0 |
| 3 | 0.36 | 87.5 | 82.45 | 0.94 | 75.33 | 70.17 | 0 | 0 | 0 |
| 4 | 0.2 | 91.88 | 89.15 | 0.97 | 76.98 | 71.27 | 0 | 0 | 0 |
| 5 | 0.13 | 95.36 | 93.16 | 1.1 | 78.26 | 73.2 | 0 | 0 | 0 |
| 6 | 0.09 | 96.59 | 94.96 | 1 | 79.65 | 74.86 | 0 | 0 | 0 |
| 7 | 0.05 | 97.87 | 97.24 | 1.22 | 79.98 | 75.14 | 0 | 0 | 0 |
| 8 | 0.05 | 98.31 | 97.51 | 1.27 | 79.89 | 74.86 | 0 | 0 | 0 |
| 9 | 0.04 | 98.36 | 97.79 | 1.09 | 80.27 | 76.24 | 0 | 0 | 0 |
| 10 | 0.04 | 98.53 | 97.86 | 1.34 | 78.86 | 75.14 | 0 | 0 | 0 | | {} | question-answering | pgajo/mbert-xlwa-en-it_EW-TT-PE_U0_S1_DROP1_mbert_E9_DEV76.0 | [
"transformers",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | 2024-02-11T16:34:50+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #question-answering #endpoints_compatible #region-us
| Model description:
```
Model: pgajo/mbert-xlwa-en-it
Dataset: TASTEset
Unshuffled ratio: ['0']
Shuffled ratio: ['1']
Best exact match epoch: 9
Best exact match: 76.24
Best epoch: 9
Drop duplicates: ['1']
Max epochs = 10
Optimizer lr = 3e-05
Optimizer eps = 1e-08
Batch size = 32
Dataset path = pgajo/EW-TT-PE_U0_S1_DROP1_mbert
```
Results
| [] | [
"TAGS\n#transformers #safetensors #bert #question-answering #endpoints_compatible #region-us \n"
] | [
30
] | [
"passage: TAGS\n#transformers #safetensors #bert #question-answering #endpoints_compatible #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. -->
# beto-sentiment-analysis-finetuned-detests24
This model is a fine-tuned version of [finiteautomata/beto-sentiment-analysis](https://huggingface.co/finiteautomata/beto-sentiment-analysis) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0647
- Accuracy: 0.8609
- F1-score: 0.7906
- Precision: 0.8107
- Recall: 0.7755
- Auc: 0.7755
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|:------:|
| 0.4035 | 1.0 | 153 | 0.3459 | 0.8527 | 0.7540 | 0.8257 | 0.7219 | 0.7219 |
| 0.2217 | 2.0 | 306 | 0.4773 | 0.8183 | 0.7700 | 0.7519 | 0.8088 | 0.8088 |
| 0.0787 | 3.0 | 459 | 0.6757 | 0.8576 | 0.7959 | 0.7982 | 0.7936 | 0.7936 |
| 0.016 | 4.0 | 612 | 0.7801 | 0.8478 | 0.7851 | 0.7830 | 0.7873 | 0.7873 |
| 0.0251 | 5.0 | 765 | 0.9783 | 0.8511 | 0.7994 | 0.7862 | 0.8173 | 0.8173 |
| 0.0159 | 6.0 | 918 | 0.9841 | 0.8576 | 0.7926 | 0.8001 | 0.7860 | 0.7860 |
| 0.0002 | 7.0 | 1071 | 0.9943 | 0.8609 | 0.7906 | 0.8107 | 0.7755 | 0.7755 |
| 0.0001 | 8.0 | 1224 | 1.0252 | 0.8625 | 0.7925 | 0.8139 | 0.7765 | 0.7765 |
| 0.0013 | 9.0 | 1377 | 1.0663 | 0.8511 | 0.7808 | 0.7916 | 0.7716 | 0.7716 |
| 0.0001 | 10.0 | 1530 | 1.0647 | 0.8609 | 0.7906 | 0.8107 | 0.7755 | 0.7755 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall"], "base_model": "finiteautomata/beto-sentiment-analysis", "model-index": [{"name": "beto-sentiment-analysis-finetuned-detests24", "results": []}]} | text-classification | Pablo94/beto-sentiment-analysis-finetuned-detests24 | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:finiteautomata/beto-sentiment-analysis",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T16:36:20+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-finiteautomata/beto-sentiment-analysis #autotrain_compatible #endpoints_compatible #region-us
| beto-sentiment-analysis-finetuned-detests24
===========================================
This model is a fine-tuned version of finiteautomata/beto-sentiment-analysis on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0647
* Accuracy: 0.8609
* F1-score: 0.7906
* Precision: 0.8107
* Recall: 0.7755
* Auc: 0.7755
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: 10
### Training results
### Framework versions
* Transformers 4.37.2
* 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: 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: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\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: 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: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-finiteautomata/beto-sentiment-analysis #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: 10### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\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. -->
# spellcorrector_11_02_050_1_per_word_v5
This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0399
- Precision: 0.9989
- Recall: 0.9946
- F1: 0.9968
- Accuracy: 0.9880
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3884 | 1.0 | 967 | 0.1563 | 0.9714 | 0.9635 | 0.9674 | 0.9611 |
| 0.1648 | 2.0 | 1934 | 0.1297 | 0.9784 | 0.9716 | 0.9750 | 0.9669 |
| 0.1431 | 3.0 | 2901 | 0.1157 | 0.9924 | 0.9753 | 0.9838 | 0.9698 |
| 0.1286 | 4.0 | 3868 | 0.1042 | 0.9897 | 0.9807 | 0.9852 | 0.9722 |
| 0.1201 | 5.0 | 4835 | 0.0969 | 0.9903 | 0.9839 | 0.9871 | 0.9737 |
| 0.1134 | 6.0 | 5802 | 0.0882 | 0.9903 | 0.9861 | 0.9882 | 0.9757 |
| 0.106 | 7.0 | 6769 | 0.0808 | 0.9935 | 0.9855 | 0.9895 | 0.9773 |
| 0.1002 | 8.0 | 7736 | 0.0763 | 0.9924 | 0.9861 | 0.9892 | 0.9786 |
| 0.0945 | 9.0 | 8703 | 0.0696 | 0.9957 | 0.9855 | 0.9906 | 0.9799 |
| 0.0903 | 10.0 | 9670 | 0.0641 | 0.9919 | 0.9893 | 0.9906 | 0.9813 |
| 0.0866 | 11.0 | 10637 | 0.0597 | 0.9920 | 0.9925 | 0.9922 | 0.9825 |
| 0.0822 | 12.0 | 11604 | 0.0557 | 0.9962 | 0.9925 | 0.9944 | 0.9835 |
| 0.0787 | 13.0 | 12571 | 0.0523 | 0.9978 | 0.9914 | 0.9946 | 0.9843 |
| 0.0751 | 14.0 | 13538 | 0.0500 | 0.9984 | 0.9946 | 0.9965 | 0.9852 |
| 0.0715 | 15.0 | 14505 | 0.0467 | 0.9968 | 0.9946 | 0.9957 | 0.9861 |
| 0.0698 | 16.0 | 15472 | 0.0438 | 0.9995 | 0.9952 | 0.9973 | 0.9868 |
| 0.0674 | 17.0 | 16439 | 0.0426 | 0.9984 | 0.9952 | 0.9968 | 0.9870 |
| 0.0652 | 18.0 | 17406 | 0.0410 | 0.9989 | 0.9952 | 0.9970 | 0.9875 |
| 0.0639 | 19.0 | 18373 | 0.0403 | 0.9989 | 0.9946 | 0.9968 | 0.9879 |
| 0.0628 | 20.0 | 19340 | 0.0399 | 0.9989 | 0.9946 | 0.9968 | 0.9880 |
### 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"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "google/canine-s", "model-index": [{"name": "spellcorrector_11_02_050_1_per_word_v5", "results": []}]} | token-classification | Buseak/spellcorrector_11_02_050_1_per_word_v5 | [
"transformers",
"tensorboard",
"safetensors",
"canine",
"token-classification",
"generated_from_trainer",
"base_model:google/canine-s",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T16:37:54+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #canine #token-classification #generated_from_trainer #base_model-google/canine-s #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| spellcorrector\_11\_02\_050\_1\_per\_word\_v5
=============================================
This model is a fine-tuned version of google/canine-s on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0399
* Precision: 0.9989
* Recall: 0.9946
* F1: 0.9968
* Accuracy: 0.9880
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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
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"### Training results",
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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. -->
# LoRA text2image fine-tuning - cosmo3769/test
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the text-to-image dataset. You can find some example images in the following.
## 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": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "inference": true, "base_model": "runwayml/stable-diffusion-v1-5"} | text-to-image | cosmo3769/test-model-card-template-t2i-lora | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | 2024-02-11T16:38:27+00:00 | [] | [] | TAGS
#diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us
|
# LoRA text2image fine-tuning - cosmo3769/test
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the text-to-image dataset. You can find some example images in the following.
!img_0
!img_1
!img_2
## 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] | [
"# LoRA text2image fine-tuning - cosmo3769/test\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the text-to-image dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2",
"## 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 #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n",
"# LoRA text2image fine-tuning - cosmo3769/test\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the text-to-image dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2",
"## 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]"
] | [
68,
82,
9,
5,
24,
16
] | [
"passage: TAGS\n#diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n# LoRA text2image fine-tuning - cosmo3769/test\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the text-to-image dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2## 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 Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | kenchenxingyu/flan-large-lora-emotion_human | [
"transformers",
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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
## 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
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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="arekpaterak/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 | arekpaterak/q-FrozenLake-v1-4x4-noSlippery | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-11T16:46:55+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 |
# mlx-community/openchat-3.5-0106
This model was converted to MLX format from [`openchat/openchat-3.5-0106`]().
Refer to the [original model card](https://huggingface.co/openchat/openchat-3.5-0106) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/openchat-3.5-0106")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"license": "apache-2.0", "library_name": "transformers", "tags": ["openchat", "mistral", "C-RLFT", "mlx"], "base_model": "mistralai/Mistral-7B-v0.1", "pipeline_tag": "text-generation"} | text-generation | mlx-community/openchat-3.5-0106 | [
"transformers",
"mistral",
"text-generation",
"openchat",
"C-RLFT",
"mlx",
"conversational",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T16:47:55+00:00 | [] | [] | TAGS
#transformers #mistral #text-generation #openchat #C-RLFT #mlx #conversational #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mlx-community/openchat-3.5-0106
This model was converted to MLX format from ['openchat/openchat-3.5-0106']().
Refer to the original model card for more details on the model.
## Use with mlx
| [
"# mlx-community/openchat-3.5-0106\nThis model was converted to MLX format from ['openchat/openchat-3.5-0106']().\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
"TAGS\n#transformers #mistral #text-generation #openchat #C-RLFT #mlx #conversational #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mlx-community/openchat-3.5-0106\nThis model was converted to MLX format from ['openchat/openchat-3.5-0106']().\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
81,
52,
5
] | [
"passage: TAGS\n#transformers #mistral #text-generation #openchat #C-RLFT #mlx #conversational #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# mlx-community/openchat-3.5-0106\nThis model was converted to MLX format from ['openchat/openchat-3.5-0106']().\nRefer to the original model card for more details on the model.## Use with mlx"
] | [
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null | null | transformers |
# Model Card for InternVL-Chat-Chinese-V1.2
## What is InternVL?
\[[Paper](https://arxiv.org/abs/2312.14238)\] \[[GitHub](https://github.com/OpenGVLab/InternVL)\] \[[Chat Demo](https://internvl.opengvlab.com/)\]
InternVL scales up the ViT to _**6B parameters**_ and aligns it with LLM.
## InternVL-Chat-V1.2 Blog
> Date: 2024/02/12<br>
> Developed by: Zhe Chen, Weiyun Wang, Wenhai Wang, Erfei Cui, Zhangwei Gao, Xizhou Zhu, Lewei Lu, Tong Lu, Yu Qiao, Jifeng Dai
We are excited to introduce InternVL-Chat-V1.2. Inspired by [LLaVA-NeXT-34B](https://llava-vl.github.io/blog/2024-01-30-llava-next/), we have also adopted [Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) as the language model. Below is the pipeline.
<img width="600" alt="image" src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/GIEKCvNc1Y5iMQqLv645p.png">
From the experimental results, **we've observed that a stronger language model (34B) can better leverage the powerful capabilities of our vision foundation model ([InternViT-6B](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2)).**
For better training reproducibility, we follow the minimalist design and data efficiency similar to LLaVA-NeXT. To reduce training costs, we provide a pre-trained MLP projector and only employ around 1 million visual instruction tuning samples for SFT. Our model has a total of 40 billion parameters and can be trained within 1.5 days using 32 A100 GPUs. The code, data, and model will be made publicly available.
### Data Preparation
Inspired by LLaVA-NeXT, we adopted a data-efficient SFT strategy to train InternVL-Chat-V1.2, utilizing approximately 1.2M of visual instruction tuning samples in total, all of which are fully open-source. In a macro sense, we build upon [ShareGPT-4V](https://github.com/InternLM/InternLM-XComposer/blob/main/projects/ShareGPT4V/docs/Data.md#prepare-images) and additionally integrate [LLaVA-ZH](https://huggingface.co/datasets/openbmb/llava_zh), [DVQA](https://github.com/kushalkafle/DVQA_dataset), [ChartQA](https://github.com/vis-nlp/ChartQA), [AI2D](https://allenai.org/data/diagrams), [DocVQA](https://www.docvqa.org/datasets), [GeoQA+](https://github.com/SCNU203/GeoQA-Plus), and [SynthDoG-EN](https://huggingface.co/datasets/naver-clova-ix/synthdog-en). Most of the data remains consistent with LLaVA-NeXT.
For more details about data preparation, please see [here](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat#prepare-training-datasets).
### Performance
\* Proprietary Model
| name | image size | MMMU<br>(val) | MMMU<br>(test) | MathVista<br>(testmini) | MMB<br>(test) | MMB−CN<br>(test) | MMVP | MME | ScienceQA<br>(image) | POPE | TextVQA | SEEDv1<br>(image) | VizWiz<br>(test) | GQA<br>(test) |
| ------------------ | ---------- | ------------- | -------------- | ----------------------- | ------------- | ---------------- | ---- | -------- | -------------------- | ---- | ------- | ----------------- | ---------------- | ------------- |
| GPT-4V\* | unknown | 56.8 | 55.7 | 49.9 | 77.0 | 74.4 | 38.7 | 1409/517 | - | - | 78.0 | 71.6 | - | - |
| Gemini Ultra\* | unknown | 59.4 | - | 53.0 | - | - | - | - | - | - | 82.3 | - | - | - |
| Gemini Pro\* | unknown | 47.9 | - | 45.2 | 73.6 | 74.3 | 40.7 | 1497/437 | - | - | 74.6 | 70.7 | - | - |
| Qwen-VL-Plus\* | unknown | 45.2 | 40.8 | 43.3 | 67.0 | 70.7 | - | 1681/502 | - | - | 78.9 | 65.7 | - | - |
| Qwen-VL-Max\* | unknown | 51.4 | 46.8 | 51.0 | 77.6 | 75.7 | - | - | - | - | 79.5 | - | - | - |
| | | | | | | | | | | | | | | |
| LLaVA-NEXT-34B | 672x672 | 51.1 | 44.7 | 46.5 | 79.3 | 79.0 | - | 1631/397 | 81.8 | 87.7 | 69.5 | 75.9 | 63.8 | 67.1 |
| InternVL-Chat-V1.2 | 448x448 | 51.6 | 46.2 | 47.7 | 82.2 | 81.2 | 56.7 | 1672/509 | 83.3 | 88.0 | 69.7 | 75.6 | 60.0 | 64.0 |
- MMBench results are collected from the [leaderboard](https://mmbench.opencompass.org.cn/leaderboard).
- In most benchmarks, InternVL-Chat-V1.2 achieves better performance than LLaVA-NeXT-34B.
### Training (SFT)
We provide [slurm scripts](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat/shell/hermes2_yi34b/internvl_chat_v1_2_hermes2_yi34b_448_finetune.sh) for multi-node multi-GPU training. You can use either 32 or 64 GPUs to train this model. If you use 64 GPUs, training will take approximately 18 hours.
For more details about training, please see [here](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat#start-training).
The hyperparameters used for finetuning are listed in the following table.
| Hyperparameter | Trainable Param | Global Batch Size | Learning rate | Epochs | Max length | Weight decay |
| ------------------ | ---------------- | ----------------- | ------------- | ------ | ---------- | ------------ |
| InternVL-Chat-V1.2 | 40B (full model) | 512 | 1e-5 | 1 | 2048 | 0.05 |
## Model Details
- **Model Type:** vision large language model, multimodal chatbot
- **Model Stats:**
- Architecture: [InternViT-6B-448px-V1-2](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2) + MLP + [Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B)
- Params: 40B
- Image size: 448 x 448
- Number of visual tokens: 256
- **Training Strategy:**
- Pretraining Stage
- Learnable Component: MLP
- Data: Trained on 8192x4800=39.3M samples, including COYO, LAION, CC12M, CC3M, SBU, Wukong, GRIT, Objects365, OpenImages, and OCR data.
- Note: In this stage, we load the pretrained weights of [InternViT-6B-448px-V1-2](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2). Moreover, in order to reduce the number of visual tokens, we use a pixel shuffle to reduce 1024 tokens to 256 tokens.
- SFT Stage
- Learnable Component: ViT + MLP + LLM
- Data: A simplified, fully open-source dataset, containing approximately 1 million samples.
## Model Usage
We provide a minimum code example to run InternVL-Chat using only the `transformers` library.
You also can use our [online demo](https://internvl.opengvlab.com/) for a quick experience of this model.
