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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. -->
# EN_t5-base_15_spider
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2692
- Rouge2 Precision: 0.6128
- Rouge2 Recall: 0.3948
- Rouge2 Fmeasure: 0.4517
## 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: 30
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| No log | 1.0 | 234 | 0.2675 | 0.4828 | 0.3113 | 0.3529 |
| No log | 2.0 | 468 | 0.2426 | 0.5265 | 0.3444 | 0.3904 |
| 0.4735 | 3.0 | 702 | 0.2384 | 0.5486 | 0.3481 | 0.3988 |
| 0.4735 | 4.0 | 936 | 0.2388 | 0.5846 | 0.3787 | 0.4324 |
| 0.1397 | 5.0 | 1170 | 0.2407 | 0.5789 | 0.3672 | 0.4212 |
| 0.1397 | 6.0 | 1404 | 0.2429 | 0.5976 | 0.3835 | 0.4391 |
| 0.1028 | 7.0 | 1638 | 0.2489 | 0.587 | 0.3779 | 0.4317 |
| 0.1028 | 8.0 | 1872 | 0.2534 | 0.602 | 0.3897 | 0.4448 |
| 0.0837 | 9.0 | 2106 | 0.2576 | 0.5971 | 0.3838 | 0.4394 |
| 0.0837 | 10.0 | 2340 | 0.2555 | 0.6024 | 0.3848 | 0.4412 |
| 0.0712 | 11.0 | 2574 | 0.2638 | 0.6106 | 0.3894 | 0.4469 |
| 0.0712 | 12.0 | 2808 | 0.2680 | 0.6061 | 0.3874 | 0.4444 |
| 0.0649 | 13.0 | 3042 | 0.2667 | 0.6114 | 0.3924 | 0.4498 |
| 0.0649 | 14.0 | 3276 | 0.2683 | 0.6117 | 0.3934 | 0.4504 |
| 0.0614 | 15.0 | 3510 | 0.2692 | 0.6128 | 0.3948 | 0.4517 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.7.dev0
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "EN_t5-base_15_spider", "results": []}]} | text2text-generation | e22vvb/EN_t5-base_15_spider | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T07:01:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| EN\_t5-base\_15\_spider
=======================
This model is a fine-tuned version of t5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2692
* Rouge2 Precision: 0.6128
* Rouge2 Recall: 0.3948
* Rouge2 Fmeasure: 0.4517
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: 30
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15
### Training results
### Framework versions
* Transformers 4.26.1
* Pytorch 2.0.1+cu117
* Datasets 2.14.7.dev0
* Tokenizers 0.13.3
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.26.1\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.7.dev0\n* Tokenizers 0.13.3"
] | [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.26.1\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.7.dev0\n* Tokenizers 0.13.3"
] | [
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"passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #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: 5e-05\n* train\\_batch\\_size: 30\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15### Training results### Framework versions\n\n\n* Transformers 4.26.1\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.7.dev0\n* Tokenizers 0.13.3"
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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": "251.43 +/- 16.42", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Rockey786/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-12T07:04:39+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 | https://civitai.com/models/301800/guinaifen-honkai-star-rail | {"license": "creativeml-openrail-m"} | null | LarryAIDraw/guinaifen__honkai_star_rail_ | [
"license:creativeml-openrail-m",
"region:us"
] | 2024-02-12T07:05:11+00:00 | [] | [] | TAGS
#license-creativeml-openrail-m #region-us
| URL | [] | [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
] | [
18
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null | null | null | https://civitai.com/models/301840/endorsi-or-tower-of-god-new-world-game | {"license": "creativeml-openrail-m"} | null | LarryAIDraw/TowerOfGod_Endorsi_1 | [
"license:creativeml-openrail-m",
"region:us"
] | 2024-02-12T07:05:49+00:00 | [] | [] | TAGS
#license-creativeml-openrail-m #region-us
| URL | [] | [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
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18
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null | null | null | https://civitai.com/models/302471/ubel-or-frieren-beyond-journeys-end | {"license": "creativeml-openrail-m"} | null | LarryAIDraw/ubel_v1 | [
"license:creativeml-openrail-m",
"region:us"
] | 2024-02-12T07:06:11+00:00 | [] | [] | TAGS
#license-creativeml-openrail-m #region-us
| URL | [] | [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
] | [
18
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null | null | null | https://civitai.com/models/301290/hadesiv-mystic-wiz-black-cat | {"license": "creativeml-openrail-m"} | null | LarryAIDraw/hades_ver1 | [
"license:creativeml-openrail-m",
"region:us"
] | 2024-02-12T07:06:31+00:00 | [] | [] | TAGS
#license-creativeml-openrail-m #region-us
| URL | [] | [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
] | [
18
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null | null | null | https://civitai.com/models/301608/delta-the-eminence-in-shadow | {"license": "creativeml-openrail-m"} | null | LarryAIDraw/delta__kage_no_jitsuryokusha_ni_naritakute__ | [
"license:creativeml-openrail-m",
"region:us"
] | 2024-02-12T07:07:01+00:00 | [] | [] | TAGS
#license-creativeml-openrail-m #region-us
| URL | [] | [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
] | [
18
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null | null | null | https://civitai.com/models/300204/clara-honkai-star-rail | {"license": "creativeml-openrail-m"} | null | LarryAIDraw/clara__honkai_star_rail_ | [
"license:creativeml-openrail-m",
"region:us"
] | 2024-02-12T07:07:51+00:00 | [] | [] | TAGS
#license-creativeml-openrail-m #region-us
| URL | [] | [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
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null | null | null | https://civitai.com/models/299893/elysiamerge | {"license": "creativeml-openrail-m"} | null | LarryAIDraw/ElysiaMERGE | [
"license:creativeml-openrail-m",
"region:us"
] | 2024-02-12T07:08:13+00:00 | [] | [] | TAGS
#license-creativeml-openrail-m #region-us
| URL | [] | [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
] | [
18
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null | null | null | https://civitai.com/models/299914/grimm-sentouin-hakenshimasu | {"license": "creativeml-openrail-m"} | null | LarryAIDraw/GrimmV3-07 | [
"license:creativeml-openrail-m",
"region:us"
] | 2024-02-12T07:08:33+00:00 | [] | [] | TAGS
#license-creativeml-openrail-m #region-us
| URL | [] | [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
] | [
18
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null | null | null | https://civitai.com/models/181698/pozemka-arknights | {"license": "creativeml-openrail-m"} | null | LarryAIDraw/pozemka_arknights | [
"license:creativeml-openrail-m",
"region:us"
] | 2024-02-12T07:09:07+00:00 | [] | [] | TAGS
#license-creativeml-openrail-m #region-us
| URL | [] | [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
] | [
18
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null | null | peft |
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**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 | {"library_name": "peft", "base_model": "mistralai/Mistral-7B-Instruct-v0.1"} | null | seankhatiri/ludwig-webinar | [
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# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
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APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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] |
null | null | transformers |
# VinaLLaMA - State-of-the-art Vietnamese LLMs
Read our [Paper](https://huggingface.co/papers/2312.11011) | {"language": ["vi"], "license": "llama2"} | text-generation | LoneStriker/vinallama-7b-3.0bpw-h6-exl2 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"vi",
"arxiv:2312.11011",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T07:10:36+00:00 | [
"2312.11011"
] | [
"vi"
] | TAGS
#transformers #pytorch #llama #text-generation #vi #arxiv-2312.11011 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# VinaLLaMA - State-of-the-art Vietnamese LLMs
!image
Read our Paper | [
"# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #vi #arxiv-2312.11011 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper"
] | [
64,
24
] | [
"passage: TAGS\n#transformers #pytorch #llama #text-generation #vi #arxiv-2312.11011 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper"
] | [
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] |
null | null | transformers |
# VinaLLaMA - State-of-the-art Vietnamese LLMs
Read our [Paper](https://huggingface.co/papers/2312.11011) | {"language": ["vi"], "license": "llama2"} | text-generation | LoneStriker/vinallama-7b-4.0bpw-h6-exl2 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"vi",
"arxiv:2312.11011",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T07:11:58+00:00 | [
"2312.11011"
] | [
"vi"
] | TAGS
#transformers #pytorch #llama #text-generation #vi #arxiv-2312.11011 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# VinaLLaMA - State-of-the-art Vietnamese LLMs
!image
Read our Paper | [
"# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #vi #arxiv-2312.11011 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper"
] | [
64,
24
] | [
"passage: TAGS\n#transformers #pytorch #llama #text-generation #vi #arxiv-2312.11011 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper"
] | [
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] |
null | null | transformers |
# VinaLLaMA - State-of-the-art Vietnamese LLMs
Read our [Paper](https://huggingface.co/papers/2312.11011) | {"language": ["vi"], "license": "llama2"} | text-generation | LoneStriker/vinallama-7b-5.0bpw-h6-exl2 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"vi",
"arxiv:2312.11011",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T07:13:36+00:00 | [
"2312.11011"
] | [
"vi"
] | TAGS
#transformers #pytorch #llama #text-generation #vi #arxiv-2312.11011 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# VinaLLaMA - State-of-the-art Vietnamese LLMs
!image
Read our Paper | [
"# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #vi #arxiv-2312.11011 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper"
] | [
64,
24
] | [
"passage: TAGS\n#transformers #pytorch #llama #text-generation #vi #arxiv-2312.11011 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | sudipto-ducs/llama-2-7b-miniguanaco | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
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] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
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- Developed by:
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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:
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## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
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BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
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] |
null | null | null |
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` | {"license": "other", "tags": ["autotrain", "text-generation"], "widget": [{"text": "I love AutoTrain because "}]} | text-generation | meizano/autotrain-6pn5d-a3pus | [
"tensorboard",
"safetensors",
"autotrain",
"text-generation",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] | 2024-02-12T07:15:04+00:00 | [] | [] | TAGS
#tensorboard #safetensors #autotrain #text-generation #conversational #license-other #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit AutoTrain.
# Usage
| [
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
"TAGS\n#tensorboard #safetensors #autotrain #text-generation #conversational #license-other #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
41,
29,
3
] | [
"passage: TAGS\n#tensorboard #safetensors #autotrain #text-generation #conversational #license-other #endpoints_compatible #region-us \n# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.# Usage"
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] |
null | null | transformers |
# VinaLLaMA - State-of-the-art Vietnamese LLMs
Read our [Paper](https://huggingface.co/papers/2312.11011) | {"language": ["vi"], "license": "llama2"} | text-generation | LoneStriker/vinallama-7b-6.0bpw-h6-exl2 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"vi",
"arxiv:2312.11011",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T07:15:35+00:00 | [
"2312.11011"
] | [
"vi"
] | TAGS
#transformers #pytorch #llama #text-generation #vi #arxiv-2312.11011 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# VinaLLaMA - State-of-the-art Vietnamese LLMs
!image
Read our Paper | [
"# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #vi #arxiv-2312.11011 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper"
] | [
64,
24
] | [
"passage: TAGS\n#transformers #pytorch #llama #text-generation #vi #arxiv-2312.11011 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper"
] | [
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] |
null | null | transformers |
DPO Finetuned Kukedlc/NeuTrixOmniBe-7B-model-remix 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.
GGUF: https://huggingface.co/eren23/dpo-binarized-NeutrixOmnibe-7B-GGUF | {"language": ["en"], "license": "apache-2.0", "tags": ["merge", "dpo", "conversation", "text-generation-inference", "Kukedlc/NeuTrixOmniBe-7B-model-remix"], "datasets": ["argilla/OpenHermes2.5-dpo-binarized-alpha"], "pipeline_tag": "text-generation"} | text-generation | eren23/dpo-binarized-NeutrixOmnibe-7B | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"dpo",
"conversation",
"text-generation-inference",
"Kukedlc/NeuTrixOmniBe-7B-model-remix",
"en",
"dataset:argilla/OpenHermes2.5-dpo-binarized-alpha",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T07:15:55+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #merge #dpo #conversation #text-generation-inference #Kukedlc/NeuTrixOmniBe-7B-model-remix #en #dataset-argilla/OpenHermes2.5-dpo-binarized-alpha #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
DPO Finetuned Kukedlc/NeuTrixOmniBe-7B-model-remix 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.
GGUF: URL | [] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #dpo #conversation #text-generation-inference #Kukedlc/NeuTrixOmniBe-7B-model-remix #en #dataset-argilla/OpenHermes2.5-dpo-binarized-alpha #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
107
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #dpo #conversation #text-generation-inference #Kukedlc/NeuTrixOmniBe-7B-model-remix #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 | transformers |
# Model Card for Model ID
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## Technical Specifications [optional]
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| {"library_name": "transformers", "tags": []} | token-classification | kabir5297/deberta3base_1024 | [
"transformers",
"safetensors",
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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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## 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 |
# VinaLLaMA - State-of-the-art Vietnamese LLMs
Read our [Paper](https://huggingface.co/papers/2312.11011) | {"language": ["vi"], "license": "llama2"} | text-generation | LoneStriker/vinallama-7b-8.0bpw-h8-exl2 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"vi",
"arxiv:2312.11011",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T07:17:52+00:00 | [
"2312.11011"
] | [
"vi"
] | TAGS
#transformers #pytorch #llama #text-generation #vi #arxiv-2312.11011 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# VinaLLaMA - State-of-the-art Vietnamese LLMs
!image
Read our Paper | [
"# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #vi #arxiv-2312.11011 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper"
] | [
64,
24
] | [
"passage: TAGS\n#transformers #pytorch #llama #text-generation #vi #arxiv-2312.11011 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper"
] | [
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] |
null | null | transformers | This directory includes a few sample datasets to get you started.
* `california_housing_data*.csv` is California housing data from the 1990 US
Census; more information is available at:
https://developers.google.com/machine-learning/crash-course/california-housing-data-description
* `mnist_*.csv` is a small sample of the
[MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is
described at: http://yann.lecun.com/exdb/mnist/
* `anscombe.json` contains a copy of
[Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it
was originally described in
Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American
Statistician. 27 (1): 17-21. JSTOR 2682899.
and our copy was prepared by the
[vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).
| {} | fill-mask | Turka/dummy-model8 | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T07:23:12+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| This directory includes a few sample datasets to get you started.