Note: If you meet this error `ImportError: This modeling file requires the following packages that were not found in your environment: fastchat`, please run `pip install fschat`.
```python
import torch
from PIL import Image
from transformers import AutoModel, CLIPImageProcessor
from transformers import AutoTokenizer
path = "OpenGVLab/InternVL-Chat-Chinese-V1-2"
model = AutoModel.from_pretrained(
path,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True,
device_map='auto').eval()
tokenizer = AutoTokenizer.from_pretrained(path)
image = Image.open('./examples/image2.jpg').convert('RGB')
image = image.resize((448, 448))
image_processor = CLIPImageProcessor.from_pretrained(path)
pixel_values = image_processor(images=image, return_tensors='pt').pixel_values
pixel_values = pixel_values.to(torch.bfloat16).cuda()
generation_config = dict(
num_beams=1,
max_new_tokens=512,
do_sample=False,
)
question = "请详细描述图片"
response = model.chat(tokenizer, pixel_values, question, generation_config)
```
## Citation
If you find this project useful in your research, please consider citing:
```BibTeX
@article{chen2023internvl,
title={InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks},
author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and Li, Bin and Luo, Ping and Lu, Tong and Qiao, Yu and Dai, Jifeng},
journal={arXiv preprint arXiv:2312.14238},
year={2023}
}
```
## License
This project is released under the MIT license. Parts of this project contain code and models (e.g., LLaMA2) from other sources, which are subject to their respective licenses.
Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.
## Acknowledgement
InternVL is built with reference to the code of the following projects: [OpenAI CLIP](https://github.com/openai/CLIP), [Open CLIP](https://github.com/mlfoundations/open_clip), [CLIP Benchmark](https://github.com/LAION-AI/CLIP_benchmark), [EVA](https://github.com/baaivision/EVA/tree/master), [InternImage](https://github.com/OpenGVLab/InternImage), [ViT-Adapter](https://github.com/czczup/ViT-Adapter), [MMSegmentation](https://github.com/open-mmlab/mmsegmentation), [Transformers](https://github.com/huggingface/transformers), [DINOv2](https://github.com/facebookresearch/dinov2), [BLIP-2](https://github.com/salesforce/LAVIS/tree/main/projects/blip2), [Qwen-VL](https://github.com/QwenLM/Qwen-VL/tree/master/eval_mm), and [LLaVA-1.5](https://github.com/haotian-liu/LLaVA). Thanks for their awesome work!
| {"license": "mit", "datasets": ["laion/laion2B-en", "laion/laion-coco", "laion/laion2B-multi", "kakaobrain/coyo-700m", "conceptual_captions", "wanng/wukong100m"]} | feature-extraction | OpenGVLab/InternVL-Chat-Chinese-V1-2 | [
"transformers",
"tensorboard",
"safetensors",
"internvl_chat",
"feature-extraction",
"custom_code",
"dataset:laion/laion2B-en",
"dataset:laion/laion-coco",
"dataset:laion/laion2B-multi",
"dataset:kakaobrain/coyo-700m",
"dataset:conceptual_captions",
"dataset:wanng/wukong100m",
"arxiv:2312.14238",
"license:mit",
"region:us"
] | 2024-02-11T16:48:02+00:00 | [
"2312.14238"
] | [] | TAGS
#transformers #tensorboard #safetensors #internvl_chat #feature-extraction #custom_code #dataset-laion/laion2B-en #dataset-laion/laion-coco #dataset-laion/laion2B-multi #dataset-kakaobrain/coyo-700m #dataset-conceptual_captions #dataset-wanng/wukong100m #arxiv-2312.14238 #license-mit #region-us
| Model Card for InternVL-Chat-Chinese-V1.2
=========================================
What is InternVL?
-----------------
[Paper] [GitHub] [Chat Demo]
InternVL scales up the ViT to *6B parameters* and aligns it with LLM.
InternVL-Chat-V1.2 Blog
-----------------------
>
> Date: 2024/02/12
>
> Developed by: Zhe Chen, Weiyun Wang, Wenhai Wang, Erfei Cui, Zhangwei Gao, Xizhou Zhu, Lewei Lu, Tong Lu, Yu Qiao, Jifeng Dai
>
>
>
We are excited to introduce InternVL-Chat-V1.2. Inspired by LLaVA-NeXT-34B, we have also adopted Nous-Hermes-2-Yi-34B as the language model. Below is the pipeline.
<img width="600" alt="image" src="URL
From the experimental results, we've observed that a stronger language model (34B) can better leverage the powerful capabilities of our vision foundation model (InternViT-6B).
For better training reproducibility, we follow the minimalist design and data efficiency similar to LLaVA-NeXT. To reduce training costs, we provide a pre-trained MLP projector and only employ around 1 million visual instruction tuning samples for SFT. Our model has a total of 40 billion parameters and can be trained within 1.5 days using 32 A100 GPUs. The code, data, and model will be made publicly available.
### Data Preparation
Inspired by LLaVA-NeXT, we adopted a data-efficient SFT strategy to train InternVL-Chat-V1.2, utilizing approximately 1.2M of visual instruction tuning samples in total, all of which are fully open-source. In a macro sense, we build upon ShareGPT-4V and additionally integrate LLaVA-ZH, DVQA, ChartQA, AI2D, DocVQA, GeoQA+, and SynthDoG-EN. Most of the data remains consistent with LLaVA-NeXT.
For more details about data preparation, please see here.
### Performance
\* Proprietary Model
* MMBench results are collected from the leaderboard.
* In most benchmarks, InternVL-Chat-V1.2 achieves better performance than LLaVA-NeXT-34B.
### Training (SFT)
We provide slurm scripts for multi-node multi-GPU training. You can use either 32 or 64 GPUs to train this model. If you use 64 GPUs, training will take approximately 18 hours.
For more details about training, please see here.
The hyperparameters used for finetuning are listed in the following table.
Model Details
-------------
* Model Type: vision large language model, multimodal chatbot
* Model Stats:
+ Architecture: InternViT-6B-448px-V1-2 + MLP + Nous-Hermes-2-Yi-34B
+ Params: 40B
+ Image size: 448 x 448
+ Number of visual tokens: 256
* Training Strategy:
+ Pretraining Stage
- Learnable Component: MLP
- Data: Trained on 8192x4800=39.3M samples, including COYO, LAION, CC12M, CC3M, SBU, Wukong, GRIT, Objects365, OpenImages, and OCR data.
- Note: In this stage, we load the pretrained weights of InternViT-6B-448px-V1-2. Moreover, in order to reduce the number of visual tokens, we use a pixel shuffle to reduce 1024 tokens to 256 tokens.
+ SFT Stage
- Learnable Component: ViT + MLP + LLM
- Data: A simplified, fully open-source dataset, containing approximately 1 million samples.
Model Usage
-----------
We provide a minimum code example to run InternVL-Chat using only the 'transformers' library.
You also can use our online demo for a quick experience of this model.
Note: If you meet this error 'ImportError: This modeling file requires the following packages that were not found in your environment: fastchat', please run 'pip install fschat'.
If you find this project useful in your research, please consider citing:
License
-------
This project is released under the MIT license. Parts of this project contain code and models (e.g., LLaMA2) from other sources, which are subject to their respective licenses.
Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.
Acknowledgement
---------------
InternVL is built with reference to the code of the following projects: OpenAI CLIP, Open CLIP, CLIP Benchmark, EVA, InternImage, ViT-Adapter, MMSegmentation, Transformers, DINOv2, BLIP-2, Qwen-VL, and LLaVA-1.5. Thanks for their awesome work!
| [
"### Data Preparation\n\n\nInspired by LLaVA-NeXT, we adopted a data-efficient SFT strategy to train InternVL-Chat-V1.2, utilizing approximately 1.2M of visual instruction tuning samples in total, all of which are fully open-source. In a macro sense, we build upon ShareGPT-4V and additionally integrate LLaVA-ZH, DVQA, ChartQA, AI2D, DocVQA, GeoQA+, and SynthDoG-EN. Most of the data remains consistent with LLaVA-NeXT.\n\n\nFor more details about data preparation, please see here.",
"### Performance\n\n\n\\* Proprietary Model\n\n\n\n* MMBench results are collected from the leaderboard.\n* In most benchmarks, InternVL-Chat-V1.2 achieves better performance than LLaVA-NeXT-34B.",
"### Training (SFT)\n\n\nWe provide slurm scripts for multi-node multi-GPU training. You can use either 32 or 64 GPUs to train this model. If you use 64 GPUs, training will take approximately 18 hours.\n\n\nFor more details about training, please see here.\n\n\nThe hyperparameters used for finetuning are listed in the following table.\n\n\n\nModel Details\n-------------\n\n\n* Model Type: vision large language model, multimodal chatbot\n* Model Stats:\n\n\n\t+ Architecture: InternViT-6B-448px-V1-2 + MLP + Nous-Hermes-2-Yi-34B\n\t+ Params: 40B\n\t+ Image size: 448 x 448\n\t+ Number of visual tokens: 256\n* Training Strategy:\n\n\n\t+ Pretraining Stage\n\t\t- Learnable Component: MLP\n\t\t- Data: Trained on 8192x4800=39.3M samples, including COYO, LAION, CC12M, CC3M, SBU, Wukong, GRIT, Objects365, OpenImages, and OCR data.\n\t\t- Note: In this stage, we load the pretrained weights of InternViT-6B-448px-V1-2. Moreover, in order to reduce the number of visual tokens, we use a pixel shuffle to reduce 1024 tokens to 256 tokens.\n\t+ SFT Stage\n\t\t- Learnable Component: ViT + MLP + LLM\n\t\t- Data: A simplified, fully open-source dataset, containing approximately 1 million samples.\n\n\nModel Usage\n-----------\n\n\nWe provide a minimum code example to run InternVL-Chat using only the 'transformers' library.\n\n\nYou also can use our online demo for a quick experience of this model.\n\n\nNote: If you meet this error 'ImportError: This modeling file requires the following packages that were not found in your environment: fastchat', please run 'pip install fschat'.\n\n\nIf you find this project useful in your research, please consider citing:\n\n\nLicense\n-------\n\n\nThis project is released under the MIT license. Parts of this project contain code and models (e.g., LLaMA2) from other sources, which are subject to their respective licenses.\n\n\nLlama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.\n\n\nAcknowledgement\n---------------\n\n\nInternVL is built with reference to the code of the following projects: OpenAI CLIP, Open CLIP, CLIP Benchmark, EVA, InternImage, ViT-Adapter, MMSegmentation, Transformers, DINOv2, BLIP-2, Qwen-VL, and LLaVA-1.5. Thanks for their awesome work!"
] | [
"TAGS\n#transformers #tensorboard #safetensors #internvl_chat #feature-extraction #custom_code #dataset-laion/laion2B-en #dataset-laion/laion-coco #dataset-laion/laion2B-multi #dataset-kakaobrain/coyo-700m #dataset-conceptual_captions #dataset-wanng/wukong100m #arxiv-2312.14238 #license-mit #region-us \n",
"### Data Preparation\n\n\nInspired by LLaVA-NeXT, we adopted a data-efficient SFT strategy to train InternVL-Chat-V1.2, utilizing approximately 1.2M of visual instruction tuning samples in total, all of which are fully open-source. In a macro sense, we build upon ShareGPT-4V and additionally integrate LLaVA-ZH, DVQA, ChartQA, AI2D, DocVQA, GeoQA+, and SynthDoG-EN. Most of the data remains consistent with LLaVA-NeXT.\n\n\nFor more details about data preparation, please see here.",
"### Performance\n\n\n\\* Proprietary Model\n\n\n\n* MMBench results are collected from the leaderboard.\n* In most benchmarks, InternVL-Chat-V1.2 achieves better performance than LLaVA-NeXT-34B.",
"### Training (SFT)\n\n\nWe provide slurm scripts for multi-node multi-GPU training. You can use either 32 or 64 GPUs to train this model. If you use 64 GPUs, training will take approximately 18 hours.\n\n\nFor more details about training, please see here.\n\n\nThe hyperparameters used for finetuning are listed in the following table.\n\n\n\nModel Details\n-------------\n\n\n* Model Type: vision large language model, multimodal chatbot\n* Model Stats:\n\n\n\t+ Architecture: InternViT-6B-448px-V1-2 + MLP + Nous-Hermes-2-Yi-34B\n\t+ Params: 40B\n\t+ Image size: 448 x 448\n\t+ Number of visual tokens: 256\n* Training Strategy:\n\n\n\t+ Pretraining Stage\n\t\t- Learnable Component: MLP\n\t\t- Data: Trained on 8192x4800=39.3M samples, including COYO, LAION, CC12M, CC3M, SBU, Wukong, GRIT, Objects365, OpenImages, and OCR data.\n\t\t- Note: In this stage, we load the pretrained weights of InternViT-6B-448px-V1-2. Moreover, in order to reduce the number of visual tokens, we use a pixel shuffle to reduce 1024 tokens to 256 tokens.\n\t+ SFT Stage\n\t\t- Learnable Component: ViT + MLP + LLM\n\t\t- Data: A simplified, fully open-source dataset, containing approximately 1 million samples.\n\n\nModel Usage\n-----------\n\n\nWe provide a minimum code example to run InternVL-Chat using only the 'transformers' library.\n\n\nYou also can use our online demo for a quick experience of this model.\n\n\nNote: If you meet this error 'ImportError: This modeling file requires the following packages that were not found in your environment: fastchat', please run 'pip install fschat'.\n\n\nIf you find this project useful in your research, please consider citing:\n\n\nLicense\n-------\n\n\nThis project is released under the MIT license. Parts of this project contain code and models (e.g., LLaMA2) from other sources, which are subject to their respective licenses.\n\n\nLlama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.\n\n\nAcknowledgement\n---------------\n\n\nInternVL is built with reference to the code of the following projects: OpenAI CLIP, Open CLIP, CLIP Benchmark, EVA, InternImage, ViT-Adapter, MMSegmentation, Transformers, DINOv2, BLIP-2, Qwen-VL, and LLaVA-1.5. Thanks for their awesome work!"
] | [
121,
138,
51,
592
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #internvl_chat #feature-extraction #custom_code #dataset-laion/laion2B-en #dataset-laion/laion-coco #dataset-laion/laion2B-multi #dataset-kakaobrain/coyo-700m #dataset-conceptual_captions #dataset-wanng/wukong100m #arxiv-2312.14238 #license-mit #region-us \n### Data Preparation\n\n\nInspired by LLaVA-NeXT, we adopted a data-efficient SFT strategy to train InternVL-Chat-V1.2, utilizing approximately 1.2M of visual instruction tuning samples in total, all of which are fully open-source. In a macro sense, we build upon ShareGPT-4V and additionally integrate LLaVA-ZH, DVQA, ChartQA, AI2D, DocVQA, GeoQA+, and SynthDoG-EN. Most of the data remains consistent with LLaVA-NeXT.\n\n\nFor more details about data preparation, please see here.### Performance\n\n\n\\* Proprietary Model\n\n\n\n* MMBench results are collected from the leaderboard.\n* In most benchmarks, InternVL-Chat-V1.2 achieves better performance than LLaVA-NeXT-34B."
] | [
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null | null | transformers | Model description:
Model: microsoft/mdeberta-v3-base
Dataset: TASTEset
Unshuffled ratio: ['1']
Shuffled ratio: ['0']
Best exact match epoch: 2
Best exact match: 99.72
Best epoch: 2
Drop duplicates: ['1']
Max epochs = 10
Optimizer lr = 3e-05
Optimizer eps = 1e-08
Batch size = 8
Dataset path = pgajo/EW-TT-PE_U1_S0_DROP1_mdeberta
Results
| epoch | train_loss | train_f1 | train_exact | dev_loss | dev_f1 | dev_exact | test_loss | test_f1 | test_exact |
|--------:|-------------:|-----------:|--------------:|-----------:|---------:|------------:|------------:|----------:|-------------:|
| 1 | 1.1 | 75.67 | 72.08 | 0.04 | 99.63 | 98.9 | 0 | 0 | 0 |
| 2 | 0.05 | 99.41 | 98.96 | 0.02 | 99.95 | 99.72 | 0 | 0 | 0 |
| 3 | 0.03 | 99.51 | 99.03 | 0.01 | 99.95 | 99.72 | 0 | 0 | 0 |
| 4 | 0.01 | 99.82 | 99.72 | 0.01 | 99.95 | 99.72 | 0 | 0 | 0 |
| 5 | 0.01 | 99.83 | 99.79 | 0.01 | 99.72 | 99.17 | 0 | 0 | 0 | | {} | question-answering | pgajo/mdeberta-v3-base_EW-TT-PE_U1_S0_DROP1_mdeberta_E2_DEV100.0 | [
"transformers",
"safetensors",
"deberta-v2",
"question-answering",
"endpoints_compatible",
"region:us"
] | 2024-02-11T16:49:28+00:00 | [] | [] | TAGS
#transformers #safetensors #deberta-v2 #question-answering #endpoints_compatible #region-us
| Model description:
```
Model: microsoft/mdeberta-v3-base
Dataset: TASTEset
Unshuffled ratio: ['1']
Shuffled ratio: ['0']
Best exact match epoch: 2
Best exact match: 99.72
Best epoch: 2
Drop duplicates: ['1']
Max epochs = 10
Optimizer lr = 3e-05
Optimizer eps = 1e-08
Batch size = 8
Dataset path = pgajo/EW-TT-PE_U1_S0_DROP1_mdeberta
```
Results
| [] | [
"TAGS\n#transformers #safetensors #deberta-v2 #question-answering #endpoints_compatible #region-us \n"
] | [
35
] | [
"passage: TAGS\n#transformers #safetensors #deberta-v2 #question-answering #endpoints_compatible #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. -->
# donut-base-cord-test1-CMS
This model is a fine-tuned version of [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2) on the imagefolder 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: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "naver-clova-ix/donut-base-finetuned-cord-v2", "model-index": [{"name": "donut-base-cord-test1-CMS", "results": []}]} | null | ShekDass/donut-base-cord-test1-CMS | [
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:naver-clova-ix/donut-base-finetuned-cord-v2",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-11T16:51:24+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-naver-clova-ix/donut-base-finetuned-cord-v2 #license-mit #endpoints_compatible #region-us
|
# donut-base-cord-test1-CMS
This model is a fine-tuned version of naver-clova-ix/donut-base-finetuned-cord-v2 on the imagefolder 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: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# donut-base-cord-test1-CMS\n\nThis model is a fine-tuned version of naver-clova-ix/donut-base-finetuned-cord-v2 on the imagefolder 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: 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- num_epochs: 20\n- mixed_precision_training: Native AMP",
"### 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 #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-naver-clova-ix/donut-base-finetuned-cord-v2 #license-mit #endpoints_compatible #region-us \n",
"# donut-base-cord-test1-CMS\n\nThis model is a fine-tuned version of naver-clova-ix/donut-base-finetuned-cord-v2 on the imagefolder 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: 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- num_epochs: 20\n- mixed_precision_training: Native AMP",
"### 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"
] | [
79,
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"passage: TAGS\n#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-naver-clova-ix/donut-base-finetuned-cord-v2 #license-mit #endpoints_compatible #region-us \n# donut-base-cord-test1-CMS\n\nThis model is a fine-tuned version of naver-clova-ix/donut-base-finetuned-cord-v2 on the imagefolder 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: 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- num_epochs: 20\n- mixed_precision_training: Native AMP### 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 |
MambaSan-370m 🐍
MambaSan-370m is the first chat Japanese language model based on a state-space model architecture (Mamba).