* 'california_housing_data*.csv' is California housing data from the 1990 US
Census; more information is available at:
URL
* 'mnist_*.csv' is a small sample of the
MNIST database, which is
described at: URL
* 'URL' contains a copy of
Anscombe's quartet; it
was originally described in
Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American
Statistician. 27 (1): 17-21. JSTOR 2682899.
and our copy was prepared by the
vega_datasets library.
| [] | [
"TAGS\n#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
38
] | [
"passage: TAGS\n#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | diffusers | ### My-Pet-Dog-xzq 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-xzq | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-12T07:24:15+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-xzq 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-xzq 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#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"### My-Pet-Dog-xzq 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"
] | [
73,
59
] | [
"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-xzq 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 | transformers |
# VinaLLaMA - State-of-the-art Vietnamese LLMs
Read our [Paper](https://huggingface.co/papers/2312.11011) | {"language": ["vi"], "license": "llama2"} | text-generation | LoneStriker/vinallama-7b-AWQ | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"vi",
"arxiv:2312.11011",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2024-02-12T07:27:21+00:00 | [
"2312.11011"
] | [
"vi"
] | TAGS
#transformers #pytorch #safetensors #llama #text-generation #vi #arxiv-2312.11011 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# VinaLLaMA - State-of-the-art Vietnamese LLMs
!image
Read our Paper | [
"# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper"
] | [
"TAGS\n#transformers #pytorch #safetensors #llama #text-generation #vi #arxiv-2312.11011 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
"# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper"
] | [
72,
24
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #llama #text-generation #vi #arxiv-2312.11011 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper"
] | [
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null | null | transformers |
# NeuralPipe-7B-slerp
NeuralPipe-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218)
* [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: OpenPipe/mistral-ft-optimized-1218
layer_range: [0, 32]
- model: mlabonne/NeuralHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1218
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "macintoshtran/NeuralPipe-7B-slerp"
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"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "OpenPipe/mistral-ft-optimized-1218", "mlabonne/NeuralHermes-2.5-Mistral-7B"], "base_model": ["OpenPipe/mistral-ft-optimized-1218", "mlabonne/NeuralHermes-2.5-Mistral-7B"]} | text-generation | macintoshtran/NeuralPipe-7B-slerp | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"OpenPipe/mistral-ft-optimized-1218",
"mlabonne/NeuralHermes-2.5-Mistral-7B",
"base_model:OpenPipe/mistral-ft-optimized-1218",
"base_model:mlabonne/NeuralHermes-2.5-Mistral-7B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T07:29:20+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #OpenPipe/mistral-ft-optimized-1218 #mlabonne/NeuralHermes-2.5-Mistral-7B #base_model-OpenPipe/mistral-ft-optimized-1218 #base_model-mlabonne/NeuralHermes-2.5-Mistral-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# NeuralPipe-7B-slerp
NeuralPipe-7B-slerp is a merge of the following models using LazyMergekit:
* OpenPipe/mistral-ft-optimized-1218
* mlabonne/NeuralHermes-2.5-Mistral-7B
## Configuration
## Usage
| [
"# NeuralPipe-7B-slerp\n\nNeuralPipe-7B-slerp is a merge of the following models using LazyMergekit:\n* OpenPipe/mistral-ft-optimized-1218\n* mlabonne/NeuralHermes-2.5-Mistral-7B",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #OpenPipe/mistral-ft-optimized-1218 #mlabonne/NeuralHermes-2.5-Mistral-7B #base_model-OpenPipe/mistral-ft-optimized-1218 #base_model-mlabonne/NeuralHermes-2.5-Mistral-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# NeuralPipe-7B-slerp\n\nNeuralPipe-7B-slerp is a merge of the following models using LazyMergekit:\n* OpenPipe/mistral-ft-optimized-1218\n* mlabonne/NeuralHermes-2.5-Mistral-7B",
"## Configuration",
"## Usage"
] | [
126,
63,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #OpenPipe/mistral-ft-optimized-1218 #mlabonne/NeuralHermes-2.5-Mistral-7B #base_model-OpenPipe/mistral-ft-optimized-1218 #base_model-mlabonne/NeuralHermes-2.5-Mistral-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# NeuralPipe-7B-slerp\n\nNeuralPipe-7B-slerp is a merge of the following models using LazyMergekit:\n* OpenPipe/mistral-ft-optimized-1218\n* mlabonne/NeuralHermes-2.5-Mistral-7B## Configuration## Usage"
] | [
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null | null | sentence-transformers |
# LazarusNLP/all-indo-e5-small-v3
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('LazarusNLP/all-indo-e5-small-v3')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('LazarusNLP/all-indo-e5-small-v3')
model = AutoModel.from_pretrained('LazarusNLP/all-indo-e5-small-v3')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=LazarusNLP/all-indo-e5-small-v3)
## Training
The model was trained with the parameters:
**DataLoader**:
`MultiDatasetDataLoader.MultiDatasetDataLoader` of length 1077 with parameters:
```
{'batch_size': 'unknown'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 539,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"language": ["ind"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["indonli", "indolem/indo_story_cloze", "unicamp-dl/mmarco", "miracl/miracl", "LazarusNLP/multilingual-NLI-26lang-2mil7-id", "SEACrowd/wrete", "SEACrowd/indolem_ntp", "khalidalt/tydiqa-goldp", "SEACrowd/facqa", "indonesian-nlp/lfqa_id", "jakartaresearch/indoqa", "jakartaresearch/id-paraphrase-detection"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | LazarusNLP/all-indo-e5-small-v3 | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"ind",
"dataset:indonli",
"dataset:indolem/indo_story_cloze",
"dataset:unicamp-dl/mmarco",
"dataset:miracl/miracl",
"dataset:LazarusNLP/multilingual-NLI-26lang-2mil7-id",
"dataset:SEACrowd/wrete",
"dataset:SEACrowd/indolem_ntp",
"dataset:khalidalt/tydiqa-goldp",
"dataset:SEACrowd/facqa",
"dataset:indonesian-nlp/lfqa_id",
"dataset:jakartaresearch/indoqa",
"dataset:jakartaresearch/id-paraphrase-detection",
"endpoints_compatible",
"region:us"
] | 2024-02-12T07:30:16+00:00 | [] | [
"ind"
] | TAGS
#sentence-transformers #safetensors #bert #feature-extraction #sentence-similarity #transformers #ind #dataset-indonli #dataset-indolem/indo_story_cloze #dataset-unicamp-dl/mmarco #dataset-miracl/miracl #dataset-LazarusNLP/multilingual-NLI-26lang-2mil7-id #dataset-SEACrowd/wrete #dataset-SEACrowd/indolem_ntp #dataset-khalidalt/tydiqa-goldp #dataset-SEACrowd/facqa #dataset-indonesian-nlp/lfqa_id #dataset-jakartaresearch/indoqa #dataset-jakartaresearch/id-paraphrase-detection #endpoints_compatible #region-us
|
# LazarusNLP/all-indo-e5-small-v3
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'MultiDatasetDataLoader.MultiDatasetDataLoader' of length 1077 with parameters:
Loss:
'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
| [
"# LazarusNLP/all-indo-e5-small-v3\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'MultiDatasetDataLoader.MultiDatasetDataLoader' of length 1077 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
"TAGS\n#sentence-transformers #safetensors #bert #feature-extraction #sentence-similarity #transformers #ind #dataset-indonli #dataset-indolem/indo_story_cloze #dataset-unicamp-dl/mmarco #dataset-miracl/miracl #dataset-LazarusNLP/multilingual-NLI-26lang-2mil7-id #dataset-SEACrowd/wrete #dataset-SEACrowd/indolem_ntp #dataset-khalidalt/tydiqa-goldp #dataset-SEACrowd/facqa #dataset-indonesian-nlp/lfqa_id #dataset-jakartaresearch/indoqa #dataset-jakartaresearch/id-paraphrase-detection #endpoints_compatible #region-us \n",
"# LazarusNLP/all-indo-e5-small-v3\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'MultiDatasetDataLoader.MultiDatasetDataLoader' of length 1077 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
201,
60,
38,
64,
29,
92,
5,
6
] | [
"passage: TAGS\n#sentence-transformers #safetensors #bert #feature-extraction #sentence-similarity #transformers #ind #dataset-indonli #dataset-indolem/indo_story_cloze #dataset-unicamp-dl/mmarco #dataset-miracl/miracl #dataset-LazarusNLP/multilingual-NLI-26lang-2mil7-id #dataset-SEACrowd/wrete #dataset-SEACrowd/indolem_ntp #dataset-khalidalt/tydiqa-goldp #dataset-SEACrowd/facqa #dataset-indonesian-nlp/lfqa_id #dataset-jakartaresearch/indoqa #dataset-jakartaresearch/id-paraphrase-detection #endpoints_compatible #region-us \n# LazarusNLP/all-indo-e5-small-v3\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'MultiDatasetDataLoader.MultiDatasetDataLoader' of length 1077 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors"
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tinyllama-colorist-lora
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v0.3](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.3) 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 200
- 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": "apache-2.0", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v0.3", "model-index": [{"name": "tinyllama-colorist-lora", "results": []}]} | null | scrawlsbraid/tinyllama-colorist-lora | [
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v0.3",
"license:apache-2.0",
"region:us"
] | 2024-02-12T07:31:38+00:00 | [] | [] | TAGS
#tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-TinyLlama/TinyLlama-1.1B-Chat-v0.3 #license-apache-2.0 #region-us
|
# tinyllama-colorist-lora
This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v0.3 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 200
- 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
| [
"# tinyllama-colorist-lora\n\nThis model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v0.3 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: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 200\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#tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-TinyLlama/TinyLlama-1.1B-Chat-v0.3 #license-apache-2.0 #region-us \n",
"# tinyllama-colorist-lora\n\nThis model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v0.3 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: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 200\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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"passage: TAGS\n#tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-TinyLlama/TinyLlama-1.1B-Chat-v0.3 #license-apache-2.0 #region-us \n# tinyllama-colorist-lora\n\nThis model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v0.3 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: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 200\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 |
<!-- 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. -->
# ner-wand-test
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### 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": "microsoft/deberta-v3-base", "model-index": [{"name": "ner-wand-test", "results": []}]} | token-classification | blaze999/ner-wand-test | [
"transformers",
"tensorboard",
"safetensors",
"deberta-v2",
"token-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T07:38:25+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #deberta-v2 #token-classification #generated_from_trainer #base_model-microsoft/deberta-v3-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# ner-wand-test
This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
| [
"# ner-wand-test\n\nThis model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 30\n- mixed_precision_training: Native AMP",
"### 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 #tensorboard #safetensors #deberta-v2 #token-classification #generated_from_trainer #base_model-microsoft/deberta-v3-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# ner-wand-test\n\nThis model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 30\n- mixed_precision_training: Native AMP",
"### 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"
] | [
73,
33,
6,
12,
8,
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142,
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"passage: TAGS\n#transformers #tensorboard #safetensors #deberta-v2 #token-classification #generated_from_trainer #base_model-microsoft/deberta-v3-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# ner-wand-test\n\nThis model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 30\n- mixed_precision_training: Native AMP### 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 | peft | ## Training procedure
### Framework versions
- PEFT 0.4.0
| {"library_name": "peft"} | null | lourenswal/bloom_prompt_tuning_1707723677.92155 | [
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#peft #safetensors #region-us
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### Framework versions
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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": "149.99 +/- 24.97", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | mrxuanl/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-12T07:42:24+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. -->
# Whisper Tiny Hu v10 - cleaned
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 16.1 hu cleaned dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2366
- Wer Ortho: 21.1511
- Wer: 19.9338
## 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: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 300
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:-------:|
| 0.2738 | 0.83 | 1000 | 0.2861 | 32.5590 | 30.9570 |
| 0.1454 | 1.66 | 2000 | 0.2307 | 26.5776 | 25.1495 |
| 0.0811 | 2.49 | 3000 | 0.2144 | 24.4515 | 23.2845 |
| 0.0424 | 3.32 | 4000 | 0.2118 | 22.5739 | 21.3068 |
| 0.0248 | 4.15 | 5000 | 0.2154 | 22.3803 | 21.2875 |
| 0.0309 | 4.99 | 6000 | 0.2116 | 22.2384 | 21.0753 |
| 0.0254 | 5.82 | 7000 | 0.2211 | 22.2093 | 21.1493 |
| 0.0146 | 6.65 | 8000 | 0.2262 | 22.4545 | 21.3776 |
| 0.0134 | 7.48 | 9000 | 0.2294 | 21.2156 | 20.0238 |
| 0.011 | 8.31 | 10000 | 0.2366 | 21.1511 | 19.9338 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"language": ["hu"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_16_1"], "metrics": ["wer"], "base_model": "openai/whisper-tiny", "widget": [{"example_title": "Sample 1", "src": "https://huggingface.co/datasets/Hungarians/samples/resolve/main/Sample1.flac"}, {"example_title": "Sample 2", "src": "https://huggingface.co/datasets/Hungarians/samples/resolve/main/Sample2.flac"}], "pipeline_tag": "automatic-speech-recognition", "model-index": [{"name": "Whisper Tiny Hungarian v10 - cleaned", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 16.1 - Hungarian", "type": "mozilla-foundation/common_voice_16_1", "config": "hu", "split": "test", "args": "hu"}, "metrics": [{"type": "wer", "value": 19.9338, "name": "Wer"}]}]}]} | automatic-speech-recognition | sarpba/whisper-tiny-cv16.1-hu-v10-cleaned | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"hu",
"dataset:mozilla-foundation/common_voice_16_1",
"base_model:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-02-12T07:46:10+00:00 | [] | [
"hu"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hu #dataset-mozilla-foundation/common_voice_16_1 #base_model-openai/whisper-tiny #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Whisper Tiny Hu v10 - cleaned
=============================
This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 16.1 hu cleaned dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2366
* Wer Ortho: 21.1511
* Wer: 19.9338
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: 4e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant\_with\_warmup
* lr\_scheduler\_warmup\_steps: 300
* training\_steps: 10000
* mixed\_precision\_training: Native AMP
### 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: 4e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\\_with\\_warmup\n* lr\\_scheduler\\_warmup\\_steps: 300\n* training\\_steps: 10000\n* mixed\\_precision\\_training: Native AMP",
"### 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 #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hu #dataset-mozilla-foundation/common_voice_16_1 #base_model-openai/whisper-tiny #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: 4e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\\_with\\_warmup\n* lr\\_scheduler\\_warmup\\_steps: 300\n* training\\_steps: 10000\n* mixed\\_precision\\_training: Native AMP",
"### 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"
] | [
102,
165,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hu #dataset-mozilla-foundation/common_voice_16_1 #base_model-openai/whisper-tiny #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: 4e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\\_with\\_warmup\n* lr\\_scheduler\\_warmup\\_steps: 300\n* training\\_steps: 10000\n* mixed\\_precision\\_training: Native AMP### 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 | Ubaidbhat/Financial_Analyst | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2024-02-12T07:47:57+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #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]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
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"### 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 #autotrain_compatible #endpoints_compatible #has_space #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 #autotrain_compatible #endpoints_compatible #has_space #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 |
# Miquella 120B
## Model has been remade with the [fixed dequantization](https://huggingface.co/152334H/miqu-1-70b-sf) of miqu.
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
An attempt at re-creating [goliath-120b](https://huggingface.co/alpindale/goliath-120b) using the new miqu-1-70b model instead of Xwin.
The merge ratios are the same as goliath, only that Xwin is swapped with miqu.