The model is based on Albert Gu's and Tri Dao's work Mamba: Linear-Time Sequence Modeling with Selective State Spaces (paper) as well as their model implementation. .
The Code used for pretraining will soon be published on my github: https://github.com/lcabannes
Citation
bibtex
@misc{lcabannes2024MambaSan-370m,
title = {MambaSan-370m},
author = {Loïc Cabannes},
year = {2024},
howpublished = {HuggingFace},
url = {https://huggingface.co/loiccabannes/MambaSan-370m/}
}
| {"language": ["ja"], "license": "apache-2.0", "datasets": ["SkelterLabsInc/JaQuAD"]} | null | loiccabannes/MambaSan-370m | [
"transformers",
"pytorch",
"ja",
"dataset:SkelterLabsInc/JaQuAD",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-11T16:51:57+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #ja #dataset-SkelterLabsInc/JaQuAD #license-apache-2.0 #endpoints_compatible #region-us
|
MambaSan-370m
MambaSan-370m is the first chat Japanese language model based on a state-space model architecture (Mamba).
The model is based on Albert Gu's and Tri Dao's work Mamba: Linear-Time Sequence Modeling with Selective State Spaces (paper) as well as their model implementation. .
The Code used for pretraining will soon be published on my github: URL
Citation
bibtex
@misc{lcabannes2024MambaSan-370m,
title = {MambaSan-370m},
author = {Loïc Cabannes},
year = {2024},
howpublished = {HuggingFace},
url = {URL
}
| [] | [
"TAGS\n#transformers #pytorch #ja #dataset-SkelterLabsInc/JaQuAD #license-apache-2.0 #endpoints_compatible #region-us \n"
] | [
46
] | [
"passage: TAGS\n#transformers #pytorch #ja #dataset-SkelterLabsInc/JaQuAD #license-apache-2.0 #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": []} | text2text-generation | dura-garage/finetuning-b2b | [
"transformers",
"safetensors",
"encoder-decoder",
"text2text-generation",
"arxiv:1910.09700",
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"1910.09700"
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#transformers #safetensors #encoder-decoder #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# 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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- 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
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APA:
## Glossary [optional]
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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-base-imdb-text-classification
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3282
- F1: 59.3935
- Gen Len: 2.6646
## 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: 0.0003
- 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: 2
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "google/flan-t5-base", "model-index": [{"name": "flan-t5-base-imdb-text-classification", "results": []}]} | text2text-generation | momina296/flan-t5-base-imdb-text-classification | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T16:54:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/flan-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# flan-t5-base-imdb-text-classification
This model is a fine-tuned version of google/flan-t5-base on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3282
- F1: 59.3935
- Gen Len: 2.6646
## 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: 0.0003
- 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: 2
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
| [
"# flan-t5-base-imdb-text-classification\n\nThis model is a fine-tuned version of google/flan-t5-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3282\n- F1: 59.3935\n- Gen Len: 2.6646",
"## 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: 0.0003\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: 2",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.0\n- Pytorch 2.1.2\n- Datasets 2.1.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/flan-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# flan-t5-base-imdb-text-classification\n\nThis model is a fine-tuned version of google/flan-t5-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3282\n- F1: 59.3935\n- Gen Len: 2.6646",
"## 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: 0.0003\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: 2",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.0\n- Pytorch 2.1.2\n- Datasets 2.1.0\n- Tokenizers 0.15.1"
] | [
84,
70,
6,
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8,
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89,
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] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/flan-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# flan-t5-base-imdb-text-classification\n\nThis model is a fine-tuned version of google/flan-t5-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3282\n- F1: 59.3935\n- Gen Len: 2.6646## 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: 0.0003\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: 2### Training results### Framework versions\n\n- Transformers 4.37.0\n- Pytorch 2.1.2\n- Datasets 2.1.0\n- Tokenizers 0.15.1"
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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="arekpaterak/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": "7.44 +/- 2.73", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | arekpaterak/q-Taxi-v3 | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-11T16:57:08+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 | 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. -->
# racism-finetuned-detests24
This model is a fine-tuned version of [davidmasip/racism](https://huggingface.co/davidmasip/racism) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1060
- Accuracy: 0.8429
- F1-score: 0.7668
- Precision: 0.7800
- Recall: 0.7562
- Auc: 0.7562
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|:------:|
| 0.3373 | 1.0 | 153 | 0.3420 | 0.8478 | 0.7336 | 0.8355 | 0.6985 | 0.6985 |
| 0.2465 | 2.0 | 306 | 0.7181 | 0.8478 | 0.7747 | 0.7873 | 0.7644 | 0.7644 |
| 0.0004 | 3.0 | 459 | 0.9072 | 0.8380 | 0.7689 | 0.7696 | 0.7682 | 0.7682 |
| 0.0002 | 4.0 | 612 | 0.9867 | 0.8429 | 0.7668 | 0.7800 | 0.7562 | 0.7562 |
| 0.0001 | 5.0 | 765 | 1.0289 | 0.8429 | 0.7655 | 0.7807 | 0.7537 | 0.7537 |
| 0.0001 | 6.0 | 918 | 1.0580 | 0.8445 | 0.7686 | 0.7829 | 0.7572 | 0.7572 |
| 0.0001 | 7.0 | 1071 | 1.0791 | 0.8429 | 0.7668 | 0.7800 | 0.7562 | 0.7562 |
| 0.0001 | 8.0 | 1224 | 1.0939 | 0.8429 | 0.7668 | 0.7800 | 0.7562 | 0.7562 |
| 0.0001 | 9.0 | 1377 | 1.1029 | 0.8429 | 0.7668 | 0.7800 | 0.7562 | 0.7562 |
| 0.0001 | 10.0 | 1530 | 1.1060 | 0.8429 | 0.7668 | 0.7800 | 0.7562 | 0.7562 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "cc", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall"], "base_model": "davidmasip/racism", "model-index": [{"name": "racism-finetuned-detests24", "results": []}]} | text-classification | Pablo94/racism-finetuned-detests24 | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:davidmasip/racism",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T16:59:26+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-davidmasip/racism #license-cc #autotrain_compatible #endpoints_compatible #region-us
| racism-finetuned-detests24
==========================
This model is a fine-tuned version of davidmasip/racism on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1060
* Accuracy: 0.8429
* F1-score: 0.7668
* Precision: 0.7800
* Recall: 0.7562
* Auc: 0.7562
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: 10
### Training results
### Framework versions
* Transformers 4.37.2
* 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: 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: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-davidmasip/racism #license-cc #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: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
66,
98,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-davidmasip/racism #license-cc #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: 10### Training results### Framework versions\n\n\n* Transformers 4.37.2\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": []} | text-generation | Basha738/llama2-13B-supervised-ft-5epochs-411 | [
"transformers",
"safetensors",
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"text-generation",
"arxiv:1910.09700",
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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.
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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
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### Training Procedure
#### Preprocessing [optional]
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- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
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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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## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
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BibTeX:
APA:
## Glossary [optional]
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## Model Card Contact
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null | null | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# brownniek/mt5-small-finetuned-extractiveOrg
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 7.0270
- Validation Loss: 2.5137
- Epoch: 4
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 345, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 11.7087 | 3.1796 | 0 |
| 7.8734 | 2.6951 | 1 |
| 7.3007 | 2.5137 | 2 |
| 7.0408 | 2.5137 | 3 |
| 7.0270 | 2.5137 | 4 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "google/mt5-small", "model-index": [{"name": "brownniek/mt5-small-finetuned-extractiveOrg", "results": []}]} | text2text-generation | brownniek/mt5-small-finetuned-extractiveOrg | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"base_model:google/mt5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T17:03:53+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #base_model-google/mt5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| brownniek/mt5-small-finetuned-extractiveOrg
===========================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 7.0270
* Validation Loss: 2.5137
* Epoch: 4
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:
* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 5.6e-05, 'decay\_steps': 345, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: mixed\_float16
### Training results
### Framework versions
* Transformers 4.35.2
* TensorFlow 2.15.0
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 345, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: mixed\\_float16",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #base_model-google/mt5-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* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 345, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: mixed\\_float16",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
79,
232,
4,
31
] | [
"passage: TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #base_model-google/mt5-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* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 345, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: mixed\\_float16### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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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. -->
# mistral_grammar
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the generator 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: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "mistral_grammar", "results": []}]} | null | sylviali/mistral_grammar | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | 2024-02-11T17:04:08+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us
|
# mistral_grammar
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the generator 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: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | [
"# mistral_grammar\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the generator 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: 3\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 6\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- PEFT 0.7.2.dev0\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n",
"# mistral_grammar\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the generator 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: 3\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 6\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- PEFT 0.7.2.dev0\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n# mistral_grammar\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the generator 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: 3\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 6\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3### Training results### Framework versions\n\n- PEFT 0.7.2.dev0\n- Transformers 4.36.2\n- Pytorch 2.1.2+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. -->
# vilt_finetuned_200
This model is a fine-tuned version of [dandelin/vilt-b32-mlm](https://huggingface.co/dandelin/vilt-b32-mlm) 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: 5e-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
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "dandelin/vilt-b32-mlm", "model-index": [{"name": "vilt_finetuned_200", "results": []}]} | visual-question-answering | tanish1403/vilt_finetuned_200 | [
"transformers",
"tensorboard",
"safetensors",
"vilt",
"visual-question-answering",
"generated_from_trainer",
"base_model:dandelin/vilt-b32-mlm",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-11T17:06:36+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vilt #visual-question-answering #generated_from_trainer #base_model-dandelin/vilt-b32-mlm #license-apache-2.0 #endpoints_compatible #region-us
|
# vilt_finetuned_200
This model is a fine-tuned version of dandelin/vilt-b32-mlm 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: 5e-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
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
| [
"# vilt_finetuned_200\n\nThis model is a fine-tuned version of dandelin/vilt-b32-mlm 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: 5e-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- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.0.0\n- Datasets 2.1.0\n- Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #vilt #visual-question-answering #generated_from_trainer #base_model-dandelin/vilt-b32-mlm #license-apache-2.0 #endpoints_compatible #region-us \n",
"# vilt_finetuned_200\n\nThis model is a fine-tuned version of dandelin/vilt-b32-mlm 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: 5e-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- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.0.0\n- Datasets 2.1.0\n- Tokenizers 0.14.1"
] | [
70,
38,
6,
12,
8,
3,
90,
4,
30
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #vilt #visual-question-answering #generated_from_trainer #base_model-dandelin/vilt-b32-mlm #license-apache-2.0 #endpoints_compatible #region-us \n# vilt_finetuned_200\n\nThis model is a fine-tuned version of dandelin/vilt-b32-mlm 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: 5e-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- num_epochs: 1### Training results### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.0.0\n- Datasets 2.1.0\n- Tokenizers 0.14.1"
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null | null | peft |
# Model Card for Model ID
This is an adapter prepared to return True or False depending on whether the student's answer ("student_answer") is correct based on the question ("question") and comparing it with a given answer ("best_answer").
The prompt has the following structure:
```
<s>[INST]Analyze the question, the expected answer, and the student's response.
Determine if the student's answer is correct or not. It only returns True if the student's answer is correct with respect to the expected answer or False otherwise.
Add a brief comment explaining why the answer is correct or incorrect.\n\n
Question: {question}\n
Expected Answer: {best_answer}\n
Student Answer: {student_answer}[/INST]</s>"
```
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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 Dataset 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 Dataset 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]
### Framework versions
- PEFT 0.8.2 | {"language": ["en", "es"], "license": "apache-2.0", "library_name": "peft", "datasets": ["nmarafo/truthful_qa_TrueFalse-Feedback"], "base_model": "mistralai/Mixtral-8x7B-Instruct-v0.1"} | null | nmarafo/Mixtral-8x7B-Instruct-v0.1-TrueFalse-Feedback | [
"peft",
"safetensors",
"en",
"es",
"dataset:nmarafo/truthful_qa_TrueFalse-Feedback",
"arxiv:1910.09700",
"base_model:mistralai/Mixtral-8x7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | 2024-02-11T17:06:38+00:00 | [
"1910.09700"
] | [
"en",
"es"
] | TAGS
#peft #safetensors #en #es #dataset-nmarafo/truthful_qa_TrueFalse-Feedback #arxiv-1910.09700 #base_model-mistralai/Mixtral-8x7B-Instruct-v0.1 #license-apache-2.0 #region-us
|
# Model Card for Model ID
This is an adapter prepared to return True or False depending on whether the student's answer ("student_answer") is correct based on the question ("question") and comparing it with a given answer ("best_answer").