### Models Merged
The following models were included in the merge:
* [miqu-1-70b](https://huggingface.co/alpindale/miqu-1-70b-fp16)
* [Euryale-1.3-L2-70B](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B)
Miquella the Unalloyed, by @eldrtchmoon
| {"tags": ["mergekit", "merge"], "base_model": []} | text-generation | LoneStriker/miquella-120b-5.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T07:52:24+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #mergekit #merge #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Miquella 120B
## Model has been remade with the fixed dequantization of miqu.
This is a merge of pre-trained language models created using mergekit.
An attempt at re-creating goliath-120b using the new miqu-1-70b model instead of Xwin.
The merge ratios are the same as goliath, only that Xwin is swapped with miqu.
### Models Merged
The following models were included in the merge:
* miqu-1-70b
* Euryale-1.3-L2-70B
!image/png
Miquella the Unalloyed, by @eldrtchmoon
| [
"# Miquella 120B",
"## Model has been remade with the fixed dequantization of miqu.\nThis is a merge of pre-trained language models created using mergekit.\nAn attempt at re-creating goliath-120b using the new miqu-1-70b model instead of Xwin.\n\nThe merge ratios are the same as goliath, only that Xwin is swapped with miqu.",
"### Models Merged\n\nThe following models were included in the merge:\n* miqu-1-70b\n* Euryale-1.3-L2-70B\n\n!image/png\nMiquella the Unalloyed, by @eldrtchmoon"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Miquella 120B",
"## Model has been remade with the fixed dequantization of miqu.\nThis is a merge of pre-trained language models created using mergekit.\nAn attempt at re-creating goliath-120b using the new miqu-1-70b model instead of Xwin.\n\nThe merge ratios are the same as goliath, only that Xwin is swapped with miqu.",
"### Models Merged\n\nThe following models were included in the merge:\n* miqu-1-70b\n* Euryale-1.3-L2-70B\n\n!image/png\nMiquella the Unalloyed, by @eldrtchmoon"
] | [
54,
5,
82,
50
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Miquella 120B## Model has been remade with the fixed dequantization of miqu.\nThis is a merge of pre-trained language models created using mergekit.\nAn attempt at re-creating goliath-120b using the new miqu-1-70b model instead of Xwin.\n\nThe merge ratios are the same as goliath, only that Xwin is swapped with miqu.### Models Merged\n\nThe following models were included in the merge:\n* miqu-1-70b\n* Euryale-1.3-L2-70B\n\n!image/png\nMiquella the Unalloyed, by @eldrtchmoon"
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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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8556
- Bleu: 52.8840
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "base_model": "Helsinki-NLP/opus-mt-en-fr", "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": "kde4", "config": "en-fr", "split": "train", "args": "en-fr"}, "metrics": [{"type": "bleu", "value": 52.88398487672078, "name": "Bleu"}]}]}]} | translation | y-oguchi/marian-finetuned-kde4-en-to-fr | [
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T07:55:16+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #base_model-Helsinki-NLP/opus-mt-en-fr #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8556
- Bleu: 52.8840
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8556\n- Bleu: 52.8840",
"## 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: 32\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\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 #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #base_model-Helsinki-NLP/opus-mt-en-fr #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8556\n- Bleu: 52.8840",
"## 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: 32\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\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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33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #base_model-Helsinki-NLP/opus-mt-en-fr #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8556\n- Bleu: 52.8840## 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: 32\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\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 |
# Qwen1.5-0.5B-Chat
## Introduction
Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:
* 6 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, and 72B;
* Significant performance improvement in human preference for chat models;
* Multilingual support of both base and chat models;
* Stable support of 32K context length for models of all sizes
* No need of `trust_remote_code`.
For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).
<br>
## Model Details
Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA and the mixture of SWA and full attention.
## Training details
We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization. However, DPO leads to improvements in human preference evaluation but degradation in benchmark evaluation. In the very near future, we will fix both problems.
## Requirements
The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
```
KeyError: 'qwen2'
```
## Quickstart
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen1.5-0.5B-Chat",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-0.5B-Chat")
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
For quantized models, we advise you to use the GPTQ, AWQ, and GGUF correspondents, namely `Qwen1.5-0.5B-Chat-GPTQ-Int4`, `Qwen1.5-0.5B-Chat-GPTQ-Int8`, `Qwen1.5-0.5B-Chat-AWQ`, and `Qwen1.5-0.5B-Chat-GGUF`.
## Tips
* If you encounter code switching or other bad cases, we advise you to use our provided hyper-parameters in `generation_config.json`.
## Citation
If you find our work helpful, feel free to give us a cite.
```
@article{qwen,
title={Qwen Technical Report},
author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu},
journal={arXiv preprint arXiv:2309.16609},
year={2023}
}
``` | {"language": ["en"], "license": "other", "tags": ["chat"], "license_name": "tongyi-qianwen-research", "license_link": "https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat/blob/main/LICENSE", "pipeline_tag": "text-generation"} | text-generation | SalmanHabeeb/qwen-llamafiles | [
"transformers",
"safetensors",
"gguf",
"qwen2",
"text-generation",
"chat",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T07:58:58+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #gguf #qwen2 #text-generation #chat #conversational #en #license-other #autotrain_compatible #endpoints_compatible #region-us
|
# Qwen1.5-0.5B-Chat
## Introduction
Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:
* 6 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, and 72B;
* Significant performance improvement in human preference for chat models;
* Multilingual support of both base and chat models;
* Stable support of 32K context length for models of all sizes
* No need of 'trust_remote_code'.
For more details, please refer to our blog post and GitHub repo.
<br>
## Model Details
Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA and the mixture of SWA and full attention.
## Training details
We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization. However, DPO leads to improvements in human preference evaluation but degradation in benchmark evaluation. In the very near future, we will fix both problems.
## Requirements
The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install 'transformers>=4.37.0', or you might encounter the following error:
## Quickstart
Here provides a code snippet with 'apply_chat_template' to show you how to load the tokenizer and model and how to generate contents.
For quantized models, we advise you to use the GPTQ, AWQ, and GGUF correspondents, namely 'Qwen1.5-0.5B-Chat-GPTQ-Int4', 'Qwen1.5-0.5B-Chat-GPTQ-Int8', 'Qwen1.5-0.5B-Chat-AWQ', and 'Qwen1.5-0.5B-Chat-GGUF'.
## Tips
* If you encounter code switching or other bad cases, we advise you to use our provided hyper-parameters in 'generation_config.json'.
If you find our work helpful, feel free to give us a cite.
| [
"# Qwen1.5-0.5B-Chat",
"## Introduction\n\nQwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include: \n\n* 6 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, and 72B;\n* Significant performance improvement in human preference for chat models;\n* Multilingual support of both base and chat models;\n* Stable support of 32K context length for models of all sizes\n* No need of 'trust_remote_code'.\n\nFor more details, please refer to our blog post and GitHub repo.\n<br>",
"## Model Details\nQwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA and the mixture of SWA and full attention.",
"## Training details\nWe pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization. However, DPO leads to improvements in human preference evaluation but degradation in benchmark evaluation. In the very near future, we will fix both problems.",
"## Requirements\nThe code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install 'transformers>=4.37.0', or you might encounter the following error:",
"## Quickstart\n\nHere provides a code snippet with 'apply_chat_template' to show you how to load the tokenizer and model and how to generate contents.\n\n\n\nFor quantized models, we advise you to use the GPTQ, AWQ, and GGUF correspondents, namely 'Qwen1.5-0.5B-Chat-GPTQ-Int4', 'Qwen1.5-0.5B-Chat-GPTQ-Int8', 'Qwen1.5-0.5B-Chat-AWQ', and 'Qwen1.5-0.5B-Chat-GGUF'.",
"## Tips\n\n* If you encounter code switching or other bad cases, we advise you to use our provided hyper-parameters in 'generation_config.json'.\n\n\nIf you find our work helpful, feel free to give us a cite."
] | [
"TAGS\n#transformers #safetensors #gguf #qwen2 #text-generation #chat #conversational #en #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"# Qwen1.5-0.5B-Chat",
"## Introduction\n\nQwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include: \n\n* 6 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, and 72B;\n* Significant performance improvement in human preference for chat models;\n* Multilingual support of both base and chat models;\n* Stable support of 32K context length for models of all sizes\n* No need of 'trust_remote_code'.\n\nFor more details, please refer to our blog post and GitHub repo.\n<br>",
"## Model Details\nQwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA and the mixture of SWA and full attention.",
"## Training details\nWe pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization. However, DPO leads to improvements in human preference evaluation but degradation in benchmark evaluation. In the very near future, we will fix both problems.",
"## Requirements\nThe code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install 'transformers>=4.37.0', or you might encounter the following error:",
"## Quickstart\n\nHere provides a code snippet with 'apply_chat_template' to show you how to load the tokenizer and model and how to generate contents.\n\n\n\nFor quantized models, we advise you to use the GPTQ, AWQ, and GGUF correspondents, namely 'Qwen1.5-0.5B-Chat-GPTQ-Int4', 'Qwen1.5-0.5B-Chat-GPTQ-Int8', 'Qwen1.5-0.5B-Chat-AWQ', and 'Qwen1.5-0.5B-Chat-GGUF'.",
"## Tips\n\n* If you encounter code switching or other bad cases, we advise you to use our provided hyper-parameters in 'generation_config.json'.\n\n\nIf you find our work helpful, feel free to give us a cite."
] | [
55,
9,
150,
128,
70,
44,
130,
52
] | [
"passage: TAGS\n#transformers #safetensors #gguf #qwen2 #text-generation #chat #conversational #en #license-other #autotrain_compatible #endpoints_compatible #region-us \n# Qwen1.5-0.5B-Chat## Introduction\n\nQwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include: \n\n* 6 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, and 72B;\n* Significant performance improvement in human preference for chat models;\n* Multilingual support of both base and chat models;\n* Stable support of 32K context length for models of all sizes\n* No need of 'trust_remote_code'.\n\nFor more details, please refer to our blog post and GitHub repo.\n<br>## Model Details\nQwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA and the mixture of SWA and full attention.## Training details\nWe pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization. However, DPO leads to improvements in human preference evaluation but degradation in benchmark evaluation. In the very near future, we will fix both problems.## Requirements\nThe code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install 'transformers>=4.37.0', or you might encounter the following error:"
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null | null | transformers | # DarkSapling-7B-v2.0
## Model Details
- A result of 4 models merge. DARE TIES method was used this time so resulting model better preserves characteristics of all included models than v1.x.
- models used for merge:
[cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser)
[KoboldAI/Mistral-7B-Holodeck-1](https://huggingface.co/KoboldAI/Mistral-7B-Holodeck-1)
[KoboldAI/Mistral-7B-Erebus-v3](https://huggingface.co/KoboldAI/Mistral-7B-Erebus-v3)
[cognitivecomputations/samantha-mistral-7b](https://huggingface.co/cognitivecomputations/samantha-mistral-7b)
- See [mergekit-config.yml](https://huggingface.co/TeeZee/DarkSapling-7B-v2.0/resolve/main/mergekit-config.yml) for details on the merge method used.
**Warning: This model can produce NSFW content!**
## Results
- a little different than version v1.0, more romantic and empathetic.
- smarter than versions 1.0 and 1.1.
- best for one-on-one ERP.
- produces SFW nad NSFW content without issues, switches context seamlessly.
- sticks to character card
- pretty smart due to mistral, empathetic after Samantha and sometimes produces dark scenarions - Erebus.
- storytelling is satisfactory due to Holodeck
- good at following instructions
All comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel:
<a href="https://www.buymeacoffee.com/TeeZee" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
| {"language": ["en"], "license": "apache-2.0", "tags": ["mistral", "not-for-all-audiences", "merge"], "pipeline_tag": "text-generation", "inference": false} | text-generation | TeeZee/DarkSapling-7B-v2.0 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"not-for-all-audiences",
"merge",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T08:04:21+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #not-for-all-audiences #merge #en #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
| # DarkSapling-7B-v2.0
!image/png
## Model Details
- A result of 4 models merge. DARE TIES method was used this time so resulting model better preserves characteristics of all included models than v1.x.
- models used for merge:
cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
KoboldAI/Mistral-7B-Holodeck-1
KoboldAI/Mistral-7B-Erebus-v3
cognitivecomputations/samantha-mistral-7b
- See URL for details on the merge method used.
Warning: This model can produce NSFW content!
## Results
- a little different than version v1.0, more romantic and empathetic.
- smarter than versions 1.0 and 1.1.
- best for one-on-one ERP.
- produces SFW nad NSFW content without issues, switches context seamlessly.
- sticks to character card
- pretty smart due to mistral, empathetic after Samantha and sometimes produces dark scenarions - Erebus.
- storytelling is satisfactory due to Holodeck
- good at following instructions
All comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel:
<a href="URL target="_blank"><img src="URL alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
| [
"# DarkSapling-7B-v2.0\n\n!image/png",
"## Model Details\n\n- A result of 4 models merge. DARE TIES method was used this time so resulting model better preserves characteristics of all included models than v1.x.\n- models used for merge:\n cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser\n KoboldAI/Mistral-7B-Holodeck-1\n KoboldAI/Mistral-7B-Erebus-v3\n cognitivecomputations/samantha-mistral-7b\n- See URL for details on the merge method used.\n\nWarning: This model can produce NSFW content!",
"## Results\n\n- a little different than version v1.0, more romantic and empathetic.\n- smarter than versions 1.0 and 1.1.\n- best for one-on-one ERP.\n- produces SFW nad NSFW content without issues, switches context seamlessly.\n- sticks to character card\n- pretty smart due to mistral, empathetic after Samantha and sometimes produces dark scenarions - Erebus.\n- storytelling is satisfactory due to Holodeck\n- good at following instructions\n\n\nAll comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel:\n<a href=\"URL target=\"_blank\"><img src=\"URL alt=\"Buy Me A Coffee\" style=\"height: 60px !important;width: 217px !important;\" ></a>"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #not-for-all-audiences #merge #en #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n",
"# DarkSapling-7B-v2.0\n\n!image/png",
"## Model Details\n\n- A result of 4 models merge. DARE TIES method was used this time so resulting model better preserves characteristics of all included models than v1.x.\n- models used for merge:\n cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser\n KoboldAI/Mistral-7B-Holodeck-1\n KoboldAI/Mistral-7B-Erebus-v3\n cognitivecomputations/samantha-mistral-7b\n- See URL for details on the merge method used.\n\nWarning: This model can produce NSFW content!",
"## Results\n\n- a little different than version v1.0, more romantic and empathetic.\n- smarter than versions 1.0 and 1.1.\n- best for one-on-one ERP.\n- produces SFW nad NSFW content without issues, switches context seamlessly.\n- sticks to character card\n- pretty smart due to mistral, empathetic after Samantha and sometimes produces dark scenarions - Erebus.\n- storytelling is satisfactory due to Holodeck\n- good at following instructions\n\n\nAll comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel:\n<a href=\"URL target=\"_blank\"><img src=\"URL alt=\"Buy Me A Coffee\" style=\"height: 60px !important;width: 217px !important;\" ></a>"
] | [
61,
14,
130,
177
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #not-for-all-audiences #merge #en #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n# DarkSapling-7B-v2.0\n\n!image/png## Model Details\n\n- A result of 4 models merge. DARE TIES method was used this time so resulting model better preserves characteristics of all included models than v1.x.\n- models used for merge:\n cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser\n KoboldAI/Mistral-7B-Holodeck-1\n KoboldAI/Mistral-7B-Erebus-v3\n cognitivecomputations/samantha-mistral-7b\n- See URL for details on the merge method used.\n\nWarning: This model can produce NSFW content!## Results\n\n- a little different than version v1.0, more romantic and empathetic.\n- smarter than versions 1.0 and 1.1.\n- best for one-on-one ERP.\n- produces SFW nad NSFW content without issues, switches context seamlessly.\n- sticks to character card\n- pretty smart due to mistral, empathetic after Samantha and sometimes produces dark scenarions - Erebus.\n- storytelling is satisfactory due to Holodeck\n- good at following instructions\n\n\nAll comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel:\n<a href=\"URL target=\"_blank\"><img src=\"URL alt=\"Buy Me A Coffee\" style=\"height: 60px !important;width: 217px !important;\" ></a>"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | YashRawal225/NewfineFormatedData | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
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#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
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## Uses
### Direct 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
#### Summary
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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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APA:
## Glossary [optional]
## More Information [optional]
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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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## Training Details
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## Evaluation
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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-fa-zwnj-base-ner-finetuned-imdb
This model is a fine-tuned version of [HooshvareLab/distilbert-fa-zwnj-base-ner](https://huggingface.co/HooshvareLab/distilbert-fa-zwnj-base-ner) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4208
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.6035 | 1.0 | 157 | 4.9251 |
| 4.8152 | 2.0 | 314 | 4.5462 |
| 4.5829 | 3.0 | 471 | 4.4394 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "base_model": "HooshvareLab/distilbert-fa-zwnj-base-ner", "model-index": [{"name": "distilbert-fa-zwnj-base-ner-finetuned-imdb", "results": []}]} | fill-mask | drAliMollaei/distilbert-fa-zwnj-base-ner-finetuned-imdb | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"fill-mask",
"generated_from_trainer",
"base_model:HooshvareLab/distilbert-fa-zwnj-base-ner",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T08:06:55+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #distilbert #fill-mask #generated_from_trainer #base_model-HooshvareLab/distilbert-fa-zwnj-base-ner #autotrain_compatible #endpoints_compatible #region-us
| distilbert-fa-zwnj-base-ner-finetuned-imdb
==========================================
This model is a fine-tuned version of HooshvareLab/distilbert-fa-zwnj-base-ner on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 4.4208
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
* 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
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP",
"### 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: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP",
"### 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"
] | [
73,
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"passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #fill-mask #generated_from_trainer #base_model-HooshvareLab/distilbert-fa-zwnj-base-ner #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP### 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8556
- Bleu: 52.8840
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "base_model": "Helsinki-NLP/opus-mt-en-fr", "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": "kde4", "config": "en-fr", "split": "train", "args": "en-fr"}, "metrics": [{"type": "bleu", "value": 52.88398487672078, "name": "Bleu"}]}]}]} | translation | sophiayk/marian-finetuned-kde4-en-to-fr | [
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T08:08:59+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #base_model-Helsinki-NLP/opus-mt-en-fr #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8556
- Bleu: 52.8840
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8556\n- Bleu: 52.8840",
"## 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: 32\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\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 #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #base_model-Helsinki-NLP/opus-mt-en-fr #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8556\n- Bleu: 52.8840",
"## 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: 32\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\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"
] | [
94,
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"passage: TAGS\n#transformers #tensorboard #safetensors #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #base_model-Helsinki-NLP/opus-mt-en-fr #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8556\n- Bleu: 52.8840## 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: 32\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\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 | 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. -->
# NXAIR_M_mistral-7B
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8751
## 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: 0.00025
- 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_ratio: 0.03
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0718 | 0.16 | 100 | 1.0160 |
| 0.9627 | 0.32 | 200 | 0.9874 |
| 0.8621 | 0.48 | 300 | 0.8852 |
| 0.8674 | 0.63 | 400 | 0.8725 |
| 0.8039 | 0.79 | 500 | 0.8270 |
| 0.7757 | 0.95 | 600 | 0.8043 |
| 0.5737 | 1.11 | 700 | 0.7233 |
| 0.6043 | 1.27 | 800 | 0.7233 |
| 0.5896 | 1.43 | 900 | 0.7176 |
| 0.5701 | 1.58 | 1000 | 0.7050 |
| 0.5474 | 1.74 | 1100 | 0.7020 |
| 0.5622 | 1.9 | 1200 | 0.6686 |
| 0.4321 | 2.06 | 1300 | 0.7203 |
| 0.4063 | 2.22 | 1400 | 0.7155 |
| 0.4318 | 2.38 | 1500 | 0.7143 |
| 0.4375 | 2.54 | 1600 | 0.7128 |
| 0.4377 | 2.69 | 1700 | 0.6971 |
| 0.4364 | 2.85 | 1800 | 0.7102 |
| 0.4224 | 3.01 | 1900 | 0.6962 |
| 0.3352 | 3.17 | 2000 | 0.7134 |
| 0.3973 | 3.33 | 2100 | 0.7228 |
| 0.3907 | 3.49 | 2200 | 0.7293 |
| 0.3843 | 3.65 | 2300 | 0.7406 |
| 0.3972 | 3.8 | 2400 | 0.7381 |
| 0.4118 | 3.96 | 2500 | 0.7100 |
| 0.3011 | 4.12 | 2600 | 0.7390 |
| 0.3211 | 4.28 | 2700 | 0.7564 |
| 0.3228 | 4.44 | 2800 | 0.7676 |
| 0.3051 | 4.6 | 2900 | 0.7419 |
| 0.3272 | 4.75 | 3000 | 0.7520 |
| 0.3758 | 4.91 | 3100 | 0.7169 |
| 0.2952 | 5.07 | 3200 | 0.8331 |
| 0.3521 | 5.23 | 3300 | 0.7892 |
| 0.3582 | 5.39 | 3400 | 0.8023 |
| 0.3583 | 5.55 | 3500 | 0.7672 |
| 0.38 | 5.71 | 3600 | 0.7964 |
| 0.3735 | 5.86 | 3700 | 0.7602 |
| 0.3332 | 6.02 | 3800 | 0.8012 |
| 0.2981 | 6.18 | 3900 | 0.8070 |
| 0.3074 | 6.34 | 4000 | 0.7881 |
| 0.3579 | 6.5 | 4100 | 0.7447 |
| 0.3639 | 6.66 | 4200 | 0.7517 |
| 0.3481 | 6.81 | 4300 | 0.7815 |
| 0.3784 | 6.97 | 4400 | 0.7393 |
| 0.2917 | 7.13 | 4500 | 0.7802 |
| 0.2979 | 7.29 | 4600 | 0.7772 |
| 0.3005 | 7.45 | 4700 | 0.8432 |
| 0.3142 | 7.61 | 4800 | 0.8144 |
| 0.3468 | 7.77 | 4900 | 0.7675 |
| 0.3559 | 7.92 | 5000 | 0.7737 |
| 0.3028 | 8.08 | 5100 | 0.8472 |
| 0.3284 | 8.24 | 5200 | 0.8341 |
| 0.3123 | 8.4 | 5300 | 0.8470 |
| 0.3408 | 8.56 | 5400 | 0.7995 |
| 0.3283 | 8.72 | 5500 | 0.8048 |
| 0.3483 | 8.87 | 5600 | 0.8527 |
| 0.281 | 9.03 | 5700 | 0.8267 |
| 0.2738 | 9.19 | 5800 | 0.8195 |
| 0.3095 | 9.35 | 5900 | 0.8311 |
| 0.2954 | 9.51 | 6000 | 0.8241 |
| 0.309 | 9.67 | 6100 | 0.7944 |
| 0.3125 | 9.83 | 6200 | 0.8135 |
| 0.3339 | 9.98 | 6300 | 0.8094 |
| 0.3295 | 10.14 | 6400 | 0.8286 |
| 0.341 | 10.3 | 6500 | 0.8858 |
| 0.3157 | 10.46 | 6600 | 0.8527 |
| 0.3264 | 10.62 | 6700 | 0.8476 |
| 0.3631 | 10.78 | 6800 | 0.8255 |
| 0.3428 | 10.94 | 6900 | 0.8423 |
| 0.2963 | 11.09 | 7000 | 0.8148 |
| 0.3594 | 11.25 | 7100 | 0.8159 |
| 0.3309 | 11.41 | 7200 | 0.8058 |
| 0.3535 | 11.57 | 7300 | 0.8440 |
| 0.3679 | 11.73 | 7400 | 0.8273 |
| 0.3684 | 11.89 | 7500 | 0.7772 |
| 0.2645 | 12.04 | 7600 | 0.8764 |
| 0.3003 | 12.2 | 7700 | 0.8540 |
| 0.3225 | 12.36 | 7800 | 0.8711 |
| 0.3479 | 12.52 | 7900 | 0.8292 |
| 0.3414 | 12.68 | 8000 | 0.8558 |
| 0.3338 | 12.84 | 8100 | 0.8511 |
| 0.3569 | 13.0 | 8200 | 0.8418 |
| 0.3182 | 13.15 | 8300 | 0.8521 |
| 0.3119 | 13.31 | 8400 | 0.9313 |
| 0.3432 | 13.47 | 8500 | 0.8739 |
| 0.3366 | 13.63 | 8600 | 0.8637 |
| 0.3639 | 13.79 | 8700 | 0.8404 |
| 0.3764 | 13.95 | 8800 | 0.8386 |
| 0.2987 | 14.1 | 8900 | 0.8915 |
| 0.3061 | 14.26 | 9000 | 0.8548 |
| 0.3217 | 14.42 | 9100 | 0.8387 |
| 0.3166 | 14.58 | 9200 | 0.8253 |
| 0.3369 | 14.74 | 9300 | 0.8607 |
| 0.3461 | 14.9 | 9400 | 0.8751 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "NXAIR_M_mistral-7B", "results": []}]} | null | codewizardUV/NXAIR_M_mistral-7B | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | 2024-02-12T08:13:12+00:00 | [] | [] | TAGS
#peft #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us
| NXAIR\_M\_mistral-7B
====================
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8751
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: 0.00025
* 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\_ratio: 0.03
* num\_epochs: 15
### Training results
### Framework versions
* PEFT 0.8.2
* Transformers 4.37.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00025\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\\_ratio: 0.03\n* num\\_epochs: 15",
"### 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 0.15.2"
] | [
"TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-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: 0.00025\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\\_ratio: 0.03\n* num\\_epochs: 15",
"### 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 0.15.2"
] | [
51,
117,
4,
39
] | [
"passage: TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-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: 0.00025\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\\_ratio: 0.03\n* num\\_epochs: 15### 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 0.15.2"
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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_20 | [
"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
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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
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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 |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | tommymarto/LernnaviBERT_mcqbert1_students_answers_384_lstm_seq_len_20 | [
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# Model Card for Model ID
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## How to Get Started with the Model
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## Training Details
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## Evaluation
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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. -->
# DreamBooth - essiam/dbooth
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
## 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": ["text-to-image", "dreambooth", "stable-diffusion", "stable-diffusion-diffusers"], "inference": true, "base_model": "CompVis/stable-diffusion-v1-4", "instance_prompt": "a photo of sks dog"} | text-to-image | essiam/dbooth | [
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-12T08:22:32+00:00 | [] | [] | TAGS
#diffusers #tensorboard #safetensors #text-to-image #dreambooth #stable-diffusion #stable-diffusion-diffusers #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
|
# DreamBooth - essiam/dbooth
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using DreamBooth.
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
## 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] | [
"# DreamBooth - essiam/dbooth\n\nThis is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Training details\n\n[TODO: describe the data used to train the model]"
] | [
"TAGS\n#diffusers #tensorboard #safetensors #text-to-image #dreambooth #stable-diffusion #stable-diffusion-diffusers #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"# DreamBooth - essiam/dbooth\n\nThis is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False.",
"## 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]"
] | [
97,
78,
9,
5,
24,
16
] | [
"passage: TAGS\n#diffusers #tensorboard #safetensors #text-to-image #dreambooth #stable-diffusion #stable-diffusion-diffusers #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n# DreamBooth - essiam/dbooth\n\nThis is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False.## 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 |
# Miquella 120B
## Model has been remade with the [fixed dequantization](https://huggingface.co/152334H/miqu-1-70b-sf) of miqu.
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
An attempt at re-creating [goliath-120b](https://huggingface.co/alpindale/goliath-120b) using the new miqu-1-70b model instead of Xwin.
The merge ratios are the same as goliath, only that Xwin is swapped with miqu.
### Models Merged
The following models were included in the merge:
* [miqu-1-70b](https://huggingface.co/alpindale/miqu-1-70b-fp16)
* [Euryale-1.3-L2-70B](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B)
Miquella the Unalloyed, by @eldrtchmoon
| {"tags": ["mergekit", "merge"], "base_model": []} | text-generation | LoneStriker/miquella-120b-5.5bpw-h6-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T08:23:54+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #mergekit #merge #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Miquella 120B
## Model has been remade with the fixed dequantization of miqu.
This is a merge of pre-trained language models created using mergekit.
An attempt at re-creating goliath-120b using the new miqu-1-70b model instead of Xwin.
The merge ratios are the same as goliath, only that Xwin is swapped with miqu.