The prompt has the following structure:
## 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 | [
"# Model Card for Model ID\n\nThis is an adapter prepared to return True or False depending on whether the student's answer (\"student_answer\") is correct based on the question (\"question\") and comparing it with a given answer (\"best_answer\").\nThe prompt has the following structure:",
"## 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 0.8.2"
] | [
"TAGS\n#peft #safetensors #en #es #dataset-nmarafo/truthful_qa_TrueFalse-Feedback #arxiv-1910.09700 #base_model-mistralai/Mixtral-8x7B-Instruct-v0.1 #license-apache-2.0 #region-us \n",
"# Model Card for Model ID\n\nThis is an adapter prepared to return True or False depending on whether the student's answer (\"student_answer\") is correct based on the question (\"question\") and comparing it with a given answer (\"best_answer\").\nThe prompt has the following structure:",
"## 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 0.8.2"
] | [
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"passage: TAGS\n#peft #safetensors #en #es #dataset-nmarafo/truthful_qa_TrueFalse-Feedback #arxiv-1910.09700 #base_model-mistralai/Mixtral-8x7B-Instruct-v0.1 #license-apache-2.0 #region-us \n# Model Card for Model ID\n\nThis is an adapter prepared to return True or False depending on whether the student's answer (\"student_answer\") is correct based on the question (\"question\") and comparing it with a given answer (\"best_answer\").\nThe prompt has the following structure:## 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"
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null | null | transformers |
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.01787894032895565
f1: 1.0
precision: 1.0
recall: 1.0
auc: 1.0
accuracy: 1.0
| {"tags": ["autotrain", "image-classification"], "datasets": ["mulder-scully-2024/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 | dvs/mulder-scully-2024 | [
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain",
"dataset:mulder-scully-2024/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2024-02-11T17:08:13+00:00 | [] | [] | TAGS
#transformers #safetensors #vit #image-classification #autotrain #dataset-mulder-scully-2024/autotrain-data #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.01787894032895565
f1: 1.0
precision: 1.0
recall: 1.0
auc: 1.0
accuracy: 1.0
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Image Classification",
"## Validation Metricsg\nloss: 0.01787894032895565\n\nf1: 1.0\n\nprecision: 1.0\n\nrecall: 1.0\n\nauc: 1.0\n\naccuracy: 1.0"
] | [
"TAGS\n#transformers #safetensors #vit #image-classification #autotrain #dataset-mulder-scully-2024/autotrain-data #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Image Classification",
"## Validation Metricsg\nloss: 0.01787894032895565\n\nf1: 1.0\n\nprecision: 1.0\n\nrecall: 1.0\n\nauc: 1.0\n\naccuracy: 1.0"
] | [
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"passage: TAGS\n#transformers #safetensors #vit #image-classification #autotrain #dataset-mulder-scully-2024/autotrain-data #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Model Trained Using AutoTrain\n\n- Problem type: Image Classification## Validation Metricsg\nloss: 0.01787894032895565\n\nf1: 1.0\n\nprecision: 1.0\n\nrecall: 1.0\n\nauc: 1.0\n\naccuracy: 1.0"
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null | null | transformers | Model description:
Model: microsoft/mdeberta-v3-base
Dataset: TASTEset
Unshuffled ratio: ['0']
Shuffled ratio: ['1']
Best exact match epoch: 8
Best exact match: 93.09
Best epoch: 8
Drop duplicates: ['1']
Max epochs = 10
Optimizer lr = 3e-05
Optimizer eps = 1e-08
Batch size = 8
Dataset path = pgajo/mdeberta_EW-TT-PE_U0_S1_DROP1
Results
| epoch | train_loss | train_f1 | train_exact | dev_loss | dev_f1 | dev_exact | test_loss | test_f1 | test_exact |
|--------:|-------------:|-----------:|--------------:|-----------:|---------:|------------:|------------:|----------:|-------------:|
| 1 | 1.95 | 48.6 | 40.98 | 0.46 | 85.03 | 81.22 | 0 | 0 | 0 |
| 2 | 0.36 | 88.68 | 85.63 | 0.39 | 90.41 | 89.23 | 0 | 0 | 0 |
| 3 | 0.25 | 91.68 | 89.5 | 0.36 | 90.08 | 88.12 | 0 | 0 | 0 |
| 4 | 0.13 | 95.32 | 94.4 | 0.29 | 91.88 | 90.61 | 0 | 0 | 0 |
| 5 | 0.09 | 97 | 96.27 | 0.3 | 93.72 | 92.54 | 0 | 0 | 0 |
| 6 | 0.09 | 97.04 | 96.34 | 0.37 | 91.44 | 89.78 | 0 | 0 | 0 |
| 7 | 0.07 | 97.2 | 96.61 | 0.29 | 92.68 | 91.99 | 0 | 0 | 0 |
| 8 | 0.05 | 98.34 | 98.06 | 0.33 | 93.5 | 93.09 | 0 | 0 | 0 |
| 9 | 0.03 | 98.67 | 98.55 | 0.35 | 93.67 | 91.99 | 0 | 0 | 0 |
| 10 | 0.02 | 99.33 | 98.96 | 0.4 | 93.54 | 92.54 | 0 | 0 | 0 | | {} | question-answering | pgajo/mdeberta-v3-base_mdeberta_EW-TT-PE_U0_S1_DROP1_E8_DEV93.0 | [
"transformers",
"safetensors",
"deberta-v2",
"question-answering",
"endpoints_compatible",
"region:us"
] | 2024-02-11T17:09:19+00:00 | [] | [] | TAGS
#transformers #safetensors #deberta-v2 #question-answering #endpoints_compatible #region-us
| Model description:
```
Model: microsoft/mdeberta-v3-base
Dataset: TASTEset
Unshuffled ratio: ['0']
Shuffled ratio: ['1']
Best exact match epoch: 8
Best exact match: 93.09
Best epoch: 8
Drop duplicates: ['1']
Max epochs = 10
Optimizer lr = 3e-05
Optimizer eps = 1e-08
Batch size = 8
Dataset path = pgajo/mdeberta_EW-TT-PE_U0_S1_DROP1
```
Results
| [] | [
"TAGS\n#transformers #safetensors #deberta-v2 #question-answering #endpoints_compatible #region-us \n"
] | [
35
] | [
"passage: TAGS\n#transformers #safetensors #deberta-v2 #question-answering #endpoints_compatible #region-us \n"
] | [
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null | null | diffusers | ### My-Pet-Dog Dreambooth model trained by BharatMata following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: Roll-no.27
Sample pictures of this concept:
| {"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]} | text-to-image | BharatMata/my-pet-dog | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-11T17:11:06+00:00 | [] | [] | TAGS
#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
| ### My-Pet-Dog Dreambooth model trained by BharatMata following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: Roll-no.27
Sample pictures of this concept:
| [
"### My-Pet-Dog Dreambooth model trained by BharatMata following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: Roll-no.27\n\nSample pictures of this concept:"
] | [
"TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"### My-Pet-Dog Dreambooth model trained by BharatMata following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: Roll-no.27\n\nSample pictures of this concept:"
] | [
73,
53
] | [
"passage: TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n### My-Pet-Dog Dreambooth model trained by BharatMata following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: Roll-no.27\n\nSample pictures of this concept:"
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] |
null | null | transformers |
# Model Card for Mistral-7B-v0.1-finetuned-guanaco-NF4-QLORA
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This model is a quantized version of the meta-llama/Llama-2-7b-hf model. The model was quantized using NF4. The model was fine-tuned on the dataset timdettmers/openassistant-guanaco using the QLoRA technique
- **Developed by:** Ted Whooley
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** mistral
- **Language(s) (NLP):** en
- **License:** other
- **Finetuned from model [optional]:** mistralai/Mistral-7B-v0.1
### Model Sources [optional]
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## Uses
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## Bias, Risks, and Limitations
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[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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[More Information Needed]
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<!-- 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).
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|
# Model Card for Mistral-7B-v0.1-finetuned-guanaco-NF4-QLORA
## Model Details
### Model Description
This model is a quantized version of the meta-llama/Llama-2-7b-hf model. The model was quantized using NF4. The model was fine-tuned on the dataset timdettmers/openassistant-guanaco using the QLoRA technique
- Developed by: Ted Whooley
- Funded by [optional]:
- Shared by [optional]:
- Model type: mistral
- Language(s) (NLP): en
- License: other
- Finetuned from model [optional]: mistralai/Mistral-7B-v0.1
### 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
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | saracandu/wizardLM-7b-harrypotter | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T17:13:07+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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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 #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 | null | ### My-Pet-Dog-xzg Dreambooth model trained by vig155 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: U21CN319
Sample pictures of this concept:
| {"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]} | text-to-image | vig155/my-pet-dog-xzg | [
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
] | 2024-02-11T17:14:29+00:00 | [] | [] | TAGS
#NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #region-us
| ### My-Pet-Dog-xzg Dreambooth model trained by vig155 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: U21CN319
Sample pictures of this concept:
!0
| [
"### My-Pet-Dog-xzg Dreambooth model trained by vig155 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: U21CN319\n\nSample pictures of this concept:\n\n !0"
] | [
"TAGS\n#NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #region-us \n",
"### My-Pet-Dog-xzg Dreambooth model trained by vig155 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: U21CN319\n\nSample pictures of this concept:\n\n !0"
] | [
43,
58
] | [
"passage: TAGS\n#NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #region-us \n### My-Pet-Dog-xzg Dreambooth model trained by vig155 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: U21CN319\n\nSample pictures of this concept:\n\n !0"
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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="JOBBIME/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 | JOBBIME/q-FrozenLake-v1-4x4-noSlippery | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-11T17:18:15+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
should probably proofread and complete it, then remove this comment. -->
# Whisper Large v2
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Common Voice 16.1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1401
- Wer Ortho: 0.1663
- Wer: 0.0689
## 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: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 1500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.1691 | 0.03 | 500 | 0.1776 | 0.2060 | 0.0941 |
| 0.1538 | 0.05 | 1000 | 0.1459 | 0.1743 | 0.0730 |
| 0.1522 | 0.08 | 1500 | 0.1401 | 0.1663 | 0.0689 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"language": ["sr"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_16_1", "google/fleurs", "Sagicc/audio-lmb-ds", "classla/ParlaSpeech-RS"], "metrics": ["wer"], "base_model": "openai/whisper-large-v3", "model-index": [{"name": "Whisper Large v2", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 16.1", "type": "mozilla-foundation/common_voice_16_1", "config": "sr", "split": "test", "args": "sr"}, "metrics": [{"type": "wer", "value": 0.06891082129009517, "name": "Wer"}]}]}]} | automatic-speech-recognition | Sagicc/whisper-large-sr-v2 | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"sr",
"dataset:mozilla-foundation/common_voice_16_1",
"dataset:google/fleurs",
"dataset:Sagicc/audio-lmb-ds",
"dataset:classla/ParlaSpeech-RS",
"base_model:openai/whisper-large-v3",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-02-11T17:19:56+00:00 | [] | [
"sr"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #sr #dataset-mozilla-foundation/common_voice_16_1 #dataset-google/fleurs #dataset-Sagicc/audio-lmb-ds #dataset-classla/ParlaSpeech-RS #base_model-openai/whisper-large-v3 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Whisper Large v2
================
This model is a fine-tuned version of openai/whisper-large-v3 on the Common Voice 16.1 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1401
* Wer Ortho: 0.1663
* Wer: 0.0689
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: 8
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* training\_steps: 1500
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.0.1+cu117
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### 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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* training\\_steps: 1500\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #sr #dataset-mozilla-foundation/common_voice_16_1 #dataset-google/fleurs #dataset-Sagicc/audio-lmb-ds #dataset-classla/ParlaSpeech-RS #base_model-openai/whisper-large-v3 #license-apache-2.0 #model-index #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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* training\\_steps: 1500\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
134,
158,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #sr #dataset-mozilla-foundation/common_voice_16_1 #dataset-google/fleurs #dataset-Sagicc/audio-lmb-ds #dataset-classla/ParlaSpeech-RS #base_model-openai/whisper-large-v3 #license-apache-2.0 #model-index #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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* training\\_steps: 1500\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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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="JOBBIME/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": "7.54 +/- 2.71", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | JOBBIME/Taxi-v3 | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-11T17:20:32+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 | 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. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the gazeta dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2573
- Rouge1: 9.9348
- Rouge2: 1.4701
- Rougel: 9.7352
- Rougelsum: 9.7173
## 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: 5.6e-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
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 5.0727 | 1.0 | 763 | 2.4238 | 9.9038 | 2.2835 | 9.5715 | 9.6056 |
| 3.4561 | 2.0 | 1526 | 2.3779 | 10.5328 | 2.1668 | 10.297 | 10.2517 |
| 3.2731 | 3.0 | 2289 | 2.3248 | 11.0603 | 2.3552 | 10.9513 | 10.9458 |
| 3.1629 | 4.0 | 3052 | 2.2993 | 9.6206 | 1.553 | 9.4704 | 9.4079 |
| 3.0912 | 5.0 | 3815 | 2.2779 | 9.9379 | 1.5493 | 9.7858 | 9.7129 |
| 3.0449 | 6.0 | 4578 | 2.2698 | 10.1558 | 1.5231 | 9.947 | 9.8629 |
| 3.0184 | 7.0 | 5341 | 2.2683 | 9.7056 | 1.5373 | 9.4965 | 9.3964 |
| 2.9987 | 8.0 | 6104 | 2.2573 | 9.9348 | 1.4701 | 9.7352 | 9.7173 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["gazeta"], "metrics": ["rouge"], "base_model": "google/mt5-small", "model-index": [{"name": "mt5-small-finetuned-amazon-en-es", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "gazeta", "type": "gazeta", "config": "default", "split": "validation", "args": "default"}, "metrics": [{"type": "rouge", "value": 9.9348, "name": "Rouge1"}]}]}]} | summarization | petr-B/mt5-small-finetuned-amazon-en-es | [
"transformers",
"tensorboard",
"safetensors",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:gazeta",
"base_model:google/mt5-small",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T17:20:49+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-gazeta #base_model-google/mt5-small #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-amazon-en-es
================================
This model is a fine-tuned version of google/mt5-small on the gazeta dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2573
* Rouge1: 9.9348
* Rouge2: 1.4701
* Rougel: 9.7352
* Rougelsum: 9.7173
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: 5.6e-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
* num\_epochs: 8
### 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: 5.6e-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* num\\_epochs: 8",
"### 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"
] | [
"TAGS\n#transformers #tensorboard #safetensors #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-gazeta #base_model-google/mt5-small #license-apache-2.0 #model-index #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: 5.6e-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* num\\_epochs: 8",
"### 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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4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-gazeta #base_model-google/mt5-small #license-apache-2.0 #model-index #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: 5.6e-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* num\\_epochs: 8### 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 |
<!-- 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. -->
# xlm-roberta-base-finetuned-detests24
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0941
- Accuracy: 0.8151
- F1-score: 0.7439
- Precision: 0.7380
- Recall: 0.7509
- Auc: 0.7509
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|:------:|
| 0.4432 | 1.0 | 153 | 0.4079 | 0.8298 | 0.7158 | 0.7778 | 0.6893 | 0.6893 |
| 0.4326 | 2.0 | 306 | 0.5061 | 0.7447 | 0.7078 | 0.7052 | 0.7840 | 0.7840 |
| 0.2533 | 3.0 | 459 | 0.5227 | 0.7676 | 0.7195 | 0.7070 | 0.7709 | 0.7709 |
| 0.3354 | 4.0 | 612 | 0.5113 | 0.8347 | 0.7689 | 0.7645 | 0.7737 | 0.7737 |
| 0.2157 | 5.0 | 765 | 0.8228 | 0.8020 | 0.7484 | 0.7321 | 0.7830 | 0.7830 |
| 0.1815 | 6.0 | 918 | 0.9407 | 0.8036 | 0.7528 | 0.7359 | 0.7917 | 0.7917 |
| 0.0829 | 7.0 | 1071 | 0.9539 | 0.8363 | 0.7648 | 0.7676 | 0.7621 | 0.7621 |
| 0.1077 | 8.0 | 1224 | 0.9649 | 0.8200 | 0.7501 | 0.7445 | 0.7566 | 0.7566 |
| 0.0473 | 9.0 | 1377 | 1.0557 | 0.8200 | 0.7439 | 0.7439 | 0.7439 | 0.7439 |
| 0.0632 | 10.0 | 1530 | 1.0941 | 0.8151 | 0.7439 | 0.7380 | 0.7509 | 0.7509 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-detests24", "results": []}]} | text-classification | Pablo94/xlm-roberta-base-finetuned-detests24 | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T17:21:20+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-detests24
====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0941
* Accuracy: 0.8151
* F1-score: 0.7439
* Precision: 0.7380
* Recall: 0.7509
* Auc: 0.7509
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: 10
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\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: 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: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#transformers #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #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: 10### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | peft |
# Model Card for Model ID
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- PEFT 0.8.2 | {"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-chat-hf"} | null | NBA55/llama2-7B-improved-dataset-epoch_15 | [
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | 2024-02-11T17:26:05+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-chat-hf #region-us
|
# Model Card for Model ID
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## Training Details
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- PEFT 0.8.2 | [
"# 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 0.8.2"
] | [
"TAGS\n#peft #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-chat-hf #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 0.8.2"
] | [
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"passage: TAGS\n#peft #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-chat-hf #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 0.8.2"
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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. -->
# finetuned-air-quality
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: 0.2091
- Accuracy: 0.9722
## 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: 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.606 | 1.56 | 100 | 0.5618 | 0.8444 |
| 0.2611 | 3.12 | 200 | 0.2091 | 0.9722 |
### Framework versions
- Transformers 4.37.2
- 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": "finetuned-air-quality", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9722222222222222, "name": "Accuracy"}]}]}]} | image-classification | mo37373/finetuned-air-quality | [
"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"
] | 2024-02-11T17:26:56+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
| finetuned-air-quality
=====================
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: 0.2091
* Accuracy: 0.9722
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: 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: 4
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* 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: 0.0002\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: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
86,
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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: 0.0002\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: 4### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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] |
null | null | transformers |
AWQ Quantized
```
!pip install git+https://github.com/huggingface/transformers.git -q
!pip install huggingface_hub
!pip install autoawq -q
```
```
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
import torch
# Assuming your model and tokenizer are loaded
model_name_or_path = "arlineka/manbasya_2x7b_MOE"
model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layer=True, trust_remote_code=False, safetensors=True)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False)
# Set device to CUDA if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Move model to the device
model.to(device)
# Prepare your input text and move input tensors to the same device
input_text = "Hello. Input Here"
input_ids = tokenizer.encode(input_text, return_tensors="pt").to(device)
# Now generate text with model and input tensors on the same device
output = model.generate(input_ids, max_new_tokens=2048) # Example usage, adjust as necessary
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
``` | {"license": "apache-2.0"} | text-generation | arlineka/manbasya_2x7b_MOE | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2024-02-11T17:28:01+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
AWQ Quantized
| [] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n"
] | [
58
] | [
"passage: TAGS\n#transformers #safetensors #mixtral #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n"
] | [
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null | null | transformers | Model description:
Model: pgajo/mdeberta-xlwa-en-it
Dataset: TASTEset
Unshuffled ratio: ['1']
Shuffled ratio: ['0']
Best exact match epoch: 2
Best exact match: 99.72
Best epoch: 2
Drop duplicates: ['1']
Max epochs = 10
Optimizer lr = 3e-05
Optimizer eps = 1e-08
Batch size = 8
Dataset path = pgajo/EW-TT-PE_U1_S0_DROP1_mdeberta
Results
| epoch | train_loss | train_f1 | train_exact | dev_loss | dev_f1 | dev_exact | test_loss | test_f1 | test_exact |
|--------:|-------------:|-----------:|--------------:|-----------:|---------:|------------:|------------:|----------:|-------------:|
| 1 | 0.21 | 95.73 | 90.81 | 0.01 | 99.81 | 99.45 | 0 | 0 | 0 |
| 2 | 0.03 | 99.62 | 99.1 | 0.01 | 99.95 | 99.72 | 0 | 0 | 0 |
| 3 | 0.01 | 99.87 | 99.65 | 0 | 99.86 | 99.72 | 0 | 0 | 0 |
| 4 | 0.02 | 99.61 | 99.1 | 0.01 | 99.95 | 99.72 | 0 | 0 | 0 |
| 5 | 0.01 | 99.91 | 99.86 | 0.01 | 99.94 | 99.72 | 0 | 0 | 0 | | {} | question-answering | pgajo/mdeberta-xlwa-en-it_EW-TT-PE_U1_S0_DROP1_mdeberta_E2_DEV100.0 | [
"transformers",
"safetensors",
"deberta-v2",
"question-answering",
"endpoints_compatible",
"region:us"
] | 2024-02-11T17:28:09+00:00 | [] | [] | TAGS
#transformers #safetensors #deberta-v2 #question-answering #endpoints_compatible #region-us
| Model description:
```
Model: pgajo/mdeberta-xlwa-en-it
Dataset: TASTEset
Unshuffled ratio: ['1']
Shuffled ratio: ['0']
Best exact match epoch: 2
Best exact match: 99.72
Best epoch: 2
Drop duplicates: ['1']
Max epochs = 10
Optimizer lr = 3e-05
Optimizer eps = 1e-08
Batch size = 8
Dataset path = pgajo/EW-TT-PE_U1_S0_DROP1_mdeberta
```
Results
| [] | [
"TAGS\n#transformers #safetensors #deberta-v2 #question-answering #endpoints_compatible #region-us \n"
] | [
35
] | [
"passage: TAGS\n#transformers #safetensors #deberta-v2 #question-answering #endpoints_compatible #region-us \n"
] | [
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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. -->
# Text-to-image finetuning - cosmo3769/t2i-sdxl
This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **text-to-image** dataset. Below are some example images generated with the finetuned pipeline using the following prompt: good:
Special VAE used for training: None.
## 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": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers"], "inference": true, "base_model": "runwayml/stable-diffusion-v1-5"} | text-to-image | cosmo3769/test-model-template-card-t2i-sdxl | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | 2024-02-11T17:33:10+00:00 | [] | [] | TAGS
#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us
|
# Text-to-image finetuning - cosmo3769/t2i-sdxl
This pipeline was finetuned from runwayml/stable-diffusion-v1-5 on the text-to-image dataset. Below are some example images generated with the finetuned pipeline using the following prompt: good:
!img_0
!img_1
!img_2
Special VAE used for training: None.
## 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] | [
"# Text-to-image finetuning - cosmo3769/t2i-sdxl\n\nThis pipeline was finetuned from runwayml/stable-diffusion-v1-5 on the text-to-image dataset. Below are some example images generated with the finetuned pipeline using the following prompt: good: \n\n!img_0\n!img_1\n!img_2\n\n\nSpecial VAE used for training: None.",
"## 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 #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n",
"# Text-to-image finetuning - cosmo3769/t2i-sdxl\n\nThis pipeline was finetuned from runwayml/stable-diffusion-v1-5 on the text-to-image dataset. Below are some example images generated with the finetuned pipeline using the following prompt: good: \n\n!img_0\n!img_1\n!img_2\n\n\nSpecial VAE used for training: None.",
"## 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]"
] | [
69,
99,
9,
5,
24,
16
] | [
"passage: TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n# Text-to-image finetuning - cosmo3769/t2i-sdxl\n\nThis pipeline was finetuned from runwayml/stable-diffusion-v1-5 on the text-to-image dataset. Below are some example images generated with the finetuned pipeline using the following prompt: good: \n\n!img_0\n!img_1\n!img_2\n\n\nSpecial VAE used for training: None.## 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 | 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. -->
[<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: Qwen/Qwen1.5-4B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
# is_qwen_derived_model: true
trust_remote_code: true
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: OdiaGenAI/all_combined_odia_171k
type: alpaca:chatml
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./lora-out-qwen-4b-odia
hub_model_id: sam2ai/qwen_1.5_odia_4b
sequence_len: 2048 # supports up to 8192
sample_packing: false
pad_to_sequence_len:
adapter: qlora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: Qwen-instruct-4b-odia
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 2
micro_batch_size: 1
num_epochs: 4
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: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention:
warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
```
</details><br>
# qwen_1.5_odia_4b
This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3421
## 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
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.977 | 0.0 | 1 | 1.0190 |
| 0.4901 | 0.25 | 2108 | 0.4872 |
| 0.3966 | 0.5 | 4216 | 0.4347 |
| 0.3127 | 0.75 | 6324 | 0.4104 |
| 0.3172 | 1.0 | 8432 | 0.3932 |
| 0.281 | 1.25 | 10540 | 0.3778 |
| 0.2845 | 1.5 | 12648 | 0.3684 |
| 0.2459 | 1.75 | 14756 | 0.3616 |
| 0.1641 | 2.0 | 16864 | 0.3525 |
| 0.2121 | 2.25 | 18972 | 0.3506 |
| 0.2564 | 2.5 | 21080 | 0.3448 |
| 0.1378 | 2.75 | 23188 | 0.3426 |
| 0.2002 | 3.0 | 25296 | 0.3409 |
| 0.1671 | 3.25 | 27404 | 0.3439 |
| 0.1464 | 3.5 | 29512 | 0.3421 |
| 0.1741 | 3.75 | 31620 | 0.3421 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.0
- Pytorch 2.0.1+gita61a294
- Datasets 2.16.1
- Tokenizers 0.15.0 | {"license": "other", "library_name": "peft", "tags": ["axolotl", "generated_from_trainer"], "base_model": "Qwen/Qwen1.5-4B", "model-index": [{"name": "qwen_1.5_odia_4b", "results": []}]} | null | sam2ai/qwen_1.5_odia_4b | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen1.5-4B",
"license:other",
"4-bit",
"region:us"
] | 2024-02-11T17:43:28+00:00 | [] | [] | TAGS
#peft #safetensors #qwen2 #axolotl #generated_from_trainer #base_model-Qwen/Qwen1.5-4B #license-other #4-bit #region-us
| <img src="URL alt="Built with Axolotl" width="200" height="32"/>
See axolotl config
axolotl version: '0.4.0'
qwen\_1.5\_odia\_4b
===================
This model is a fine-tuned version of Qwen/Qwen1.5-4B on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3421
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
* distributed\_type: multi-GPU
* num\_devices: 8
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* total\_eval\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 10
* num\_epochs: 4
### Training results
### Framework versions
* PEFT 0.8.2
* Transformers 4.37.0
* Pytorch 2.0.1+gita61a294
* 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: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* total\\_eval\\_batch\\_size: 8\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: 4",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.0\n* Pytorch 2.0.1+gita61a294\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
"TAGS\n#peft #safetensors #qwen2 #axolotl #generated_from_trainer #base_model-Qwen/Qwen1.5-4B #license-other #4-bit #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: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* total\\_eval\\_batch\\_size: 8\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: 4",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.0\n* Pytorch 2.0.1+gita61a294\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
51,
178,
4,
42
] | [
"passage: TAGS\n#peft #safetensors #qwen2 #axolotl #generated_from_trainer #base_model-Qwen/Qwen1.5-4B #license-other #4-bit #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: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* total\\_eval\\_batch\\_size: 8\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: 4### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.0\n* Pytorch 2.0.1+gita61a294\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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| {"library_name": "transformers", "tags": []} | feature-extraction | tommymarto/LernnaviBERT_baseline_correct_answers_384 | [
"transformers",
"safetensors",
"bert",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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## Uses
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### Recommendations
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## 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
### Results
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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 | diffusers | # Cointreau Cocktails
<Gallery />
## Download model
[Download](/Guetrazik/cointreauTest/tree/main) them in the Files & versions tab.
| {"license": "mit", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "-", "output": {"url": "images/ghconf(5).jpg"}}], "base_model": "runwayml/stable-diffusion-v1-5"} | text-to-image | Guetrazik/cointreauTest | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:runwayml/stable-diffusion-v1-5",
"license:mit",
"has_space",
"region:us"
] | 2024-02-11T17:45:28+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-runwayml/stable-diffusion-v1-5 #license-mit #has_space #region-us
| # Cointreau Cocktails
<Gallery />
## Download model
Download them in the Files & versions tab.
| [
"# Cointreau Cocktails\n\n<Gallery />",
"## 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-runwayml/stable-diffusion-v1-5 #license-mit #has_space #region-us \n",
"# Cointreau Cocktails\n\n<Gallery />",
"## Download model\n\n\nDownload them in the Files & versions tab."
] | [
63,
11,
14
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-runwayml/stable-diffusion-v1-5 #license-mit #has_space #region-us \n# Cointreau Cocktails\n\n<Gallery />## Download model\n\n\nDownload them in the Files & versions tab."
] | [
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null | null | diffusers | # CKG
<Gallery />
## Download model
[Download](/KiQn22/CKGModel/tree/main) them in the Files & versions tab.
| {"license": "apache-2.0", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "-", "output": {"url": "images/B722A919-C4B3-4E1F-853C-536E2D5AE268.png"}}], "base_model": "cagliostrolab/animagine-xl-3.0"} | text-to-image | KiQn22/CKGModel | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:cagliostrolab/animagine-xl-3.0",
"license:apache-2.0",
"region:us"
] | 2024-02-11T17:46:06+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.0 #license-apache-2.0 #region-us
| # CKG
<Gallery />
## Download model
Download them in the Files & versions tab.
| [
"# CKG\n\n<Gallery />",
"## 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-cagliostrolab/animagine-xl-3.0 #license-apache-2.0 #region-us \n",
"# CKG\n\n<Gallery />",
"## Download model\n\n\nDownload them in the Files & versions tab."
] | [
59,
8,
14
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.0 #license-apache-2.0 #region-us \n# CKG\n\n<Gallery />## Download model\n\n\nDownload them in the Files & versions tab."
] | [
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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. -->
# phi-2-role-play
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) 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: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 200
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1 | {"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "phi-2-role-play", "results": []}]} | null | Siddheshwar1314/phi-2-role-play | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"license:mit",
"region:us"
] | 2024-02-11T17:46:38+00:00 | [] | [] | TAGS
#peft #safetensors #trl #sft #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us
|
# phi-2-role-play
This model is a fine-tuned version of microsoft/phi-2 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: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 200
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1 | [
"# phi-2-role-play\n\nThis model is a fine-tuned version of microsoft/phi-2 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: 2\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: 200",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us \n",
"# phi-2-role-play\n\nThis model is a fine-tuned version of microsoft/phi-2 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: 2\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: 200",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us \n# phi-2-role-play\n\nThis model is a fine-tuned version of microsoft/phi-2 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: 2\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: 200### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
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] |
null | null | transformers | Model description:
Model: pgajo/mdeberta-xlwa-en-it
Dataset: TASTEset
Unshuffled ratio: ['0']
Shuffled ratio: ['1']
Best exact match epoch: 8
Best exact match: 92.27
Best epoch: 8
Drop duplicates: ['1']
Max epochs = 10
Optimizer lr = 3e-05
Optimizer eps = 1e-08
Batch size = 8
Dataset path = pgajo/mdeberta_EW-TT-PE_U0_S1_DROP1
Results
| epoch | train_loss | train_f1 | train_exact | dev_loss | dev_f1 | dev_exact | test_loss | test_f1 | test_exact |
|--------:|-------------:|-----------:|--------------:|-----------:|---------:|------------:|------------:|----------:|-------------:|
| 1 | 0.91 | 75.05 | 65.45 | 0.35 | 89.94 | 87.02 | 0 | 0 | 0 |
| 2 | 0.29 | 91.33 | 87.7 | 0.29 | 91.71 | 89.78 | 0 | 0 | 0 |
| 3 | 0.17 | 93.83 | 92.05 | 0.34 | 90.88 | 90.06 | 0 | 0 | 0 |
| 4 | 0.14 | 95.56 | 94.26 | 0.3 | 93.25 | 90.88 | 0 | 0 | 0 |
| 5 | 0.1 | 96.79 | 95.72 | 0.48 | 92.13 | 90.06 | 0 | 0 | 0 |
| 6 | 0.06 | 97.98 | 97.37 | 0.37 | 93.1 | 91.71 | 0 | 0 | 0 |
| 7 | 0.05 | 98.24 | 98 | 0.43 | 93.25 | 91.16 | 0 | 0 | 0 |
| 8 | 0.05 | 98.16 | 97.79 | 0.48 | 93.57 | 92.27 | 0 | 0 | 0 |
| 9 | 0.04 | 98.67 | 98.2 | 0.5 | 92.91 | 91.44 | 0 | 0 | 0 |
| 10 | 0.05 | 98.44 | 98 | 0.42 | 91.73 | 90.06 | 0 | 0 | 0 | | {} | question-answering | pgajo/mdeberta-xlwa-en-it_mdeberta_EW-TT-PE_U0_S1_DROP1_E8_DEV92.0 | [
"transformers",
"safetensors",
"deberta-v2",
"question-answering",
"endpoints_compatible",
"region:us"
] | 2024-02-11T17:47:51+00:00 | [] | [] | TAGS
#transformers #safetensors #deberta-v2 #question-answering #endpoints_compatible #region-us
| Model description:
```
Model: pgajo/mdeberta-xlwa-en-it
Dataset: TASTEset
Unshuffled ratio: ['0']
Shuffled ratio: ['1']
Best exact match epoch: 8
Best exact match: 92.27
Best epoch: 8
Drop duplicates: ['1']
Max epochs = 10
Optimizer lr = 3e-05
Optimizer eps = 1e-08
Batch size = 8
Dataset path = pgajo/mdeberta_EW-TT-PE_U0_S1_DROP1
```
Results
| [] | [
"TAGS\n#transformers #safetensors #deberta-v2 #question-answering #endpoints_compatible #region-us \n"
] | [
35
] | [
"passage: TAGS\n#transformers #safetensors #deberta-v2 #question-answering #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": []} | feature-extraction | tommymarto/LernnaviBERT_baseline_correct_answers_768 | [
"transformers",
"safetensors",
"bert",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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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
### Results
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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 |
<!-- 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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) 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: 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
- num_epochs: 3
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-finetuned-ner", "results": []}]} | text-classification | Rajdonthi/bert-finetuned-ner | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T17:50:52+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased 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: 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
- num_epochs: 3
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# bert-finetuned-ner\n\nThis model is a fine-tuned version of bert-base-cased 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: 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- num_epochs: 3",
"### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-finetuned-ner\n\nThis model is a fine-tuned version of bert-base-cased 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: 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- num_epochs: 3",
"### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
67,
34,
6,
12,
8,
3,
90,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-finetuned-ner\n\nThis model is a fine-tuned version of bert-base-cased 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: 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- num_epochs: 3### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
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null | null | transformers | # NeuralBeagle-11B
DPO'd from vicgalle/franken-Beagle-11B, a Beagle-like model upscaled to 11B.
It is a frankenmerge model created using mergekit. Then, we applied DPO over a high-quality preference dataset.
 | {"license": "apache-2.0", "tags": ["merge"], "datasets": ["jondurbin/truthy-dpo-v0.1"]} | text-generation | vicgalle/NeuralBeagle-11B-truthy | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"conversational",
"dataset:jondurbin/truthy-dpo-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T17:52:37+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #conversational #dataset-jondurbin/truthy-dpo-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # NeuralBeagle-11B
DPO'd from vicgalle/franken-Beagle-11B, a Beagle-like model upscaled to 11B.
It is a frankenmerge model created using mergekit. Then, we applied DPO over a high-quality preference dataset.
!image/png | [
"# NeuralBeagle-11B\n\nDPO'd from vicgalle/franken-Beagle-11B, a Beagle-like model upscaled to 11B. \nIt is a frankenmerge model created using mergekit. Then, we applied DPO over a high-quality preference dataset.\n\n!image/png"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #conversational #dataset-jondurbin/truthy-dpo-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# NeuralBeagle-11B\n\nDPO'd from vicgalle/franken-Beagle-11B, a Beagle-like model upscaled to 11B. \nIt is a frankenmerge model created using mergekit. Then, we applied DPO over a high-quality preference dataset.\n\n!image/png"
] | [
78,
73
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #conversational #dataset-jondurbin/truthy-dpo-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# NeuralBeagle-11B\n\nDPO'd from vicgalle/franken-Beagle-11B, a Beagle-like model upscaled to 11B. \nIt is a frankenmerge model created using mergekit. Then, we applied DPO over a high-quality preference dataset.\n\n!image/png"
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null | null | transformers | bert-base-cased model fintuned with yelp5 dataset. One epoch on the full training set finetuning the full model
and one more epoch finetune just the classifier layers on the full training set.
| {"license": "apache-2.0", "datasets": ["yelp_review_full"], "metrics": ["accuracy 67.088%"]} | text-classification | csabakecskemeti/bert-base-case-yelp5-tuned-experiment | [
"transformers",
"safetensors",
"bert",
"text-classification",
"dataset:yelp_review_full",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T17:54:06+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #text-classification #dataset-yelp_review_full #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased model fintuned with yelp5 dataset. One epoch on the full training set finetuning the full model
and one more epoch finetune just the classifier layers on the full training set.