### Models Merged
The following models were included in the merge:
* miqu-1-70b
* Euryale-1.3-L2-70B
!image/png
Miquella the Unalloyed, by @eldrtchmoon
| [
"# Miquella 120B",
"## Model has been remade with the fixed dequantization of miqu.\nThis is a merge of pre-trained language models created using mergekit.\nAn attempt at re-creating goliath-120b using the new miqu-1-70b model instead of Xwin.\n\nThe merge ratios are the same as goliath, only that Xwin is swapped with miqu.",
"### Models Merged\n\nThe following models were included in the merge:\n* miqu-1-70b\n* Euryale-1.3-L2-70B\n\n!image/png\nMiquella the Unalloyed, by @eldrtchmoon"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Miquella 120B",
"## Model has been remade with the fixed dequantization of miqu.\nThis is a merge of pre-trained language models created using mergekit.\nAn attempt at re-creating goliath-120b using the new miqu-1-70b model instead of Xwin.\n\nThe merge ratios are the same as goliath, only that Xwin is swapped with miqu.",
"### Models Merged\n\nThe following models were included in the merge:\n* miqu-1-70b\n* Euryale-1.3-L2-70B\n\n!image/png\nMiquella the Unalloyed, by @eldrtchmoon"
] | [
54,
5,
82,
50
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Miquella 120B## Model has been remade with the fixed dequantization of miqu.\nThis is a merge of pre-trained language models created using mergekit.\nAn attempt at re-creating goliath-120b using the new miqu-1-70b model instead of Xwin.\n\nThe merge ratios are the same as goliath, only that Xwin is swapped with miqu.### Models Merged\n\nThe following models were included in the merge:\n* miqu-1-70b\n* Euryale-1.3-L2-70B\n\n!image/png\nMiquella the Unalloyed, by @eldrtchmoon"
] | [
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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. -->
# EN_t5-base_8_spider
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2576
- Rouge2 Precision: 0.5995
- Rouge2 Recall: 0.3907
- Rouge2 Fmeasure: 0.4452
## 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: 10
- eval_batch_size: 16
- 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 | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.5688 | 1.0 | 700 | 0.2352 | 0.5179 | 0.334 | 0.38 |
| 0.1863 | 2.0 | 1400 | 0.2287 | 0.5608 | 0.3634 | 0.4139 |
| 0.1145 | 3.0 | 2100 | 0.2350 | 0.5824 | 0.3758 | 0.4289 |
| 0.1013 | 4.0 | 2800 | 0.2426 | 0.5818 | 0.3713 | 0.4253 |
| 0.0818 | 5.0 | 3500 | 0.2450 | 0.5888 | 0.377 | 0.4313 |
| 0.0729 | 6.0 | 4200 | 0.2507 | 0.5968 | 0.3841 | 0.4393 |
| 0.0693 | 7.0 | 4900 | 0.2545 | 0.5899 | 0.3848 | 0.4384 |
| 0.0635 | 8.0 | 5600 | 0.2576 | 0.5995 | 0.3907 | 0.4452 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.7.dev0
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "EN_t5-base_8_spider", "results": []}]} | text2text-generation | e22vvb/EN_t5-base_8_spider | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T08:25:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| EN\_t5-base\_8\_spider
======================
This model is a fine-tuned version of t5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2576
* Rouge2 Precision: 0.5995
* Rouge2 Recall: 0.3907
* Rouge2 Fmeasure: 0.4452
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: 10
* eval\_batch\_size: 16
* 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 4.26.1
* Pytorch 2.0.1+cu117
* Datasets 2.14.7.dev0
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 10\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: 8",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.26.1\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.7.dev0\n* Tokenizers 0.13.3"
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"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #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: 5e-05\n* train\\_batch\\_size: 10\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: 8",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.26.1\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.7.dev0\n* Tokenizers 0.13.3"
] | [
67,
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"passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #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: 5e-05\n* train\\_batch\\_size: 10\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: 8### Training results### Framework versions\n\n\n* Transformers 4.26.1\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.7.dev0\n* Tokenizers 0.13.3"
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null | null | transformers | ## Trendyol-LLM-7b-chat-v0.1-GGUF models
----
## Description
This repo contains all types of GGUF formatted model files for [Trendyol-LLM-7b-chat-v0.1](https://huggingface.co/Trendyol/Trendyol-LLM-7b-chat-v0.1).
<img src="https://huggingface.co/Trendyol/Trendyol-LLM-7b-chat-v0.1/resolve/main/llama-tr-image.jpeg"
alt="drawing" width="400"/>
## Quantized LLM models and methods
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [Trendyol-LLM-7b-chat-v0.1.Q2_K.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v0.1-GGUF/blob/main/trendyol-llm-7b-chat-v0.1.Q2_K.gguf) | Q2_K | 2 | 2.59 GB| 4.88 GB | smallest, significant quality loss - not recommended for most purposes |
| [Trendyol-LLM-7b-chat-v0.1.Q3_K_S.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v0.1-GGUF/blob/main/trendyol-llm-7b-chat-v0.1.Q3_K_S.gguf) | Q3_K_S | 3 | 3.01 GB| 5.56 GB | very small, high quality loss |
| [Trendyol-LLM-7b-chat-v0.1.Q3_K_M.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v0.1-GGUF/blob/main/trendyol-llm-7b-chat-v0.1.Q3_K_M.gguf) | Q3_K_M | 3 | 3.36 GB| 5.91 GB | very small, high quality loss |
| [Trendyol-LLM-7b-chat-v0.1.Q3_K_L.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v0.1-GGUF/blob/main/trendyol-llm-7b-chat-v0.1.Q3_K_L.gguf) | Q3_K_L | 3 | 3.66 GB| 6.20 GB | small, substantial quality loss |
| [Trendyol-LLM-7b-chat-v0.1.Q4_0.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v0.1-GGUF/blob/main/trendyol-llm-7b-chat-v0.1.Q4_0.gguf) | Q4_0 | 4 | 3.9 GB| 6.45 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Trendyol-LLM-7b-chat-v0.1.Q4_K_S.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v0.1-GGUF/blob/main/trendyol-llm-7b-chat-v0.1.Q4_K_S.gguf) | Q4_K_S | 4 | 3.93 GB| 6.48 GB | small, greater quality loss |
| [Trendyol-LLM-7b-chat-v0.1.Q4_K_M.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v0.1-GGUF/blob/main/trendyol-llm-7b-chat-v0.1.Q4_K_M.gguf) | Q4_K_M | 4 | 4.15 GB| 6.69 GB | medium, balanced quality - recommended |
| [Trendyol-LLM-7b-chat-v0.1.Q5_0.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v0.1-GGUF/blob/main/trendyol-llm-7b-chat-v0.1.Q5_0.gguf) | Q5_0 | 5 | 4.73 GB| 7.15 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Trendyol-LLM-7b-chat-v0.1.Q5_K_S.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v0.1-GGUF/blob/main/trendyol-llm-7b-chat-v0.1.Q5_K_S.gguf) | Q5_K_S | 5 | 4.75 GB| 7.27 GB | large, low quality loss - recommended |
| [Trendyol-LLM-7b-chat-v0.1.Q5_K_M.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v0.1-GGUF/blob/main/trendyol-llm-7b-chat-v0.1.Q5_K_M.gguf) | Q5_K_M | 5 | 4.86 GB| 7.40 GB | large, very low quality loss - recommended |
| [Trendyol-LLM-7b-chat-v0.1.Q6_K.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v0.1-GGUF/blob/main/trendyol-llm-7b-chat-v0.1.Q6_K.gguf) | Q6_K | 6 | 5.61 GB| 8.15 GB | very large, extremely low quality loss |
| [Trendyol-LLM-7b-chat-v0.1.Q8_0.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v0.1-GGUF/blob/main/trendyol-llm-7b-chat-v0.1.Q8_0.gguf) | Q8_0 | 8 | 7.27 GB| 9.81 GB | very large, extremely low quality loss - not recommended |
The names of the quantization methods follow the naming convention: "q" + the number of bits + the variant used (detailed below). Here is a list of all the models and their corresponding use cases, based on model cards made by [TheBloke](https://huggingface.co/TheBloke/):
* `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.
* `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q3_k_s`: Uses Q3_K for all tensors
* `q4_0`: Original quant method, 4-bit.
* `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
* `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K
* `q4_k_s`: Uses Q4_K for all tensors
* `q5_0`: Higher accuracy, higher resource usage and slower inference.
* `q5_1`: Even higher accuracy, resource usage and slower inference.
* `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K
* `q5_k_s`: Uses Q5_K for all tensors
* `q6_k`: Uses Q8_K for all tensors
* `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.
**TheBloke recommends using Q5_K_M** as it preserves most of the model's performance.
Alternatively, you can use Q4_K_M if you want to save some memory.
In general, K_M versions are better than K_S versions.
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
## Special thanks to [TheBloke on Huggingface](https://huggingface.co/TheBloke) and [Maxime Labonne on Github](https://github.com/mlabonne/llm-course)
-----
## Model Details
<img src="https://huggingface.co/Trendyol/Trendyol-LLM-7b-chat-v0.1/resolve/main/llama-tr-image.jpeg"
alt="drawing" width="400"/>
# **Trendyol LLM**
Trendyol LLM is a generative model that is based on LLaMa2 7B model. This is the repository for the chat model.
## Model Details
**Model Developers** Trendyol
**Variations** base and chat variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Trendyol LLM is an auto-regressive language model (based on LLaMa2 7b) that uses an optimized transformer architecture. The chat version is fine-tuned on 180K instruction sets with the following trainables by using LoRA:
- **lr**=1e-4
- **lora_rank**=64
- **lora_alpha**=128
- **lora_trainable**=q_proj,v_proj,k_proj,o_proj,gate_proj,down_proj,up_proj
- **modules_to_save**=embed_tokens,lm_head
- **lora_dropout**=0.05
- **fp16**=True
- **max_seq_length**=1024
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/lora_diagram.png"
alt="drawing" width="600"/>
## Usage
```python
from transformers import AutoModelForCausalLM, LlamaTokenizer, pipeline
model_id = "Trendyol/Trendyol-LLM-7b-chat-v0.1"
tokenizer = LlamaTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id,
device_map='auto',
load_in_8bit=True)
sampling_params = dict(do_sample=True, temperature=0.3, top_k=50, top_p=0.9)
pipe = pipeline("text-generation",
model=model,
tokenizer=tokenizer,
device_map="auto",
max_new_tokens=1024,
return_full_text=True,
repetition_penalty=1.1
)
DEFAULT_SYSTEM_PROMPT = "Sen yardımcı bir asistansın ve sana verilen talimatlar doğrultusunda en iyi cevabı üretmeye çalışacaksın.\n"
TEMPLATE = (
"[INST] <<SYS>>\n"
"{system_prompt}\n"
"<</SYS>>\n\n"
"{instruction} [/INST]"
)
def generate_prompt(instruction, system_prompt=DEFAULT_SYSTEM_PROMPT):
return TEMPLATE.format_map({'instruction': instruction,'system_prompt': system_prompt})
def generate_output(user_query, sys_prompt=DEFAULT_SYSTEM_PROMPT):
prompt = generate_prompt(user_query, sys_prompt)
outputs = pipe(prompt,
**sampling_params
)
return outputs[0]["generated_text"].split("[/INST]")[-1]
user_query = "Türkiye'de kaç il var?"
response = generate_output(user_query)
```
## Limitations, Risks, Bias, and Ethical Considerations
### Limitations and Known Biases
- **Primary Function and Application:** Trendyol LLM, an autoregressive language model, is primarily designed to predict the next token in a text string. While often used for various applications, it is important to note that it has not undergone extensive real-world application testing. Its effectiveness and reliability across diverse scenarios remain largely unverified.
- **Language Comprehension and Generation:** The model is primarily trained in standard English and Turkish. Its performance in understanding and generating slang, informal language, or other languages may be limited, leading to potential errors or misinterpretations.
- **Generation of False Information:** Users should be aware that Trendyol LLM may produce inaccurate or misleading information. Outputs should be considered as starting points or suggestions rather than definitive answers.
### Risks and Ethical Considerations
- **Potential for Harmful Use:** There is a risk that Trendyol LLM could be used to generate offensive or harmful language. We strongly discourage its use for any such purposes and emphasize the need for application-specific safety and fairness evaluations before deployment.
- **Unintended Content and Bias:** The model was trained on a large corpus of text data, which was not explicitly checked for offensive content or existing biases. Consequently, it may inadvertently produce content that reflects these biases or inaccuracies.
- **Toxicity:** Despite efforts to select appropriate training data, the model is capable of generating harmful content, especially when prompted explicitly. We encourage the open-source community to engage in developing strategies to minimize such risks.
### Recommendations for Safe and Ethical Usage
- **Human Oversight:** We recommend incorporating a human curation layer or using filters to manage and improve the quality of outputs, especially in public-facing applications. This approach can help mitigate the risk of generating objectionable content unexpectedly.
- **Application-Specific Testing:** Developers intending to use Trendyol LLM should conduct thorough safety testing and optimization tailored to their specific applications. This is crucial, as the model’s responses can be unpredictable and may occasionally be biased, inaccurate, or offensive.
- **Responsible Development and Deployment:** It is the responsibility of developers and users of Trendyol LLM to ensure its ethical and safe application. We urge users to be mindful of the model's limitations and to employ appropriate safeguards to prevent misuse or harmful consequences. | {"language": ["tr", "en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["trendyol", "llama-2", "turkish"], "model_name": "Trendyol-LLM-7b-chat-v0.1", "model_creator": "Trendyol", "base_model": "Trendyol/Trendyol-LLM-7b-chat-v0.1", "pipeline_tag": "text-generation", "model_type": "llama", "inference": false, "quantized_by": "tolgadev"} | text-generation | tolgadev/Trendyol-LLM-7b-chat-v0.1-GGUF | [
"transformers",
"gguf",
"llama",
"text-generation",
"trendyol",
"llama-2",
"turkish",
"tr",
"en",
"base_model:Trendyol/Trendyol-LLM-7b-chat-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2024-02-12T08:27:44+00:00 | [] | [
"tr",
"en"
] | TAGS
#transformers #gguf #llama #text-generation #trendyol #llama-2 #turkish #tr #en #base_model-Trendyol/Trendyol-LLM-7b-chat-v0.1 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| Trendyol-LLM-7b-chat-v0.1-GGUF models
-------------------------------------
---
Description
-----------
This repo contains all types of GGUF formatted model files for Trendyol-LLM-7b-chat-v0.1.
<img src="URL
alt="drawing" width="400"/>
Quantized LLM models and methods
--------------------------------
The names of the quantization methods follow the naming convention: "q" + the number of bits + the variant used (detailed below). Here is a list of all the models and their corresponding use cases, based on model cards made by TheBloke:
* 'q2\_k': Uses Q4\_K for the URL and feed\_forward.w2 tensors, Q2\_K for the other tensors.
* 'q3\_k\_l': Uses Q5\_K for the URL, URL, and feed\_forward.w2 tensors, else Q3\_K
* 'q3\_k\_m': Uses Q4\_K for the URL, URL, and feed\_forward.w2 tensors, else Q3\_K
* 'q3\_k\_s': Uses Q3\_K for all tensors
* 'q4\_0': Original quant method, 4-bit.
* 'q4\_1': Higher accuracy than q4\_0 but not as high as q5\_0. However has quicker inference than q5 models.
* 'q4\_k\_m': Uses Q6\_K for half of the URL and feed\_forward.w2 tensors, else Q4\_K
* 'q4\_k\_s': Uses Q4\_K for all tensors
* 'q5\_0': Higher accuracy, higher resource usage and slower inference.
* 'q5\_1': Even higher accuracy, resource usage and slower inference.
* 'q5\_k\_m': Uses Q6\_K for half of the URL and feed\_forward.w2 tensors, else Q5\_K
* 'q5\_k\_s': Uses Q5\_K for all tensors
* 'q6\_k': Uses Q8\_K for all tensors
* 'q8\_0': Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.