| [] | [
"TAGS\n#transformers #safetensors #bert #text-classification #dataset-yelp_review_full #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
55
] | [
"passage: TAGS\n#transformers #safetensors #bert #text-classification #dataset-yelp_review_full #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
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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 | LoneStriker/Tess-72B-v1.5b-GPTQ | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T17:55:40+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 |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | samanjoy2/Mixtral-8x7B-Instruct-v0.1_ML-ESG-3_eng_fr_v2.0 | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
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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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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
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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 | 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 | RansikaC99/llama2-qlora-finetunined-4-bit-1500-ingredient-and-title-5epoch | [
"peft",
"region:us"
] | 2024-02-11T18:06:06+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 | null | # Susan.zob
this is a Susan.zob | {"license": "unknown"} | null | PraPra94/Susan.zob | [
"license:unknown",
"region:us"
] | 2024-02-11T18:08:12+00:00 | [] | [] | TAGS
#license-unknown #region-us
| # URL
this is a URL | [
"# URL\n\nthis is a URL"
] | [
"TAGS\n#license-unknown #region-us \n",
"# URL\n\nthis is a URL"
] | [
13,
6
] | [
"passage: TAGS\n#license-unknown #region-us \n# URL\n\nthis is a URL"
] | [
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null | null | setfit |
# SetFit with ppsingh/TAPP-multilabel-mpnet
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [ppsingh/TAPP-multilabel-mpnet](https://huggingface.co/ppsingh/TAPP-multilabel-mpnet) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [ppsingh/TAPP-multilabel-mpnet](https://huggingface.co/ppsingh/TAPP-multilabel-mpnet)
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:---------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| NEGATIVE | <ul><li>'(p 70-1).Antigua and Barbuda’s 2021 update to the first Nationally Determined Contribution the most vulnerable in society have been predominantly focused on adaptation measures like building resilience to flooding and hurricanes. The updated NDC ambition provides an opportunity to focus more intently on enabling access to energy efficiency and renewable energy for the most vulnerable, particularly women who are most affected when electricity is not available since the grid is down after an extreme weather event. Nationally, Antigua and Barbuda intends to utilize the SIRF Fund as a mechanism primarily to catalyse and leverage investment in the transition for NGOs, MSMEs and informal sectors that normally cannot access traditional local commercial financing due to perceived high risks.'</li><li>'The transport system cost will be increased by 16.2% compared to the BAU level. Electric trucks and electric pick-ups will account for the highest share of investment followed by electric buses and trucks. In the manufacturing industries, the energy efficiency improvement in the heating and the motor systems and the deployment of CCS require the highest investment in the non-metallic and the chemical industries in 2050. The manufacturing industries system cost will be increased by 15.3% compared to the BAU level.'</li><li>'Figure 1-9: Total GHG emissions by sector (excluding LULUCF) 2000 and 2016 1.2.2 Greenhouse Gas Emission by Sector • Energy Total direct GHG emissions from the Energy sector in 2016 were estimated to be 253,895.61 eq. The majority of GHG emissions in the Energy sector were generated by fuel combustion, consisting mostly of grid-connected electricity and heat production at around eq (42.84%). GHG emissions from Transport, Manufacturing Industries and Construction, and other sectors were 68,260.17 GgCO2 eq eq (6.10%), respectively. Fugitive Emissions from fuel eq or a little over 4.33% of total GHG emissions from the Energy sector. Details of GHG emissions in the Energy sector by gas type and source in 2016 are presented in Figure 1-10. Source: Thailand Third Biennial Update Report, UNFCCC 2020.'</li></ul> |
| TARGET | <ul><li>'DNPM, NFA,. Cocoa. Board,. Spice Board,. Provincial. gov-ernments. in the. Momase. region. Ongoing -. 2025. 340. European Union. Support committed. Priority Sector: Health. By 2030, 100% of the population benefit from introduced health measures to respond to malaria and other climate-sensitive diseases in PNG. Action or Activity. Indicator. Status. Lead. Implementing. Agencies. Supporting. Agencies. Time Frame. Budget (USD). Funding Source. (Existing/Potential). Other Support. Improve vector control. measures, with a priority. of all households having. access to a long-lasting. insecticidal net (LLIN).'</li><li>'Conditionality: With national effort it is intended to increase the attention to vulnerable groups in case of disasters and/or emergencies up to 50% of the target and 100% of the target with international cooperation. Description: In this goal, it is projected to increase coverage from 33% to 50% (211,000 families) of agricultural insurance in attention to the number of families, whose crops were affected by various adverse weather events (flood, drought, frost, hailstorm, among others), in addition to the implementation of comprehensive actions for risk management and adaptation to Climate Change.'</li><li>'By 2030, upgrade watershed health and vitality in at least 20 districts to a higher condition category. By 2030, create an inventory of wetlands in Nepal and sustainably manage vulnerable wetlands. By 2025, enhance the sink capacity of the landuse sector by instituting the Forest Development Fund (FDF) for compensation of plantations and forest restoration. Increase growing stock including Mean Annual Increment in Tarai, Hills and Mountains. Afforest/reforest viable public and private lands, including agroforestry.'</li></ul> |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("ppsingh/iki_target_setfit")
# Run inference
preds = model("In the oil sector, the country has benefited from 372 million dollars for the reduction of gas flaring at the initiative (GGFR - \"Global Gas Flaring Reduction\") of the World Bank after having adopted in November 2015 a national reduction plan flaring and associated gas upgrading. In the electricity sector, the NDC highlights the development of hydroelectricity which should make it possible to cover 80% of production in 2025, the remaining 20% ​​being covered by gas and other renewable energies.")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:---------|:----|
| Word count | 58 | 116.6632 | 508 |
| Label | Training Sample Count |
|:---------|:----------------------|
| NEGATIVE | 51 |
| TARGET | 44 |
### Training Hyperparameters
- batch_size: (8, 2)
- num_epochs: (1, 0)
- max_steps: -1
- sampling_strategy: undersampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0018 | 1 | 0.3343 | - |
| 0.1783 | 100 | 0.0026 | 0.1965 |
| 0.3565 | 200 | 0.0001 | 0.1995 |
| 0.5348 | 300 | 0.0001 | 0.2105 |
| 0.7130 | 400 | 0.0001 | 0.2153 |
| 0.8913 | 500 | 0.0 | 0.1927 |
### Training Results Classifier
- Classes Representation in Test Data: Target: 9, Negative: 8
- F1-score: 87.8%
- Accuracy: 88.2%
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.006 kg of CO2
- **Hours Used**: 0.185 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x Tesla T4
- **CPU Model**: Intel(R) Xeon(R) CPU @ 2.00GHz
- **RAM Size**: 12.67 GB
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.3.0
- Tokenizers: 0.15.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> | {"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "metrics": ["accuracy"], "widget": [{"text": "During 2021-2030, Thailand s LEDS will be implemented through the NDC roadmap and sectoral action plans which provide detailed guidance on measures and realistic actions to achieve the 1st NDC target by 2030, as well as regular monitoring and evaluation of the progress and achievement. The monitoring and evaluation of the mitigation measures relating to the Thailand\u2019s LEDS will be carried out to ensure its effectiveness and efficiency in achieving its objectives and key performance indicators. Because it is a long-term plan spanning many years during which many changes can occur, it is envisaged that it will be subject to a comprehensive review every five years. This is consistent with the approach under the Paris Agreement that assigned Parties to submit their NDCs to the UNFCCC every five year."}, {"text": "The NDC also benefited from the reviews and comments of these implementing partners as well as local and international experts. Special thanks to The Honourable Molwyn Joseph, Minister for Health, Wellness and the Environment, for his unwavering commitment to advance this ambitious climate change agenda, while Antigua and Barbuda faced an outbreak of the COVID-19 pandemic. Significant contributions to the process were made by a wide-cross section of stakeholders from the public and private sector, civil society, trade and industry groups and training institutions, who attended NDC-related workshops, consultations and participated in key stakeholder interviews organized to inform the NDC update."}, {"text": "Antigua and Barbuda will mainstream gender in its energy planning through an Inclusive Renewable Energy Strategy. This strategy will recognize and acknowledge, among other things, the gender norms, and inequalities prevalent in the energy sector, women and men\u2019s differentiated access to energy, their different energy needs and preferences, and different impacts that energy access could have on their livelihoods. Antigua and Barbuda\u2019s plan for an inclusive renewable energy transition will ensure continued affordable and reliable access to electricity and other energy services for all."}, {"text": "Thailand\u2019s climate actions are divided into short-term, medium-term and long-term targets up to 2050. For the mitigation actions, short-term targets include: (i) develop medium- and long-term GHG emission reduction targets and prepare roadmaps for the implementation by sector, including the GHG emission reduction target on a voluntary basis (pre-2020 target), Nationally Appropriate Mitigation Actions (NAMAs) roadmaps, and measurement, reporting, and verification mechanisms, (ii) establish domestic incentive mechanisms to encourage low carbon development. The medium-term targets include: (i) reduce GHG emissions from energy and transport sectors by 7-20% against BAU level by 2020, subject to the level of international support, (ii) supply at least 25% of energy consumption from renewable energy sources by 2021 and (iii) increase the ratio of municipalities with more than 10 m2 of green space per capita."}, {"text": "In the oil sector, the country has benefited from 372 million dollars for the reduction of gas flaring at the initiative (GGFR - \"Global Gas Flaring Reduction\") of the World Bank after having adopted in November 2015 a national reduction plan flaring and associated gas upgrading. In the electricity sector, the NDC highlights the development of hydroelectricity which should make it possible to cover 80% of production in 2025, the remaining 20% ​​being covered by gas and other renewable energies."}], "pipeline_tag": "text-classification", "inference": true, "co2_eq_emissions": {"emissions": 5.901369050433577, "source": "codecarbon", "training_type": "fine-tuning", "on_cloud": false, "cpu_model": "Intel(R) Xeon(R) CPU @ 2.00GHz", "ram_total_size": 12.674789428710938, "hours_used": 0.185, "hardware_used": "1 x Tesla T4"}, "base_model": "ppsingh/TAPP-multilabel-mpnet"} | text-classification | ppsingh/iki_target_setfit | [
"setfit",
"safetensors",
"mpnet",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:ppsingh/TAPP-multilabel-mpnet",
"co2_eq_emissions",
"region:us"
] | 2024-02-11T18:11:00+00:00 | [
"2209.11055"
] | [] | TAGS
#setfit #safetensors #mpnet #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-ppsingh/TAPP-multilabel-mpnet #co2_eq_emissions #region-us
| SetFit with ppsingh/TAPP-multilabel-mpnet
=========================================
This is a SetFit model that can be used for Text Classification. This SetFit model uses ppsingh/TAPP-multilabel-mpnet as the Sentence Transformer embedding model. A SetFitHead instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
-------------
### Model Description
* Model Type: SetFit
* Sentence Transformer body: ppsingh/TAPP-multilabel-mpnet
* Classification head: a SetFitHead instance
* Maximum Sequence Length: 512 tokens
* Number of Classes: 2 classes
### Model Sources
* Repository: SetFit on GitHub
* Paper: Efficient Few-Shot Learning Without Prompts
* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
### Model Labels
Uses
----
### Direct Use for Inference
First install the SetFit library:
Then you can load this model and run inference.
Training Details
----------------
### Training Set Metrics
### Training Hyperparameters
* batch\_size: (8, 2)
* num\_epochs: (1, 0)
* max\_steps: -1
* sampling\_strategy: undersampling
* body\_learning\_rate: (2e-05, 1e-05)
* head\_learning\_rate: 0.01
* loss: CosineSimilarityLoss
* distance\_metric: cosine\_distance
* margin: 0.25
* end\_to\_end: False
* use\_amp: False
* warmup\_proportion: 0.01
* seed: 42
* eval\_max\_steps: -1
* load\_best\_model\_at\_end: False
### Training Results
### Training Results Classifier
* Classes Representation in Test Data: Target: 9, Negative: 8
* F1-score: 87.8%
* Accuracy: 88.2%
### Environmental Impact
Carbon emissions were measured using CodeCarbon.
* Carbon Emitted: 0.006 kg of CO2
* Hours Used: 0.185 hours
### Training Hardware
* On Cloud: No
* GPU Model: 1 x Tesla T4
* CPU Model: Intel(R) Xeon(R) CPU @ 2.00GHz
* RAM Size: 12.67 GB
### Framework Versions
* Python: 3.10.12
* SetFit: 1.0.3
* Sentence Transformers: 2.3.1
* Transformers: 4.35.2
* PyTorch: 2.1.0+cu121
* Datasets: 2.3.0
* Tokenizers: 0.15.1
### BibTeX
| [
"### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: ppsingh/TAPP-multilabel-mpnet\n* Classification head: a SetFitHead instance\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 2 classes",
"### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts",
"### Model Labels\n\n\n\nUses\n----",
"### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------",
"### Training Set Metrics",
"### Training Hyperparameters\n\n\n* batch\\_size: (8, 2)\n* num\\_epochs: (1, 0)\n* max\\_steps: -1\n* sampling\\_strategy: undersampling\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.01\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False",
"### Training Results",
"### Training Results Classifier\n\n\n* Classes Representation in Test Data: Target: 9, Negative: 8\n* F1-score: 87.8%\n* Accuracy: 88.2%",
"### Environmental Impact\n\n\nCarbon emissions were measured using CodeCarbon.\n\n\n* Carbon Emitted: 0.006 kg of CO2\n* Hours Used: 0.185 hours",
"### Training Hardware\n\n\n* On Cloud: No\n* GPU Model: 1 x Tesla T4\n* CPU Model: Intel(R) Xeon(R) CPU @ 2.00GHz\n* RAM Size: 12.67 GB",
"### Framework Versions\n\n\n* Python: 3.10.12\n* SetFit: 1.0.3\n* Sentence Transformers: 2.3.1\n* Transformers: 4.35.2\n* PyTorch: 2.1.0+cu121\n* Datasets: 2.3.0\n* Tokenizers: 0.15.1",
"### BibTeX"
] | [
"TAGS\n#setfit #safetensors #mpnet #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-ppsingh/TAPP-multilabel-mpnet #co2_eq_emissions #region-us \n",
"### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: ppsingh/TAPP-multilabel-mpnet\n* Classification head: a SetFitHead instance\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 2 classes",
"### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts",
"### Model Labels\n\n\n\nUses\n----",
"### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------",
"### Training Set Metrics",
"### Training Hyperparameters\n\n\n* batch\\_size: (8, 2)\n* num\\_epochs: (1, 0)\n* max\\_steps: -1\n* sampling\\_strategy: undersampling\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.01\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False",
"### Training Results",
"### Training Results Classifier\n\n\n* Classes Representation in Test Data: Target: 9, Negative: 8\n* F1-score: 87.8%\n* Accuracy: 88.2%",
"### Environmental Impact\n\n\nCarbon emissions were measured using CodeCarbon.\n\n\n* Carbon Emitted: 0.006 kg of CO2\n* Hours Used: 0.185 hours",
"### Training Hardware\n\n\n* On Cloud: No\n* GPU Model: 1 x Tesla T4\n* CPU Model: Intel(R) Xeon(R) CPU @ 2.00GHz\n* RAM Size: 12.67 GB",
"### Framework Versions\n\n\n* Python: 3.10.12\n* SetFit: 1.0.3\n* Sentence Transformers: 2.3.1\n* Transformers: 4.35.2\n* PyTorch: 2.1.0+cu121\n* Datasets: 2.3.0\n* Tokenizers: 0.15.1",
"### BibTeX"
] | [
71,
59,
52,
7,
31,
7,
169,
4,
40,
36,
43,
58,
6
] | [
"passage: TAGS\n#setfit #safetensors #mpnet #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-ppsingh/TAPP-multilabel-mpnet #co2_eq_emissions #region-us \n### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: ppsingh/TAPP-multilabel-mpnet\n* Classification head: a SetFitHead instance\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 2 classes### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts### Model Labels\n\n\n\nUses\n----### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------### Training Set Metrics### Training Hyperparameters\n\n\n* batch\\_size: (8, 2)\n* num\\_epochs: (1, 0)\n* max\\_steps: -1\n* sampling\\_strategy: undersampling\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.01\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False### Training Results### Training Results Classifier\n\n\n* Classes Representation in Test Data: Target: 9, Negative: 8\n* F1-score: 87.8%\n* Accuracy: 88.2%### Environmental Impact\n\n\nCarbon emissions were measured using CodeCarbon.\n\n\n* Carbon Emitted: 0.006 kg of CO2\n* Hours Used: 0.185 hours"
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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": "259.77 +/- 22.28", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | qimike/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-11T18:22:45+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 |
This is a simple port of [mistralai/Mistral-7B-v0.1](mistralai/Mistral-7B-v0.1) with modified configuration to fix the broken rope scaling from SWA present in v0.1 and fine-tunes of v0.1.
The fix leverages the same strategy employed by [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0) where the model config is adjusted to imitate a llama model with 4k context. | {"language": ["en"], "license": "apache-2.0", "tags": ["pretrained"], "pipeline_tag": "text-generation", "inference": {"parameters": {"temperature": 0.7}}} | text-generation | ToastyPigeon/Mistral-7B-v0.1-ROPE-Fix | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"pretrained",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T18:22:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #llama #text-generation #pretrained #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is a simple port of mistralai/Mistral-7B-v0.1 with modified configuration to fix the broken rope scaling from SWA present in v0.1 and fine-tunes of v0.1.
The fix leverages the same strategy employed by upstage/SOLAR-10.7B-v1.0 where the model config is adjusted to imitate a llama model with 4k context. | [] | [
"TAGS\n#transformers #pytorch #safetensors #llama #text-generation #pretrained #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
65
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #llama #text-generation #pretrained #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
# Model Card for Model ID
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# Model Card for Model ID
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## Uses
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### Out-of-Scope Use
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### Recommendations
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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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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
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#### 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 | Weighted imatrix quantize of https://huggingface.co/sophosympatheia/Midnight-Rose-103B-v2.0.3
The weight was calculated using an experimental (potentially crappy) method that is iterative (thus the "i1"), using 270k semi-random english tokens.