TheBloke recommends using Q5\_K\_M as it preserves most of the model's performance.
Alternatively, you can use Q4\_K\_M if you want to save some memory.
In general, K\_M versions are better than K\_S versions.
How to download GGUF files
--------------------------
Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* URL
Special thanks to TheBloke on Huggingface and Maxime Labonne on Github
----------------------------------------------------------------------
---
Model Details
-------------
<img src="URL
alt="drawing" width="400"/>
Trendyol LLM
============
Trendyol LLM is a generative model that is based on LLaMa2 7B model. This is the repository for the chat model.
Model Details
-------------
Model Developers Trendyol
Variations base and chat variations.
Input Models input text only.
Output Models generate text only.
Model Architecture Trendyol LLM is an auto-regressive language model (based on LLaMa2 7b) that uses an optimized transformer architecture. The chat version is fine-tuned on 180K instruction sets with the following trainables by using LoRA:
* lr=1e-4
* lora\_rank=64
* lora\_alpha=128
* lora\_trainable=q\_proj,v\_proj,k\_proj,o\_proj,gate\_proj,down\_proj,up\_proj
* modules\_to\_save=embed\_tokens,lm\_head
* lora\_dropout=0.05
* fp16=True
* max\_seq\_length=1024
<img src="URL
alt="drawing" width="600"/>
Usage
-----
Limitations, Risks, Bias, and Ethical Considerations
----------------------------------------------------
### Limitations and Known Biases
* Primary Function and Application: Trendyol LLM, an autoregressive language model, is primarily designed to predict the next token in a text string. While often used for various applications, it is important to note that it has not undergone extensive real-world application testing. Its effectiveness and reliability across diverse scenarios remain largely unverified.
* Language Comprehension and Generation: The model is primarily trained in standard English and Turkish. Its performance in understanding and generating slang, informal language, or other languages may be limited, leading to potential errors or misinterpretations.
* Generation of False Information: Users should be aware that Trendyol LLM may produce inaccurate or misleading information. Outputs should be considered as starting points or suggestions rather than definitive answers.
### Risks and Ethical Considerations
* Potential for Harmful Use: There is a risk that Trendyol LLM could be used to generate offensive or harmful language. We strongly discourage its use for any such purposes and emphasize the need for application-specific safety and fairness evaluations before deployment.
* Unintended Content and Bias: The model was trained on a large corpus of text data, which was not explicitly checked for offensive content or existing biases. Consequently, it may inadvertently produce content that reflects these biases or inaccuracies.
* Toxicity: Despite efforts to select appropriate training data, the model is capable of generating harmful content, especially when prompted explicitly. We encourage the open-source community to engage in developing strategies to minimize such risks.
### Recommendations for Safe and Ethical Usage
* Human Oversight: We recommend incorporating a human curation layer or using filters to manage and improve the quality of outputs, especially in public-facing applications. This approach can help mitigate the risk of generating objectionable content unexpectedly.
* Application-Specific Testing: Developers intending to use Trendyol LLM should conduct thorough safety testing and optimization tailored to their specific applications. This is crucial, as the model’s responses can be unpredictable and may occasionally be biased, inaccurate, or offensive.
* Responsible Development and Deployment: It is the responsibility of developers and users of Trendyol LLM to ensure its ethical and safe application. We urge users to be mindful of the model's limitations and to employ appropriate safeguards to prevent misuse or harmful consequences.
| [
"### Limitations and Known Biases\n\n\n* Primary Function and Application: Trendyol LLM, an autoregressive language model, is primarily designed to predict the next token in a text string. While often used for various applications, it is important to note that it has not undergone extensive real-world application testing. Its effectiveness and reliability across diverse scenarios remain largely unverified.\n* Language Comprehension and Generation: The model is primarily trained in standard English and Turkish. Its performance in understanding and generating slang, informal language, or other languages may be limited, leading to potential errors or misinterpretations.\n* Generation of False Information: Users should be aware that Trendyol LLM may produce inaccurate or misleading information. Outputs should be considered as starting points or suggestions rather than definitive answers.",
"### Risks and Ethical Considerations\n\n\n* Potential for Harmful Use: There is a risk that Trendyol LLM could be used to generate offensive or harmful language. We strongly discourage its use for any such purposes and emphasize the need for application-specific safety and fairness evaluations before deployment.\n* Unintended Content and Bias: The model was trained on a large corpus of text data, which was not explicitly checked for offensive content or existing biases. Consequently, it may inadvertently produce content that reflects these biases or inaccuracies.\n* Toxicity: Despite efforts to select appropriate training data, the model is capable of generating harmful content, especially when prompted explicitly. We encourage the open-source community to engage in developing strategies to minimize such risks.",
"### Recommendations for Safe and Ethical Usage\n\n\n* Human Oversight: We recommend incorporating a human curation layer or using filters to manage and improve the quality of outputs, especially in public-facing applications. This approach can help mitigate the risk of generating objectionable content unexpectedly.\n* Application-Specific Testing: Developers intending to use Trendyol LLM should conduct thorough safety testing and optimization tailored to their specific applications. This is crucial, as the model’s responses can be unpredictable and may occasionally be biased, inaccurate, or offensive.\n* Responsible Development and Deployment: It is the responsibility of developers and users of Trendyol LLM to ensure its ethical and safe application. We urge users to be mindful of the model's limitations and to employ appropriate safeguards to prevent misuse or harmful consequences."
] | [
"TAGS\n#transformers #gguf #llama #text-generation #trendyol #llama-2 #turkish #tr #en #base_model-Trendyol/Trendyol-LLM-7b-chat-v0.1 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n",
"### Limitations and Known Biases\n\n\n* Primary Function and Application: Trendyol LLM, an autoregressive language model, is primarily designed to predict the next token in a text string. While often used for various applications, it is important to note that it has not undergone extensive real-world application testing. Its effectiveness and reliability across diverse scenarios remain largely unverified.\n* Language Comprehension and Generation: The model is primarily trained in standard English and Turkish. Its performance in understanding and generating slang, informal language, or other languages may be limited, leading to potential errors or misinterpretations.\n* Generation of False Information: Users should be aware that Trendyol LLM may produce inaccurate or misleading information. Outputs should be considered as starting points or suggestions rather than definitive answers.",
"### Risks and Ethical Considerations\n\n\n* Potential for Harmful Use: There is a risk that Trendyol LLM could be used to generate offensive or harmful language. We strongly discourage its use for any such purposes and emphasize the need for application-specific safety and fairness evaluations before deployment.\n* Unintended Content and Bias: The model was trained on a large corpus of text data, which was not explicitly checked for offensive content or existing biases. Consequently, it may inadvertently produce content that reflects these biases or inaccuracies.\n* Toxicity: Despite efforts to select appropriate training data, the model is capable of generating harmful content, especially when prompted explicitly. We encourage the open-source community to engage in developing strategies to minimize such risks.",
"### Recommendations for Safe and Ethical Usage\n\n\n* Human Oversight: We recommend incorporating a human curation layer or using filters to manage and improve the quality of outputs, especially in public-facing applications. This approach can help mitigate the risk of generating objectionable content unexpectedly.\n* Application-Specific Testing: Developers intending to use Trendyol LLM should conduct thorough safety testing and optimization tailored to their specific applications. This is crucial, as the model’s responses can be unpredictable and may occasionally be biased, inaccurate, or offensive.\n* Responsible Development and Deployment: It is the responsibility of developers and users of Trendyol LLM to ensure its ethical and safe application. We urge users to be mindful of the model's limitations and to employ appropriate safeguards to prevent misuse or harmful consequences."
] | [
85,
191,
195,
205
] | [
"passage: TAGS\n#transformers #gguf #llama #text-generation #trendyol #llama-2 #turkish #tr #en #base_model-Trendyol/Trendyol-LLM-7b-chat-v0.1 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n### Limitations and Known Biases\n\n\n* Primary Function and Application: Trendyol LLM, an autoregressive language model, is primarily designed to predict the next token in a text string. While often used for various applications, it is important to note that it has not undergone extensive real-world application testing. Its effectiveness and reliability across diverse scenarios remain largely unverified.\n* Language Comprehension and Generation: The model is primarily trained in standard English and Turkish. Its performance in understanding and generating slang, informal language, or other languages may be limited, leading to potential errors or misinterpretations.\n* Generation of False Information: Users should be aware that Trendyol LLM may produce inaccurate or misleading information. Outputs should be considered as starting points or suggestions rather than definitive answers.### Risks and Ethical Considerations\n\n\n* Potential for Harmful Use: There is a risk that Trendyol LLM could be used to generate offensive or harmful language. We strongly discourage its use for any such purposes and emphasize the need for application-specific safety and fairness evaluations before deployment.\n* Unintended Content and Bias: The model was trained on a large corpus of text data, which was not explicitly checked for offensive content or existing biases. Consequently, it may inadvertently produce content that reflects these biases or inaccuracies.\n* Toxicity: Despite efforts to select appropriate training data, the model is capable of generating harmful content, especially when prompted explicitly. We encourage the open-source community to engage in developing strategies to minimize such risks."
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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. -->
# detr-resnet-50_finetuned_cppe5
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) 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: 6e-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: 10
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/detr-resnet-50", "model-index": [{"name": "detr-resnet-50_finetuned_cppe5", "results": []}]} | object-detection | binitt/detr-resnet-50_finetuned_cppe5 | [
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-12T08:27:55+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us
|
# detr-resnet-50_finetuned_cppe5
This model is a fine-tuned version of facebook/detr-resnet-50 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: 6e-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: 10
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.2
| [
"# detr-resnet-50_finetuned_cppe5\n\nThis model is a fine-tuned version of facebook/detr-resnet-50 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: 6e-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: 10",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu118\n- Datasets 2.17.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# detr-resnet-50_finetuned_cppe5\n\nThis model is a fine-tuned version of facebook/detr-resnet-50 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: 6e-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: 10",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu118\n- Datasets 2.17.0\n- Tokenizers 0.15.2"
] | [
62,
41,
6,
12,
8,
3,
90,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us \n# detr-resnet-50_finetuned_cppe5\n\nThis model is a fine-tuned version of facebook/detr-resnet-50 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: 6e-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: 10### Training results### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu118\n- Datasets 2.17.0\n- Tokenizers 0.15.2"
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) 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: 0.0004
- 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: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "model-index": [{"name": "results", "results": []}]} | null | Vidyuth/results | [
"tensorboard",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"region:us"
] | 2024-02-12T08:32:25+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #region-us
|
# results
This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 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: 0.0004
- 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: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.13.3
| [
"# results\n\nThis model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 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: 0.0004\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: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 4",
"### Training results",
"### Framework versions\n\n- Transformers 4.31.0\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.13.3"
] | [
"TAGS\n#tensorboard #generated_from_trainer #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #region-us \n",
"# results\n\nThis model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 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: 0.0004\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: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 4",
"### Training results",
"### Framework versions\n\n- Transformers 4.31.0\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.13.3"
] | [
47,
37,
6,
12,
8,
3,
106,
4,
33
] | [
"passage: TAGS\n#tensorboard #generated_from_trainer #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #region-us \n# results\n\nThis model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 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: 0.0004\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: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 4### Training results### Framework versions\n\n- Transformers 4.31.0\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.13.3"
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null | null | diffusers |
# Model Card for Model ID
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<!-- 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]
| {"library_name": "diffusers"} | null | krnl/test_0_db_img2img | [
"diffusers",
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|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- 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
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "beomi/polyglot-ko-12.8b-safetensors"} | null | kasiwoos/polyglot-ko-12.8b-10000step | [
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#peft #safetensors #arxiv-1910.09700 #base_model-beomi/polyglot-ko-12.8b-safetensors #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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- Model type:
- Language(s) (NLP):
- License:
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### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
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[optional]
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APA:
## Glossary [optional]
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null | null | diffusers |
# SDXL LoRA DreamBooth - jcjo/cat3
<Gallery />
## Model description
These are jcjo/cat3 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of yeonpickPJY grey and green eye cat to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](jcjo/cat3/tree/main) them in the Files & versions tab.
| {"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of yeonpickPJY grey and green eye cat"} | text-to-image | jcjo/cat3 | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"has_space",
"region:us"
] | 2024-02-12T08:41:07+00:00 | [] | [] | TAGS
#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us
|
# SDXL LoRA DreamBooth - jcjo/cat3
<Gallery />
## Model description
These are jcjo/cat3 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using DreamBooth.
LoRA for the text encoder was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of yeonpickPJY grey and green eye cat to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
| [
"# SDXL LoRA DreamBooth - jcjo/cat3\n\n<Gallery />",
"## Model description\n\nThese are jcjo/cat3 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: True.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.",
"## Trigger words\n\nYou should use a photo of yeonpickPJY grey and green eye cat to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n",
"# SDXL LoRA DreamBooth - jcjo/cat3\n\n<Gallery />",
"## Model description\n\nThese are jcjo/cat3 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: True.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.",
"## Trigger words\n\nYou should use a photo of yeonpickPJY grey and green eye cat to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
82,
20,
84,
28,
28
] | [
"passage: TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n# SDXL LoRA DreamBooth - jcjo/cat3\n\n<Gallery />## Model description\n\nThese are jcjo/cat3 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: True.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.## Trigger words\n\nYou should use a photo of yeonpickPJY grey and green eye cat to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | nakcnx/SeaLLM-2-7b-AWQ | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2024-02-12T08:42:28+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# Model Card for Model ID
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## Uses
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### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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- Training regime:
#### Speeds, Sizes, Times [optional]
## 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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"## 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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"passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #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 |
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` | {"license": "other", "tags": ["autotrain", "text-generation"], "widget": [{"text": "I love AutoTrain because "}]} | text-generation | ElderlyDed/LadnoAgas | [
"safetensors",
"autotrain",
"text-generation",
"license:other",
"endpoints_compatible",
"region:us"
] | 2024-02-12T08:42:58+00:00 | [] | [] | TAGS
#safetensors #autotrain #text-generation #license-other #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit AutoTrain.