<!-- provided-files -->
## Provided Quants
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Midnight-Rose-103B-v2.0.3-i1-GGUF/resolve/main/Midnight-Rose-103B-v2.0.3.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 27.6 | |
| [GGUF](https://huggingface.co/mradermacher/Midnight-Rose-103B-v2.0.3-i1-GGUF/resolve/main/Midnight-Rose-103B-v2.0.3.i1-IQ2_XS.gguf) | i1-IQ2_XS | 30.7 | |
| [GGUF](https://huggingface.co/mradermacher/Midnight-Rose-103B-v2.0.3-i1-GGUF/resolve/main/Midnight-Rose-103B-v2.0.3.i1-Q2_K.gguf) | i1-Q2_K | 38.2 | |
| [GGUF](https://huggingface.co/mradermacher/Midnight-Rose-103B-v2.0.3-i1-GGUF/resolve/main/Midnight-Rose-103B-v2.0.3.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 40.5 | fast, lower quality |
| [GGUF](https://huggingface.co/mradermacher/Midnight-Rose-103B-v2.0.3-i1-GGUF/resolve/main/Midnight-Rose-103B-v2.0.3.i1-Q3_K_XS.gguf) | i1-Q3_K_XS | 42.3 | |
| [GGUF](https://huggingface.co/mradermacher/Midnight-Rose-103B-v2.0.3-i1-GGUF/resolve/main/Midnight-Rose-103B-v2.0.3.i1-Q3_K_S.gguf) | i1-Q3_K_S | 44.8 | |
| [PART 1](https://huggingface.co/mradermacher/Midnight-Rose-103B-v2.0.3-i1-GGUF/resolve/main/Midnight-Rose-103B-v2.0.3.i1-Q3_K_M.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/Midnight-Rose-103B-v2.0.3-i1-GGUF/resolve/main/Midnight-Rose-103B-v2.0.3.i1-Q3_K_M.gguf.split-ab) | i1-Q3_K_M | 49.9 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/Midnight-Rose-103B-v2.0.3-i1-GGUF/resolve/main/Midnight-Rose-103B-v2.0.3.i1-Q3_K_L.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/Midnight-Rose-103B-v2.0.3-i1-GGUF/resolve/main/Midnight-Rose-103B-v2.0.3.i1-Q3_K_L.gguf.split-ab) | i1-Q3_K_L | 54.4 | |
| [PART 1](https://huggingface.co/mradermacher/Midnight-Rose-103B-v2.0.3-i1-GGUF/resolve/main/Midnight-Rose-103B-v2.0.3.i1-Q4_K_S.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/Midnight-Rose-103B-v2.0.3-i1-GGUF/resolve/main/Midnight-Rose-103B-v2.0.3.i1-Q4_K_S.gguf.split-ab) | i1-Q4_K_S | 58.9 | fast, medium quality |
| [PART 1](https://huggingface.co/mradermacher/Midnight-Rose-103B-v2.0.3-i1-GGUF/resolve/main/Midnight-Rose-103B-v2.0.3.i1-Q4_K_M.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/Midnight-Rose-103B-v2.0.3-i1-GGUF/resolve/main/Midnight-Rose-103B-v2.0.3.i1-Q4_K_M.gguf.split-ab) | i1-Q4_K_M | 62.2 | fast, medium quality |
<!-- end -->
| {"library_name": "transformers"} | null | mradermacher/Midnight-Rose-103B-v2.0.3-i1-GGUF | [
"transformers",
"gguf",
"endpoints_compatible",
"region:us"
] | 2024-02-11T18:24:08+00:00 | [] | [] | TAGS
#transformers #gguf #endpoints_compatible #region-us
| Weighted imatrix quantize of URL
The weight was calculated using an experimental (potentially crappy) method that is iterative (thus the "i1"), using 270k semi-random english tokens.
Provided Quants
---------------
| [] | [
"TAGS\n#transformers #gguf #endpoints_compatible #region-us \n"
] | [
20
] | [
"passage: TAGS\n#transformers #gguf #endpoints_compatible #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. -->
# distilbert-base-uncased-finetuned-tweets-dataset
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4993
- Accuracy: 0.818
- F1: 0.7856
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.592 | 1.0 | 250 | 0.4993 | 0.818 | 0.7856 |
### 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"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-tweets-dataset", "results": []}]} | text-classification | lambda101/distilbert-base-uncased-finetuned-tweets-dataset | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T18:34:12+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-tweets-dataset
================================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4993
* Accuracy: 0.818
* F1: 0.7856
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: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
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"### 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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"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: 32\n* eval\\_batch\\_size: 32\n* seed: 42\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\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 |
<!-- 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. -->
# donut-base-cord-test2-CMS100
This model is a fine-tuned version of [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2) on the imagefolder 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: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "naver-clova-ix/donut-base-finetuned-cord-v2", "model-index": [{"name": "donut-base-cord-test2-CMS100", "results": []}]} | null | ShekDass/donut-base-cord-test2-CMS100 | [
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:naver-clova-ix/donut-base-finetuned-cord-v2",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-11T18:34:32+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-naver-clova-ix/donut-base-finetuned-cord-v2 #license-mit #endpoints_compatible #region-us
|
# donut-base-cord-test2-CMS100
This model is a fine-tuned version of naver-clova-ix/donut-base-finetuned-cord-v2 on the imagefolder 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: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# donut-base-cord-test2-CMS100\n\nThis model is a fine-tuned version of naver-clova-ix/donut-base-finetuned-cord-v2 on the imagefolder 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: 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- num_epochs: 30\n- mixed_precision_training: Native AMP",
"### 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 #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-naver-clova-ix/donut-base-finetuned-cord-v2 #license-mit #endpoints_compatible #region-us \n",
"# donut-base-cord-test2-CMS100\n\nThis model is a fine-tuned version of naver-clova-ix/donut-base-finetuned-cord-v2 on the imagefolder 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: 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- num_epochs: 30\n- mixed_precision_training: Native AMP",
"### 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"
] | [
79,
52,
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"passage: TAGS\n#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-naver-clova-ix/donut-base-finetuned-cord-v2 #license-mit #endpoints_compatible #region-us \n# donut-base-cord-test2-CMS100\n\nThis model is a fine-tuned version of naver-clova-ix/donut-base-finetuned-cord-v2 on the imagefolder 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: 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- num_epochs: 30\n- mixed_precision_training: Native AMP### 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 |
# Model Card for Model ID
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## Model Details
### Model Description
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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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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 type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
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## Bias, Risks, and Limitations
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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.
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | DevanshSinha/mistral-7b-newsqa_bits | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-11T18:41:16+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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| [
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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]:",
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] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #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]:",
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"### 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 #arxiv-1910.09700 #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 | 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": "286.29 +/- 17.64", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | samb271/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-11T18:44:46+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 | null | 
# Surrogate Modeling Toolbox
[](https://github.com/SMTorg/smt/actions?query=workflow%3ATests)
[](https://coveralls.io/github/SMTorg/smt?branch=master)
[](https://smt.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/ambv/black)
The surrogate modeling toolbox (SMT) is a Python package that contains a collection of surrogate modeling methods, sampling techniques, and benchmarking functions. This package provides a library of surrogate models that is simple to use and facilitates the implementation of additional methods.
SMT is different from existing surrogate modeling libraries because of its emphasis on derivatives, including training derivatives used for gradient-enhanced modeling, prediction derivatives, and derivatives with respect to the training data.
It also includes new surrogate models that are not available elsewhere: kriging by partial-least squares reduction and energy-minimizing spline interpolation.
SMT is documented using custom tools for embedding automatically-tested code and dynamically-generated plots to produce high-quality user guides with minimal effort from contributors.
SMT is distributed under the New BSD license.
To cite SMT 2.0: P. Saves and R. Lafage and N. Bartoli and Y. Diouane and J. H. Bussemaker and T. Lefebvre and J. T. Hwang and J. Morlier and J. R. R. A. Martins. SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes. Advances in Engineering Software, 2024.
```
@article{saves2024smt,
author = {P. Saves and R. Lafage and N. Bartoli and Y. Diouane and J. Bussemaker and T. Lefebvre and J. T. Hwang and J. Morlier and J. R. R. A. Martins},
title = {{SMT 2.0: A} Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes},
journal = {Advances in Engineering Sofware},
year = {2024},
volume = {188},
pages = {103571},
doi = {https://doi.org/10.1016/j.advengsoft.2023.103571}}
```
To cite SMT legacy: M. A. Bouhlel and J. T. Hwang and N. Bartoli and R. Lafage and J. Morlier and J. R. R. A. Martins. A Python surrogate modeling framework with derivatives. Advances in Engineering Software, 2019.
```
@article{SMT2019,
Author = {Mohamed Amine Bouhlel and John T. Hwang and Nathalie Bartoli and Rémi Lafage and Joseph Morlier and Joaquim R. R. A. Martins},
Journal = {Advances in Engineering Software},
Title = {A Python surrogate modeling framework with derivatives},
pages = {102662},
issn = {0965-9978},
doi = {https://doi.org/10.1016/j.advengsoft.2019.03.005},
Year = {2019}}
```
# Required packages
SMT depends on the following modules: numpy, scipy, scikit-learn, pyDOE3 and Cython.
# Installation
If you want to install the latest release
```
pip install smt
```
or else if you want to install from the current master branch
```
pip install git+https://github.com/SMTOrg/smt.git@master
```
# Usage
For examples demonstrating how to use SMT, you can take a look at the [tutorial notebooks](https://github.com/SMTorg/smt/tree/master/tutorial#readme) or go to the 'smt/examples' folder.
# Documentation
[Documentation of Surrogate Modeling Toolbox](http://smt.readthedocs.io/en/stable).
# Contributing
To contribute to SMT refer to the [contributing section](https://smt.readthedocs.io/en/latest/_src_docs/dev_docs.html#contributing-to-smt) of the documentation.
| {} | null | psaves/SMT | [
"region:us"
] | 2024-02-11T18:46:27+00:00 | [] | [] | TAGS
#region-us
| !SMT Logo
# Surrogate Modeling Toolbox
 is a Python package that contains a collection of surrogate modeling methods, sampling techniques, and benchmarking functions. This package provides a library of surrogate models that is simple to use and facilitates the implementation of additional methods.
SMT is different from existing surrogate modeling libraries because of its emphasis on derivatives, including training derivatives used for gradient-enhanced modeling, prediction derivatives, and derivatives with respect to the training data.
It also includes new surrogate models that are not available elsewhere: kriging by partial-least squares reduction and energy-minimizing spline interpolation.
SMT is documented using custom tools for embedding automatically-tested code and dynamically-generated plots to produce high-quality user guides with minimal effort from contributors.
SMT is distributed under the New BSD license.
To cite SMT 2.0: P. Saves and R. Lafage and N. Bartoli and Y. Diouane and J. H. Bussemaker and T. Lefebvre and J. T. Hwang and J. Morlier and J. R. R. A. Martins. SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes. Advances in Engineering Software, 2024.
To cite SMT legacy: M. A. Bouhlel and J. T. Hwang and N. Bartoli and R. Lafage and J. Morlier and J. R. R. A. Martins. A Python surrogate modeling framework with derivatives. Advances in Engineering Software, 2019.
# Required packages
SMT depends on the following modules: numpy, scipy, scikit-learn, pyDOE3 and Cython.
# Installation
If you want to install the latest release
or else if you want to install from the current master branch
# Usage
For examples demonstrating how to use SMT, you can take a look at the tutorial notebooks or go to the 'smt/examples' folder.
# Documentation
Documentation of Surrogate Modeling Toolbox.
# Contributing
To contribute to SMT refer to the contributing section of the documentation.
| [
"# Surrogate Modeling Toolbox\n\n is a Python package that contains a collection of surrogate modeling methods, sampling techniques, and benchmarking functions. This package provides a library of surrogate models that is simple to use and facilitates the implementation of additional methods.\n\nSMT is different from existing surrogate modeling libraries because of its emphasis on derivatives, including training derivatives used for gradient-enhanced modeling, prediction derivatives, and derivatives with respect to the training data.\n\nIt also includes new surrogate models that are not available elsewhere: kriging by partial-least squares reduction and energy-minimizing spline interpolation.\nSMT is documented using custom tools for embedding automatically-tested code and dynamically-generated plots to produce high-quality user guides with minimal effort from contributors.\n\nSMT is distributed under the New BSD license.\n\nTo cite SMT 2.0: P. Saves and R. Lafage and N. Bartoli and Y. Diouane and J. H. Bussemaker and T. Lefebvre and J. T. Hwang and J. Morlier and J. R. R. A. Martins. SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes. Advances in Engineering Software, 2024.\n\n\n\nTo cite SMT legacy: M. A. Bouhlel and J. T. Hwang and N. Bartoli and R. Lafage and J. Morlier and J. R. R. A. Martins. A Python surrogate modeling framework with derivatives. Advances in Engineering Software, 2019.",
"# Required packages\nSMT depends on the following modules: numpy, scipy, scikit-learn, pyDOE3 and Cython.",
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"# Usage\nFor examples demonstrating how to use SMT, you can take a look at the tutorial notebooks or go to the 'smt/examples' folder.",
"# Documentation\nDocumentation of Surrogate Modeling Toolbox.",
"# Contributing\nTo contribute to SMT refer to the contributing section of the documentation."
] | [
"TAGS\n#region-us \n",
"# Surrogate Modeling Toolbox\n\n is a Python package that contains a collection of surrogate modeling methods, sampling techniques, and benchmarking functions. This package provides a library of surrogate models that is simple to use and facilitates the implementation of additional methods.\n\nSMT is different from existing surrogate modeling libraries because of its emphasis on derivatives, including training derivatives used for gradient-enhanced modeling, prediction derivatives, and derivatives with respect to the training data.\n\nIt also includes new surrogate models that are not available elsewhere: kriging by partial-least squares reduction and energy-minimizing spline interpolation.\nSMT is documented using custom tools for embedding automatically-tested code and dynamically-generated plots to produce high-quality user guides with minimal effort from contributors.\n\nSMT is distributed under the New BSD license.\n\nTo cite SMT 2.0: P. Saves and R. Lafage and N. Bartoli and Y. Diouane and J. H. Bussemaker and T. Lefebvre and J. T. Hwang and J. Morlier and J. R. R. A. Martins. SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes. Advances in Engineering Software, 2024.\n\n\n\nTo cite SMT legacy: M. A. Bouhlel and J. T. Hwang and N. Bartoli and R. Lafage and J. Morlier and J. R. R. A. Martins. A Python surrogate modeling framework with derivatives. Advances in Engineering Software, 2019.",
"# Required packages\nSMT depends on the following modules: numpy, scipy, scikit-learn, pyDOE3 and Cython.",
"# Installation\nIf you want to install the latest release\n\n\n\nor else if you want to install from the current master branch",
"# Usage\nFor examples demonstrating how to use SMT, you can take a look at the tutorial notebooks or go to the 'smt/examples' folder.",
"# Documentation\nDocumentation of Surrogate Modeling Toolbox.",
"# Contributing\nTo contribute to SMT refer to the contributing section of the documentation."