# Usage
| [
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
"TAGS\n#safetensors #autotrain #text-generation #license-other #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
33,
29,
3
] | [
"passage: TAGS\n#safetensors #autotrain #text-generation #license-other #endpoints_compatible #region-us \n# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.# Usage"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | token-classification | SKNahin/NER_TinyBert | [
"transformers",
"safetensors",
"bert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T08:44:55+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #token-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
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## Uses
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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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## Evaluation
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## Environmental Impact
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- Hardware Type:
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[optional]
BibTeX:
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## Glossary [optional]
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"passage: TAGS\n#transformers #safetensors #bert #token-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | 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. -->
# clip-zabir-3
This model is a fine-tuned version of [openai/clip-vit-base-patch16](https://huggingface.co/openai/clip-vit-base-patch16) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4039
## 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: 5.0
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cpu
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "base_model": "openai/clip-vit-base-patch16", "model-index": [{"name": "clip-zabir-3", "results": []}]} | zero-shot-image-classification | zabir735/clip-zabir-3 | [
"transformers",
"safetensors",
"clip",
"zero-shot-image-classification",
"generated_from_trainer",
"base_model:openai/clip-vit-base-patch16",
"endpoints_compatible",
"region:us"
] | 2024-02-12T08:50:10+00:00 | [] | [] | TAGS
#transformers #safetensors #clip #zero-shot-image-classification #generated_from_trainer #base_model-openai/clip-vit-base-patch16 #endpoints_compatible #region-us
|
# clip-zabir-3
This model is a fine-tuned version of openai/clip-vit-base-patch16 on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4039
## 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: 5.0
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cpu
- Datasets 2.16.1
- Tokenizers 0.15.1
| [
"# clip-zabir-3\n\nThis model is a fine-tuned version of openai/clip-vit-base-patch16 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.4039",
"## 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: 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: 5.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cpu\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #clip #zero-shot-image-classification #generated_from_trainer #base_model-openai/clip-vit-base-patch16 #endpoints_compatible #region-us \n",
"# clip-zabir-3\n\nThis model is a fine-tuned version of openai/clip-vit-base-patch16 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.4039",
"## 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: 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: 5.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cpu\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
57,
54,
6,
12,
8,
3,
90,
4,
38
] | [
"passage: TAGS\n#transformers #safetensors #clip #zero-shot-image-classification #generated_from_trainer #base_model-openai/clip-vit-base-patch16 #endpoints_compatible #region-us \n# clip-zabir-3\n\nThis model is a fine-tuned version of openai/clip-vit-base-patch16 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.4039## 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: 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: 5.0### Training results### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cpu\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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null | null | mlx |
# mlx-community/Kukedlic-NeuTrixOmniBe-DPO-q
This model was converted to MLX format from [`Kukedlc/NeuTrixOmniBe-DPO`]().
Refer to the [original model card](https://huggingface.co/Kukedlc/NeuTrixOmniBe-DPO) 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/Kukedlic-NeuTrixOmniBe-DPO-q")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"license": "apache-2.0", "tags": ["merge", "mergekit", "#dpo", "MaximeLabonne", "#mergeofmerge", "mlx"], "base_model": ["CultriX/NeuralTrix-7B-dpo", "paulml/OmniBeagleSquaredMBX-v3-7B-v2"]} | null | mlx-community/Kukedlic-NeuTrixOmniBe-DPO-q | [
"mlx",
"safetensors",
"mistral",
"merge",
"mergekit",
"#dpo",
"MaximeLabonne",
"#mergeofmerge",
"base_model:CultriX/NeuralTrix-7B-dpo",
"base_model:paulml/OmniBeagleSquaredMBX-v3-7B-v2",
"license:apache-2.0",
"region:us"
] | 2024-02-12T08:50:21+00:00 | [] | [] | TAGS
#mlx #safetensors #mistral #merge #mergekit ##dpo #MaximeLabonne ##mergeofmerge #base_model-CultriX/NeuralTrix-7B-dpo #base_model-paulml/OmniBeagleSquaredMBX-v3-7B-v2 #license-apache-2.0 #region-us
|
# mlx-community/Kukedlic-NeuTrixOmniBe-DPO-q
This model was converted to MLX format from ['Kukedlc/NeuTrixOmniBe-DPO']().
Refer to the original model card for more details on the model.
## Use with mlx
| [
"# mlx-community/Kukedlic-NeuTrixOmniBe-DPO-q\nThis model was converted to MLX format from ['Kukedlc/NeuTrixOmniBe-DPO']().\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
"TAGS\n#mlx #safetensors #mistral #merge #mergekit ##dpo #MaximeLabonne ##mergeofmerge #base_model-CultriX/NeuralTrix-7B-dpo #base_model-paulml/OmniBeagleSquaredMBX-v3-7B-v2 #license-apache-2.0 #region-us \n",
"# mlx-community/Kukedlic-NeuTrixOmniBe-DPO-q\nThis model was converted to MLX format from ['Kukedlc/NeuTrixOmniBe-DPO']().\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
91,
67,
5
] | [
"passage: TAGS\n#mlx #safetensors #mistral #merge #mergekit ##dpo #MaximeLabonne ##mergeofmerge #base_model-CultriX/NeuralTrix-7B-dpo #base_model-paulml/OmniBeagleSquaredMBX-v3-7B-v2 #license-apache-2.0 #region-us \n# mlx-community/Kukedlic-NeuTrixOmniBe-DPO-q\nThis model was converted to MLX format from ['Kukedlc/NeuTrixOmniBe-DPO']().\nRefer to the original model card for more details on the model.## Use with mlx"
] | [
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] |
null | null | transformers |
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` | {"license": "other", "tags": ["autotrain", "text-generation"], "widget": [{"text": "I love AutoTrain because "}]} | text-generation | ElderlyDed/LadnoMis | [
"transformers",
"pytorch",
"safetensors",
"mistral",
"text-generation",
"autotrain",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T08:56:12+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #mistral #text-generation #autotrain #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit AutoTrain.
# Usage
| [
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
"TAGS\n#transformers #pytorch #safetensors #mistral #text-generation #autotrain #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
64,
29,
3
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #mistral #text-generation #autotrain #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.# Usage"
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null | null | transformers |
<!-- 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. -->
# aeaee/my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9576
- Validation Loss: 0.8521
- Train Accuracy: 0.6033
- Epoch: 0
## 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 375, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.9576 | 0.8521 | 0.6033 | 0 |
### Framework versions
- Transformers 4.37.2
- TensorFlow 2.15.0
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "aeaee/my_awesome_model", "results": []}]} | text-classification | aeaee/my_awesome_model | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T08:59:02+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| aeaee/my\_awesome\_model
========================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.9576
* Validation Loss: 0.8521
* Train Accuracy: 0.6033
* Epoch: 0
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': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': False, 'is\_legacy\_optimizer': False, 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 375, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32
### Training results
### Framework versions
* Transformers 4.37.2
* 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': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': False, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 375, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #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* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': False, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 375, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
70,
304,
4,
31
] | [
"passage: TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #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* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': False, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 375, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# new-dot-comp-v2
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) 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: 12
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- 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": "apache-2.0", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "model-index": [{"name": "new-dot-comp-v2", "results": []}]} | null | newbie-geek/new-dot-comp-v2 | [
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"region:us"
] | 2024-02-12T09:01:03+00:00 | [] | [] | TAGS
#tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #region-us
|
# new-dot-comp-v2
This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 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: 12
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- 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
| [
"# new-dot-comp-v2\n\nThis model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 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: 12\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\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#tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #region-us \n",
"# new-dot-comp-v2\n\nThis model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 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: 12\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\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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"passage: TAGS\n#tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #region-us \n# new-dot-comp-v2\n\nThis model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 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: 12\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\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 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Image-Arousal
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 custom dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8522
- Accuracy: 0.6294
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9023 | 0.78 | 100 | 0.8522 | 0.6294 |
| 0.5376 | 1.56 | 200 | 0.8592 | 0.6686 |
| 0.2473 | 2.34 | 300 | 0.9559 | 0.6510 |
| 0.0691 | 3.12 | 400 | 1.1399 | 0.6275 |
| 0.0821 | 3.91 | 500 | 1.2060 | 0.6392 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "vit-Arousal", "results": []}]} | image-classification | SeyedAli/Image-Arousal | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T09:10:19+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Image-Arousal
=============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the custom dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8522
* Accuracy: 0.6294
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 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
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"### Training results",
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"### 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 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
75,
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"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #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: 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 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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] |
null | null | transformers | Everyone-LLM-7b-Base
EveryoneLLM series of models made by the community, for the community.
This is the first version of Everyone-LLM, a model that combines the power of the large majority of powerfull fine-tuned LLM's made by the community, to create a vast and knowledgable LLM with various abilities.
Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
The models that were used in this merger were as follow:
- https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo
- https://huggingface.co/jondurbin/bagel-dpo-7b-v0.4
- https://huggingface.co/Locutusque/Hercules-2.0-Mistral-7B
- https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca
- https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B
- https://huggingface.co/NousResearch/Nous-Capybara-7B-V1.9
- https://huggingface.co/Intel/neural-chat-7b-v3-3
- https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2
- https://huggingface.co/senseable/WestLake-7B-v2
- https://huggingface.co/defog/sqlcoder-7b
- https://huggingface.co/meta-math/MetaMath-Mistral-7B
- https://huggingface.co/nextai-team/apollo-v1-7b
- https://huggingface.co/WizardLM/WizardMath-7B-V1.1
- https://huggingface.co/openchat/openchat-3.5-0106
- https://huggingface.co/mistralai/Mistral-7B-v0.1
Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. 💗
You can find the write up for merging models here:
https://docs.google.com/document/d/1_vOftBnrk9NRk5h10UqrfJ5CDih9KBKL61yvrZtVWPE/edit?usp=sharing
# Open LLM Leaderboard Scores
```
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|------------------------------------|---------|---------|-----------|---------|------------|------------|---------|
| rombodawg/Everyone-LLM-7b-Base | 70.21 | 66.38 | 86.02 | 64.94 | 57.89 | 80.43 | 65.58 |
```
Config for the merger can be found bellow:
```yaml
models:
- model: cognitivecomputations_dolphin-2.6-mistral-7b-dpo
parameters:
weight: 1
- model: jondurbin_bagel-dpo-7b-v0.4
parameters:
weight: 1
- model: Locutusque_Hercules-2.0-Mistral-7B
parameters:
weight: 1
- model: Open-Orca_Mistral-7B-OpenOrca
parameters:
weight: 1
- model: teknium_OpenHermes-2.5-Mistral-7B
parameters:
weight: 1
- model: NousResearch_Nous-Capybara-7B-V1.9
parameters:
weight: 1
- model: Intel_neural-chat-7b-v3-3
parameters:
weight: 1
- model: mistralai_Mistral-7B-Instruct-v0.2
parameters:
weight: 1
- model: senseable_WestLake-7B-v2
parameters:
weight: 1
- model: defog_sqlcoder-7b
parameters:
weight: 1
- model: meta-math_MetaMath-Mistral-7B
parameters:
weight: 1
- model: nextai-team_apollo-v1-7b
parameters:
weight: 1
- model: WizardLM_WizardMath-7B-V1.1
parameters:
weight: 1
- model: openchat_openchat-3.5-0106
parameters:
weight: 1
merge_method: task_arithmetic
base_model: mistralai_Mistral-7B-v0.1
parameters:
normalize: true
int8_mask: true
dtype: float16
```
| {"license": "unknown", "tags": ["merge"]} | text-generation | rombodawg/Everyone-LLM-7b-Base | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T09:17:03+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #license-unknown #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Everyone-LLM-7b-Base
!image/jpeg
EveryoneLLM series of models made by the community, for the community.
This is the first version of Everyone-LLM, a model that combines the power of the large majority of powerfull fine-tuned LLM's made by the community, to create a vast and knowledgable LLM with various abilities.
Prompt template: Alpaca
The models that were used in this merger were as follow:
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community.
You can find the write up for merging models here:
URL
# Open LLM Leaderboard Scores
Config for the merger can be found bellow:
| [
"# Open LLM Leaderboard Scores\n\n\nConfig for the merger can be found bellow:"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #license-unknown #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Open LLM Leaderboard Scores\n\n\nConfig for the merger can be found bellow:"
] | [
57,
20
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #license-unknown #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Open LLM Leaderboard Scores\n\n\nConfig for the merger can be found bellow:"
] | [
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] |
null | null | null | Everyone-LLM-7b-Base-GGUF
EveryoneLLM series of models made by the community, for the community.
This is the first version of Everyone-LLM, a model that combines the power of the large majority of powerfull fine-tuned LLM's made by the community, to create a vast and knowledgable LLM with various abilities.
Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
The models that were used in this merger were as follow:
- https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo
- https://huggingface.co/jondurbin/bagel-dpo-7b-v0.4
- https://huggingface.co/Locutusque/Hercules-2.0-Mistral-7B
- https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca
- https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B
- https://huggingface.co/NousResearch/Nous-Capybara-7B-V1.9
- https://huggingface.co/Intel/neural-chat-7b-v3-3
- https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2
- https://huggingface.co/senseable/WestLake-7B-v2
- https://huggingface.co/defog/sqlcoder-7b
- https://huggingface.co/meta-math/MetaMath-Mistral-7B
- https://huggingface.co/nextai-team/apollo-v1-7b
- https://huggingface.co/WizardLM/WizardMath-7B-V1.1
- https://huggingface.co/openchat/openchat-3.5-0106
- https://huggingface.co/mistralai/Mistral-7B-v0.1
Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. 💗
You can find the write up for merging models here:
https://docs.google.com/document/d/1_vOftBnrk9NRk5h10UqrfJ5CDih9KBKL61yvrZtVWPE/edit?usp=sharing
# Open LLM Leaderboard Scores
```
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|------------------------------------|---------|---------|-----------|---------|------------|------------|---------|
| rombodawg/Everyone-LLM-7b-Base | 70.21 | 66.38 | 86.02 | 64.94 | 57.89 | 80.43 | 65.58 |
```
Config for the merger can be found bellow:
```yaml
models:
- model: cognitivecomputations_dolphin-2.6-mistral-7b-dpo
parameters:
weight: 1
- model: jondurbin_bagel-dpo-7b-v0.4
parameters:
weight: 1
- model: Locutusque_Hercules-2.0-Mistral-7B
parameters:
weight: 1
- model: Open-Orca_Mistral-7B-OpenOrca
parameters:
weight: 1
- model: teknium_OpenHermes-2.5-Mistral-7B
parameters:
weight: 1
- model: NousResearch_Nous-Capybara-7B-V1.9
parameters:
weight: 1
- model: Intel_neural-chat-7b-v3-3
parameters:
weight: 1
- model: mistralai_Mistral-7B-Instruct-v0.2
parameters:
weight: 1
- model: senseable_WestLake-7B-v2
parameters:
weight: 1
- model: defog_sqlcoder-7b
parameters:
weight: 1
- model: meta-math_MetaMath-Mistral-7B
parameters:
weight: 1
- model: nextai-team_apollo-v1-7b
parameters:
weight: 1
- model: WizardLM_WizardMath-7B-V1.1
parameters:
weight: 1
- model: openchat_openchat-3.5-0106
parameters:
weight: 1
merge_method: task_arithmetic
base_model: mistralai_Mistral-7B-v0.1
parameters:
normalize: true
int8_mask: true
dtype: float16
```
| {"license": "unknown", "tags": ["merge"]} | null | rombodawg/Everyone-LLM-7b-Base-GGUF | [
"gguf",
"merge",
"license:unknown",
"region:us"
] | 2024-02-12T09:17:25+00:00 | [] | [] | TAGS
#gguf #merge #license-unknown #region-us
| Everyone-LLM-7b-Base-GGUF
!image/jpeg
EveryoneLLM series of models made by the community, for the community.