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"passage: TAGS\n#region-us \n# Surrogate Modeling Toolbox\n\n is a Python package that contains a collection of surrogate modeling methods, sampling techniques, and benchmarking functions. This package provides a library of surrogate models that is simple to use and facilitates the implementation of additional methods.\n\nSMT is different from existing surrogate modeling libraries because of its emphasis on derivatives, including training derivatives used for gradient-enhanced modeling, prediction derivatives, and derivatives with respect to the training data.\n\nIt also includes new surrogate models that are not available elsewhere: kriging by partial-least squares reduction and energy-minimizing spline interpolation.\nSMT is documented using custom tools for embedding automatically-tested code and dynamically-generated plots to produce high-quality user guides with minimal effort from contributors.\n\nSMT is distributed under the New BSD license.\n\nTo cite SMT 2.0: P. Saves and R. Lafage and N. Bartoli and Y. Diouane and J. H. Bussemaker and T. Lefebvre and J. T. Hwang and J. Morlier and J. R. R. A. Martins. SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes. Advances in Engineering Software, 2024.\n\n\n\nTo cite SMT legacy: M. A. Bouhlel and J. T. Hwang and N. Bartoli and R. Lafage and J. Morlier and J. R. R. A. Martins. A Python surrogate modeling framework with derivatives. Advances in Engineering Software, 2019.# Required packages\nSMT depends on the following modules: numpy, scipy, scikit-learn, pyDOE3 and Cython.# Installation\nIf you want to install the latest release\n\n\n\nor else if you want to install from the current master branch"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | tommymarto/LernnaviBERT_mcqbert1_correct_answers_768 | [
"transformers",
"safetensors",
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"1910.09700"
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|
# Model Card for Model ID
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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 |
# Plastic-classification
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### HDPE
#### PE
#### PET
#### PP
#### PVC
 | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | image-classification | Kisu-2003/Plastic-classification | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T18:49:08+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #pytorch #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Plastic-classification
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### HDPE
!HDPE
#### PE
!PE
#### PET
!PET
#### PP
!PP
#### PVC
!PVC | [
"# Plastic-classification\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### HDPE\n\n!HDPE",
"#### PE\n\n!PE",
"#### PET\n\n!PET",
"#### PP\n\n!PP",
"#### PVC\n\n!PVC"
] | [
"TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #pytorch #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Plastic-classification\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### HDPE\n\n!HDPE",
"#### PE\n\n!PE",
"#### PET\n\n!PET",
"#### PP\n\n!PP",
"#### PVC\n\n!PVC"
] | [
54,
43,
4,
7,
5,
5,
5,
6
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #pytorch #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n# Plastic-classification\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.## Example Images#### HDPE\n\n!HDPE#### PE\n\n!PE#### PET\n\n!PET#### PP\n\n!PP#### PVC\n\n!PVC"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | farassadek/faras-alpaca-bitcoin-tweets-sentiment-2 | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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"region:us"
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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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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
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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 |
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| {"library_name": "transformers", "tags": []} | null | tommymarto/LernnaviBERT_mcqbert1_correct_answers_384 | [
"transformers",
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"1910.09700"
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#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us
|
# 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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## 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
- 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",
"### 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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"## 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]",
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"## Model Card Authors [optional]",
"## Model Card Contact"
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"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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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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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
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"### Model Architecture and Objective",
"### Compute Infrastructure",
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-classification | karimimanesh/text_stance_detection | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
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"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #text-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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### 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
### Results
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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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## Glossary [optional]
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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": "253.80 +/- 24.43", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | AstridsN/LunarLander | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-11T18:58:42+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 |
<!-- 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. -->
# T5-Small-Sinhala-Sumarization-test3
This model is a fine-tuned version of [Malmika/T5-Small-Sinhala-Sumarization](https://huggingface.co/Malmika/T5-Small-Sinhala-Sumarization) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0304
- Rouge1: 0.1355
- Rouge2: 0.0618
- Rougel: 0.1354
- Rougelsum: 0.1356
- Gen Len: 17.8198
## 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
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0959 | 1.0 | 4333 | 0.0560 | 0.1357 | 0.062 | 0.1357 | 0.1358 | 17.8575 |
| 0.0531 | 2.0 | 8666 | 0.0367 | 0.1355 | 0.0619 | 0.1355 | 0.1357 | 17.8214 |
| 0.0406 | 3.0 | 12999 | 0.0350 | 0.1355 | 0.0619 | 0.1355 | 0.1357 | 17.8213 |
| 0.0342 | 4.0 | 17332 | 0.0328 | 0.1355 | 0.0618 | 0.1354 | 0.1356 | 17.8198 |
| 0.0323 | 5.0 | 21665 | 0.0304 | 0.1355 | 0.0618 | 0.1354 | 0.1356 | 17.8198 |
### 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"], "metrics": ["rouge"], "base_model": "Malmika/T5-Small-Sinhala-Sumarization", "model-index": [{"name": "T5-Small-Sinhala-Sumarization-test3", "results": []}]} | text2text-generation | Malmika/Sinhala-Sumarization | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:Malmika/T5-Small-Sinhala-Sumarization",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T18:59:29+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-Malmika/T5-Small-Sinhala-Sumarization #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| T5-Small-Sinhala-Sumarization-test3
===================================
This model is a fine-tuned version of Malmika/T5-Small-Sinhala-Sumarization on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0304
* Rouge1: 0.1355
* Rouge2: 0.0618
* Rougel: 0.1354
* Rougelsum: 0.1356
* Gen Len: 17.8198
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
* 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: 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* 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: 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* 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"
] | [
88,
98,
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"passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-Malmika/T5-Small-Sinhala-Sumarization #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: 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* 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 | null | # ◆LizMix
- SakuMixベースのアニメ向けマージモデル。
----
# ◆Discord
[Join Discord Server](https://discord.gg/eN6aSWRddT)
- Hemlokのマージコミュニティです。レシピとか裏話はこちら。
----
# ◆モデル概要
- Sampler: DPM++ 3M SDE Karras or DPM++ 2M SDE Karras 推奨。
- Steps: 20-
- Clipskip: 2
- CFG Scale: 5-12
- Denoise strength: 0.6
- クオリティタグ(masterpiece,best quality等)は入れるとより絵柄が安定します。
- 別途embeddingsをおすすめします。
----
# ◆サンプル
- Prompt:
```
1girl, solo, teen, cowboy shot, (depth of field:1.2), (night), (long coat), downtown, (street light:1.1), (Fantastic lighting), looking at viewer, black hair, long hair, [smile], (Closed mouth),
best quality, 4K, ultra detailed CG, highres, source anime, newest
```
---
- Prompt:
```
1girl, solo, full body, (fantasy), (dark:1.2), (depth of field:1.2), (night), (Fantastic lighting), looking at viewer, white hair, long hair,
best quality, 4K, ultra detailed CG, highres, source anime, newest
```
---
- Prompt:
```
1girl, solo, cowboy shot, long white hair, glossy, (Gothic Lolita dress), Gorgeous Clothing, clothes that reveal little, [cute smile], in room,
best quality, 4K, ultra detailed CG, highres, source anime, newest
```
---
# ◆モデルの使い方
- モデルをダウンロードしてWebUI等でご使用ください。
- モデルはModelsフォルダの中にあります。
----
# 免責事項
- SFWおよびNSFW画像の作成は、個々のクリエイターの判断によります。モデル製作者は責任を負いません。
- このモデルは、公共の場などでNSFWコンテンツを公開するために作られたモデルではありません。
----
# ライセンス
- このモデルはFair AI Public License 1.0-SDで権利と使用方法が規定されています。
- ライセンスの全文は以下のリンクをお読みください。
[https://freedevproject.org/faipl-1.0-sd/](https://freedevproject.org/faipl-1.0-sd/) | {"language": ["ja"], "license": "other", "tags": ["stable-diffusion", "text-to-image", "art"], "license_name": "faipl-1.0-sd", "license_link": "https://freedevproject.org/faipl-1.0-sd/"} | text-to-image | Hemlok/LizMix | [
"stable-diffusion",
"text-to-image",
"art",
"ja",
"license:other",
"region:us"
] | 2024-02-11T19:04:14+00:00 | [] | [
"ja"
] | TAGS
#stable-diffusion #text-to-image #art #ja #license-other #region-us
| # ◆LizMix
- SakuMixベースのアニメ向けマージモデル。
----
# ◆Discord
Join Discord Server
- Hemlokのマージコミュニティです。レシピとか裏話はこちら。
----
# ◆モデル概要
- Sampler: DPM++ 3M SDE Karras or DPM++ 2M SDE Karras 推奨。
- Steps: 20-
- Clipskip: 2
- CFG Scale: 5-12
- Denoise strength: 0.6
- クオリティタグ(masterpiece,best quality等)は入れるとより絵柄が安定します。
- 別途embeddingsをおすすめします。
----
# ◆サンプル
- Prompt:
---
- Prompt:
---
- Prompt:
---
# ◆モデルの使い方
- モデルをダウンロードしてWebUI等でご使用ください。
- モデルはModelsフォルダの中にあります。
----
# 免責事項
- SFWおよびNSFW画像の作成は、個々のクリエイターの判断によります。モデル製作者は責任を負いません。
- このモデルは、公共の場などでNSFWコンテンツを公開するために作られたモデルではありません。
----
# ライセンス
- このモデルはFair AI Public License 1.0-SDで権利と使用方法が規定されています。
- ライセンスの全文は以下のリンクをお読みください。
URL | [
"# ◆LizMix\n\n- SakuMixベースのアニメ向けマージモデル。\n\n----",
"# ◆Discord\n\nJoin Discord Server\n- Hemlokのマージコミュニティです。レシピとか裏話はこちら。\n\n----",
"# ◆モデル概要\n\n- Sampler: DPM++ 3M SDE Karras or DPM++ 2M SDE Karras 推奨。\n- Steps: 20-\n- Clipskip: 2\n- CFG Scale: 5-12\n- Denoise strength: 0.6\n- クオリティタグ(masterpiece,best quality等)は入れるとより絵柄が安定します。\n- 別途embeddingsをおすすめします。\n\n----",
"# ◆サンプル\n\n\n- Prompt:\n\n\n---\n\n\n- Prompt:\n\n\n---\n\n\n- Prompt:\n\n\n---",
"# ◆モデルの使い方\n\n- モデルをダウンロードしてWebUI等でご使用ください。\n- モデルはModelsフォルダの中にあります。\n\n\n----",
"# 免責事項\n- SFWおよびNSFW画像の作成は、個々のクリエイターの判断によります。モデル製作者は責任を負いません。\n- このモデルは、公共の場などでNSFWコンテンツを公開するために作られたモデルではありません。\n\n----",
"# ライセンス\n\n- このモデルはFair AI Public License 1.0-SDで権利と使用方法が規定されています。\n- ライセンスの全文は以下のリンクをお読みください。\nURL"
] | [
"TAGS\n#stable-diffusion #text-to-image #art #ja #license-other #region-us \n",
"# ◆LizMix\n\n- SakuMixベースのアニメ向けマージモデル。\n\n----",
"# ◆Discord\n\nJoin Discord Server\n- Hemlokのマージコミュニティです。レシピとか裏話はこちら。\n\n----",
"# ◆モデル概要\n\n- Sampler: DPM++ 3M SDE Karras or DPM++ 2M SDE Karras 推奨。\n- Steps: 20-\n- Clipskip: 2\n- CFG Scale: 5-12\n- Denoise strength: 0.6\n- クオリティタグ(masterpiece,best quality等)は入れるとより絵柄が安定します。\n- 別途embeddingsをおすすめします。\n\n----",
"# ◆サンプル\n\n\n- Prompt:\n\n\n---\n\n\n- Prompt:\n\n\n---\n\n\n- Prompt:\n\n\n---",
"# ◆モデルの使い方\n\n- モデルをダウンロードしてWebUI等でご使用ください。\n- モデルはModelsフォルダの中にあります。\n\n\n----",
"# 免責事項\n- SFWおよびNSFW画像の作成は、個々のクリエイターの判断によります。モデル製作者は責任を負いません。\n- このモデルは、公共の場などでNSFWコンテンツを公開するために作られたモデルではありません。\n\n----",
"# ライセンス\n\n- このモデルはFair AI Public License 1.0-SDで権利と使用方法が規定されています。\n- ライセンスの全文は以下のリンクをお読みください。\nURL"
] | [
28,
29,
27,
93,
51,
30,
55,
40
] | [
"passage: TAGS\n#stable-diffusion #text-to-image #art #ja #license-other #region-us \n# ◆LizMix\n\n- SakuMixベースのアニメ向けマージモデル。\n\n----# ◆Discord\n\nJoin Discord Server\n- Hemlokのマージコミュニティです。レシピとか裏話はこちら。\n\n----# ◆モデル概要\n\n- Sampler: DPM++ 3M SDE Karras or DPM++ 2M SDE Karras 推奨。\n- Steps: 20-\n- Clipskip: 2\n- CFG Scale: 5-12\n- Denoise strength: 0.6\n- クオリティタグ(masterpiece,best quality等)は入れるとより絵柄が安定します。\n- 別途embeddingsをおすすめします。\n\n----# ◆サンプル\n\n\n- Prompt:\n\n\n---\n\n\n- Prompt:\n\n\n---\n\n\n- Prompt:\n\n\n---# ◆モデルの使い方\n\n- モデルをダウンロードしてWebUI等でご使用ください。\n- モデルはModelsフォルダの中にあります。\n\n\n----# 免責事項\n- SFWおよびNSFW画像の作成は、個々のクリエイターの判断によります。モデル製作者は責任を負いません。\n- このモデルは、公共の場などでNSFWコンテンツを公開するために作られたモデルではありません。\n\n----# ライセンス\n\n- このモデルはFair AI Public License 1.0-SDで権利と使用方法が規定されています。\n- ライセンスの全文は以下のリンクをお読みください。\nURL"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | saracandu/mistral-7b-harrypotter-BRIEF | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T19:06:22+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #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
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### Recommendations
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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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## 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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[optional]
BibTeX:
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## Model Card Contact
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"passage: TAGS\n#transformers #safetensors #mistral #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 | ## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
| {"library_name": "peft"} | null | Eskender/Saiga_2_7b_fine_tune_custom_data | [
"peft",
"region:us"
] | 2024-02-11T19:10:11+00:00 | [] | [] | TAGS
#peft #region-us
| ## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
| [
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: True\n- load_in_4bit: False\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: fp4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float32",
"### Framework versions\n\n\n- PEFT 0.5.0"
] | [
"TAGS\n#peft #region-us \n",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: True\n- load_in_4bit: False\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: fp4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float32",
"### Framework versions\n\n\n- PEFT 0.5.0"
] | [
9,
164,
11
] | [
"passage: TAGS\n#peft #region-us \n## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: True\n- load_in_4bit: False\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: fp4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float32### Framework versions\n\n\n- PEFT 0.5.0"
] | [
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null | null | null |
Llama7B fine-tuned (using QLoRA) for causal LM on data from human studies for iterative prisoner's dilemma.
| {"license": "mit"} | null | aegunal/llama7b_ft_ipd | [
"safetensors",
"license:mit",
"region:us"
] | 2024-02-11T19:10:21+00:00 | [] | [] | TAGS
#safetensors #license-mit #region-us
|
Llama7B fine-tuned (using QLoRA) for causal LM on data from human studies for iterative prisoner's dilemma.
| [] | [
"TAGS\n#safetensors #license-mit #region-us \n"
] | [
16
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null | null | transformers |
DPO Finetuned CultriX/NeuralTrix-7B-dpo using argilla/OpenHermes2.5-dpo-binarized-alpha
argilla dpo binarized pairs is a dataset built on top of: https://huggingface.co/datasets/teknium/OpenHermes-2.5 using https://github.com/argilla-io/distilabel if interested.
Thx for the great data sources. | {"language": ["en"], "license": "apache-2.0", "tags": ["conversation", "text-generation-inference", "CultriX/NeuralTrix-7B-dpo", "dpo", "merge"], "datasets": ["argilla/OpenHermes2.5-dpo-binarized-alpha"], "pipeline_tag": "text-generation"} | text-generation | eren23/dpo-binarized-NeuralTrix-7B | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversation",
"text-generation-inference",
"CultriX/NeuralTrix-7B-dpo",
"dpo",
"merge",
"en",
"dataset:argilla/OpenHermes2.5-dpo-binarized-alpha",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T19:23:46+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #conversation #text-generation-inference #CultriX/NeuralTrix-7B-dpo #dpo #merge #en #dataset-argilla/OpenHermes2.5-dpo-binarized-alpha #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
DPO Finetuned CultriX/NeuralTrix-7B-dpo using argilla/OpenHermes2.5-dpo-binarized-alpha
argilla dpo binarized pairs is a dataset built on top of: URL using URL if interested.
Thx for the great data sources. | [] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #conversation #text-generation-inference #CultriX/NeuralTrix-7B-dpo #dpo #merge #en #dataset-argilla/OpenHermes2.5-dpo-binarized-alpha #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
103
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversation #text-generation-inference #CultriX/NeuralTrix-7B-dpo #dpo #merge #en #dataset-argilla/OpenHermes2.5-dpo-binarized-alpha #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
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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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- PEFT 0.8.2 | {"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-chat-hf"} | null | NBA55/llama2-7B-diversity-improved-dataset-epoch_10-updated | [
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#peft #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-chat-hf #region-us
|
# Model Card for Model ID
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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
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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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## Technical Specifications [optional]
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null | null | transformers |
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| {"library_name": "transformers", "tags": []} | text-classification | karimimanesh/hashtag_stance_detection | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T19:24:23+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
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## 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 Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Model Architecture and Objective",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
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"#### Testing Data",
"#### Factors",
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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"
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null | null | peft | ## Training procedure
### Framework versions
- PEFT 0.4.0
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#peft #safetensors #region-us
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### Framework versions
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | tommymarto/LernnaviBERT_mcqbert3_students_answers_4096_mistral_seq_len_10 | [
"transformers",
"safetensors",
"bert",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-11T19:27:41+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us
|
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BibTeX:
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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",
"#### 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 #bert #arxiv-1910.09700 #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 #bert #arxiv-1910.09700 #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 | 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. -->
# mistral_instruct_generation
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 generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7713
## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- training_steps: 180
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7443 | 1.11 | 20 | 0.7713 |
| 0.7413 | 2.22 | 40 | 0.7713 |
| 0.7476 | 3.33 | 60 | 0.7713 |
| 0.753 | 4.44 | 80 | 0.7713 |
| 0.7514 | 5.56 | 100 | 0.7713 |
| 0.7383 | 6.67 | 120 | 0.7713 |
| 0.7434 | 7.78 | 140 | 0.7713 |
| 0.7497 | 8.89 | 160 | 0.7713 |
| 0.7644 | 10.0 | 180 | 0.7713 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "mistralai/Mistral-7B-Instruct-v0.1", "model-index": [{"name": "mistral_instruct_generation", "results": []}]} | null | Shivprasad17/mistral_instruct_generation | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | 2024-02-11T19:27:47+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-Instruct-v0.1 #license-apache-2.0 #region-us
| mistral\_instruct\_generation
=============================
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.1 on the generator dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7713
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: 4
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* lr\_scheduler\_warmup\_steps: 0.03
* training\_steps: 180
### Training results
### Framework versions
* PEFT 0.8.2
* Transformers 4.37.2
* Pytorch 2.2.0+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: 0.0002\n* train\\_batch\\_size: 4\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: constant\n* lr\\_scheduler\\_warmup\\_steps: 0.03\n* training\\_steps: 180",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-Instruct-v0.1 #license-apache-2.0 #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: 4\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: constant\n* lr\\_scheduler\\_warmup\\_steps: 0.03\n* training\\_steps: 180",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
64,
115,
4,
39
] | [
"passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-Instruct-v0.1 #license-apache-2.0 #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: 4\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: constant\n* lr\\_scheduler\\_warmup\\_steps: 0.03\n* training\\_steps: 180### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0+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": []} | text-generation | Novin-AI/Hermes-v0.1 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T19:29:01+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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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]
## 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 | transformers |
# Model Card for Model ID
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[More Information Needed] | {"license": "apache-2.0", "library_name": "transformers"} | text-generation | yam-peleg/Experiment8-7B | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #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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## 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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## Technical Specifications [optional]
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### Compute Infrastructure
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[optional]
BibTeX:
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## Glossary [optional]
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## Model Card Contact
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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="AdrienGoldszal/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 | AdrienGoldszal/q-FrozenLake-v1-4x4-noSlippery | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-11T19:34:46+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 |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | elucidator8918/clinical-ehr-prototype-0.2 | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-11T19:36:50+00:00 | [
"1910.09700"
] | [] | TAGS
#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
### 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
- 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:
## Technical Specifications [optional]
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null | null | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# serhii-korobchenko/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0278
- Validation Loss: 0.0518
- Epoch: 2
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2634, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1720 | 0.0665 | 0 |
| 0.0473 | 0.0547 | 1 |
| 0.0278 | 0.0518 | 2 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "bert-base-cased", "model-index": [{"name": "serhii-korobchenko/bert-finetuned-ner", "results": []}]} | token-classification | serhii-korobchenko/bert-finetuned-ner | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"base_model:bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T19:37:34+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| serhii-korobchenko/bert-finetuned-ner
=====================================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0278
* Validation Loss: 0.0518
* Epoch: 2
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:
* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 2634, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: mixed\_float16
### Training results
### Framework versions
* Transformers 4.35.2
* TensorFlow 2.15.0
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2634, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: mixed\\_float16",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #base_model-bert-base-cased #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* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2634, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: mixed\\_float16",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
66,
231,
4,
31
] | [
"passage: TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #base_model-bert-base-cased #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* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2634, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: mixed\\_float16### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\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": []} | text-generation | djomo/MISTRALllux2000-7b-v6 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T19:39:00+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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## Uses
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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]
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## Evaluation
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#### Testing Data
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#### 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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[optional]
BibTeX:
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## Glossary [optional]
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## Model Card Contact
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"passage: TAGS\n#transformers #safetensors #mistral #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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