This is the first version of Everyone-LLM, a model that combines the power of the large majority of powerfull fine-tuned LLM's made by the community, to create a vast and knowledgable LLM with various abilities.
Prompt template: Alpaca
The models that were used in this merger were as follow:
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community.
You can find the write up for merging models here:
URL
# Open LLM Leaderboard Scores
Config for the merger can be found bellow:
| [
"# Open LLM Leaderboard Scores\n\n\nConfig for the merger can be found bellow:"
] | [
"TAGS\n#gguf #merge #license-unknown #region-us \n",
"# Open LLM Leaderboard Scores\n\n\nConfig for the merger can be found bellow:"
] | [
19,
20
] | [
"passage: TAGS\n#gguf #merge #license-unknown #region-us \n# Open LLM Leaderboard Scores\n\n\nConfig for the merger can be found bellow:"
] | [
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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 small - Devansh Jain
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4291
- Wer: 33.3446
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0825 | 2.44 | 1000 | 0.2949 | 35.1943 |
| 0.0201 | 4.89 | 2000 | 0.3459 | 33.6578 |
| 0.0017 | 7.33 | 3000 | 0.4066 | 33.2430 |
| 0.0005 | 9.78 | 4000 | 0.4291 | 33.3446 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"language": ["hi"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_11_0"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper small - Devansh Jain", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 11.0", "type": "mozilla-foundation/common_voice_11_0", "config": "hi", "split": "None", "args": "config: hi, split: test"}, "metrics": [{"type": "wer", "value": 33.344620333530855, "name": "Wer"}]}]}]} | automatic-speech-recognition | Devanshj7/whisper-small-2 | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"hi",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-02-12T09:20:02+00:00 | [] | [
"hi"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hi #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Whisper small - Devansh Jain
============================
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4291
* Wer: 33.3446
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 4000
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hi #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-small #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: 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* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
104,
130,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hi #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-small #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: 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* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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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. -->
# Whisper-Small-Tamil1
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0291
- Wer: 17.0460
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 10
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Step | Validation Loss | Wer |
|:----:|:---------------:|:-------:|
| 1000 | 0.4926 | 82.9005 |
| 2000 | 0.2221 | 68.0131 |
| 3000 | 0.0344 | 46.8331 |
| 4000 | 0.1713 | 38.5561 |
| 5000 | 0.0291 | 17.0460 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
| {"language": ["ta"], "license": "apache-2.0", "tags": ["Tamil-ASR", "generated_from_trainer"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper-Small-Tamil1", "results": []}]} | automatic-speech-recognition | sujith013/whisper-small-tamil1 | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"Tamil-ASR",
"generated_from_trainer",
"ta",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-12T09:20:35+00:00 | [] | [
"ta"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #Tamil-ASR #generated_from_trainer #ta #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us
| Whisper-Small-Tamil1
====================
This model is a fine-tuned version of openai/whisper-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0291
* Wer: 17.0460
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* 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: 10
* training\_steps: 1000
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\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: 10\n* training\\_steps: 1000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\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 #whisper #automatic-speech-recognition #Tamil-ASR #generated_from_trainer #ta #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\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: 10\n* training\\_steps: 1000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.0.0\n* Datasets 2.1.0\n* Tokenizers 0.14.1"
] | [
76,
158,
4,
30
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #Tamil-ASR #generated_from_trainer #ta #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\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: 10\n* training\\_steps: 1000\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\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 | null | pip install -q transformers einops accelerate langchain bitsandbytes sentence_transformers llama-index pypdf python-dotenv
| {} | null | Abhishek52/Invoice | [
"region:us"
] | 2024-02-12T09:22:44+00:00 | [] | [] | TAGS
#region-us
| pip install -q transformers einops accelerate langchain bitsandbytes sentence_transformers llama-index pypdf python-dotenv
| [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
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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 | dict(
name="igenius-llama-tinystories-2B", # 2B run on AZURE
hf_config=dict(org="Igenius", name="igenius-llama-tinystories-az"),
block_size=2048,
vocab_size=32_000,
padding_multiple=64,
n_layer=16,
n_head=4 * 16,
n_embd=192 * 16,
rotary_percentage=1.0,
parallel_residual=False,
bias=False,
_norm_class="RMSNorm",
norm_eps=1e-5,
_mlp_class="LLaMAMLP", ##with LLaMAMLP 2B
intermediate_size=8192,
n_query_groups=4 * 16,
),
Trained on Wiki, Editorial, Educational, Finance corpuses for about 1 epoch | {} | text-generation | iGenius-AI-Team/Italia-2Bts-ckpt-47B-wikiEdiEduFi | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T09:27:10+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| dict(
name="igenius-llama-tinystories-2B", # 2B run on AZURE
hf_config=dict(org="Igenius", name="igenius-llama-tinystories-az"),
block_size=2048,
vocab_size=32_000,
padding_multiple=64,
n_layer=16,
n_head=4 * 16,
n_embd=192 * 16,
rotary_percentage=1.0,
parallel_residual=False,
bias=False,
_norm_class="RMSNorm",
norm_eps=1e-5,
_mlp_class="LLaMAMLP", ##with LLaMAMLP 2B
intermediate_size=8192,
n_query_groups=4 * 16,
),
Trained on Wiki, Editorial, Educational, Finance corpuses for about 1 epoch | [
"# 2B run on AZURE\n hf_config=dict(org=\"Igenius\", name=\"igenius-llama-tinystories-az\"),\n block_size=2048,\n vocab_size=32_000,\n padding_multiple=64,\n n_layer=16,\n n_head=4 * 16,\n n_embd=192 * 16,\n rotary_percentage=1.0,\n parallel_residual=False,\n bias=False,\n _norm_class=\"RMSNorm\",\n norm_eps=1e-5,\n _mlp_class=\"LLaMAMLP\", ##with LLaMAMLP 2B\n intermediate_size=8192,\n n_query_groups=4 * 16,\n ),\n\n Trained on Wiki, Editorial, Educational, Finance corpuses for about 1 epoch"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# 2B run on AZURE\n hf_config=dict(org=\"Igenius\", name=\"igenius-llama-tinystories-az\"),\n block_size=2048,\n vocab_size=32_000,\n padding_multiple=64,\n n_layer=16,\n n_head=4 * 16,\n n_embd=192 * 16,\n rotary_percentage=1.0,\n parallel_residual=False,\n bias=False,\n _norm_class=\"RMSNorm\",\n norm_eps=1e-5,\n _mlp_class=\"LLaMAMLP\", ##with LLaMAMLP 2B\n intermediate_size=8192,\n n_query_groups=4 * 16,\n ),\n\n Trained on Wiki, Editorial, Educational, Finance corpuses for about 1 epoch"
] | [
47,
182
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# 2B run on AZURE\n hf_config=dict(org=\"Igenius\", name=\"igenius-llama-tinystories-az\"),\n block_size=2048,\n vocab_size=32_000,\n padding_multiple=64,\n n_layer=16,\n n_head=4 * 16,\n n_embd=192 * 16,\n rotary_percentage=1.0,\n parallel_residual=False,\n bias=False,\n _norm_class=\"RMSNorm\",\n norm_eps=1e-5,\n _mlp_class=\"LLaMAMLP\", ##with LLaMAMLP 2B\n intermediate_size=8192,\n n_query_groups=4 * 16,\n ),\n\n Trained on Wiki, Editorial, Educational, Finance corpuses for about 1 epoch"
] | [
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] |
null | null | transformers |
chinese-roberta-wwm-ext-large from https://huggingface.co/lj1995/GPT-SoVITS
pretrained models used in https://github.com/shibing624/parrots | {"language": ["zh"], "license": "apache-2.0", "pipeline_tag": "text-to-speech"} | text-to-speech | shibing624/parrots-chinese-roberta-wwm-ext-large | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"text-to-speech",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T09:30:57+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #fill-mask #text-to-speech #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
chinese-roberta-wwm-ext-large from URL
pretrained models used in URL | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #text-to-speech #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
53
] | [
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #text-to-speech #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | mertllc/mms-tts-tur-inkilap_modified | [
"transformers",
"safetensors",
"vits",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #vits #arxiv-1910.09700 #endpoints_compatible #region-us
|
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"passage: TAGS\n#transformers #safetensors #vits #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 | 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. -->
# codet5-small-v26
This model is a fine-tuned version of [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1762
- Bleu Score: 0.0007
- Gen Len: 14.3657
## Model description
Trained,
- on: chathuranga-jayanath/context-5-finmath-times4j-html-mavendoxia-wro4j-guava-supercsv-balanced-10k-prompt-1
## 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: 30
- eval_batch_size: 30
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu Score | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:----------:|:-------:|
| 0.2776 | 1.0 | 3407 | 0.2137 | 0.0007 | 14.2809 |
| 0.2216 | 2.0 | 6814 | 0.1836 | 0.0007 | 14.3813 |
| 0.2036 | 3.0 | 10221 | 0.1762 | 0.0007 | 14.3657 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "Salesforce/codet5-small", "model-index": [{"name": "codet5-small-v26", "results": []}]} | text2text-generation | chathuranga-jayanath/codet5-small-v26 | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:Salesforce/codet5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T09:40:31+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-Salesforce/codet5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| codet5-small-v26
================
This model is a fine-tuned version of Salesforce/codet5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1762
* Bleu Score: 0.0007
* Gen Len: 14.3657
Model description
-----------------
Trained,
* on: chathuranga-jayanath/context-5-finmath-times4j-html-mavendoxia-wro4j-guava-supercsv-balanced-10k-prompt-1
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: 30
* eval\_batch\_size: 30
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.38.0.dev0
* 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 4.38.0.dev0\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: 30\n* eval\\_batch\\_size: 30\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
82,
113,
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"passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-Salesforce/codet5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 30\n* eval\\_batch\\_size: 30\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\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. -->
# roberta-base-bne-finetuned-detests-wandb24
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3730
- Accuracy: 0.8592
- F1-score: 0.7922
- Precision: 0.8046
- Recall: 0.7820
- Auc: 0.7820
## 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: 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|:------:|
| 0.344 | 1.0 | 77 | 0.3268 | 0.8642 | 0.7814 | 0.8347 | 0.7522 | 0.7522 |
| 0.1996 | 2.0 | 154 | 0.3730 | 0.8592 | 0.7922 | 0.8046 | 0.7820 | 0.7820 |
### 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"], "metrics": ["accuracy", "precision", "recall"], "base_model": "BSC-TeMU/roberta-base-bne", "model-index": [{"name": "roberta-base-bne-finetuned-detests-wandb24", "results": []}]} | text-classification | Pablo94/roberta-base-bne-finetuned-detests-wandb24 | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:BSC-TeMU/roberta-base-bne",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T09:42:44+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-BSC-TeMU/roberta-base-bne #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-bne-finetuned-detests-wandb24
==========================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3730
* Accuracy: 0.8592
* F1-score: 0.7922
* Precision: 0.8046
* Recall: 0.7820
* Auc: 0.7820
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: 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: 2
### 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: 5e-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: 2",
"### 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: 5e-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: 2",
"### 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"
] | [
75,
98,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-BSC-TeMU/roberta-base-bne #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: 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: 2### 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 | ml-agents |
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: wahdan99/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos"]} | reinforcement-learning | wahdan99/poca-SoccerTwos | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | 2024-02-12T09:44:17+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us
|
# poca Agent playing SoccerTwos
This is a trained model of a poca agent playing SoccerTwos
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your
browser: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### Watch your Agent play
You can watch your agent playing directly in your browser
1. If the environment is part of ML-Agents official environments, go to URL
2. Step 1: Find your model_id: wahdan99/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: wahdan99/poca-SoccerTwos\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us \n",
"# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: wahdan99/poca-SoccerTwos\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
52,
205
] | [
"passage: TAGS\n#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us \n# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: wahdan99/poca-SoccerTwos\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | devscion/T2IPK | [
"transformers",
"safetensors",
"git",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #git #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
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## Uses
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### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
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- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
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#### 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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[optional]
BibTeX:
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## Glossary [optional]
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## Model Card Contact
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"passage: TAGS\n#transformers #safetensors #git #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 2
### 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"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "my_awesome_model", "results": []}]} | text-classification | SayeghJS/my_awesome_model | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T09:46:04+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
|
# my_awesome_model
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 2
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# my_awesome_model\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 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: 2",
"### 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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"# my_awesome_model\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 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: 2",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
72,
35,
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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# my_awesome_model\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 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: 2### 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 |
# Mistral-Instruct-Ukrainian-slerp
Mistral-Instruct-Ukrainian-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO](https://huggingface.co/Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Radu1999/Mistral-Instruct-Ukrainian-slerp"
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.bfloat16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "mistralai/Mistral-7B-Instruct-v0.2", "Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO"], "base_model": ["mistralai/Mistral-7B-Instruct-v0.2", "Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO"]} | text-generation | Radu1999/Mistral-Instruct-Ukrainian-slerp | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO",
"conversational",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T09:46:49+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mistralai/Mistral-7B-Instruct-v0.2 #Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO #conversational #base_model-mistralai/Mistral-7B-Instruct-v0.2 #base_model-Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Mistral-Instruct-Ukrainian-slerp
Mistral-Instruct-Ukrainian-slerp is a merge of the following models using LazyMergekit:
* mistralai/Mistral-7B-Instruct-v0.2
* Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO
## Configuration
## Usage
| [
"# Mistral-Instruct-Ukrainian-slerp\n\nMistral-Instruct-Ukrainian-slerp is a merge of the following models using LazyMergekit:\n* mistralai/Mistral-7B-Instruct-v0.2\n* Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mistralai/Mistral-7B-Instruct-v0.2 #Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO #conversational #base_model-mistralai/Mistral-7B-Instruct-v0.2 #base_model-Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Mistral-Instruct-Ukrainian-slerp\n\nMistral-Instruct-Ukrainian-slerp is a merge of the following models using LazyMergekit:\n* mistralai/Mistral-7B-Instruct-v0.2\n* Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO",
"## Configuration",
"## Usage"
] | [
152,
76,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mistralai/Mistral-7B-Instruct-v0.2 #Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO #conversational #base_model-mistralai/Mistral-7B-Instruct-v0.2 #base_model-Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Mistral-Instruct-Ukrainian-slerp\n\nMistral-Instruct-Ukrainian-slerp is a merge of the following models using LazyMergekit:\n* mistralai/Mistral-7B-Instruct-v0.2\n* Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO## Configuration## Usage"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# racism-finetuned-detests-wandb24
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: 0.3916
- Accuracy: 0.8380
- F1-score: 0.7712
- Precision: 0.7692
- Recall: 0.7733
- Auc: 0.7733
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|:------:|
| 0.357 | 1.0 | 39 | 0.3343 | 0.8576 | 0.7614 | 0.8374 | 0.7277 | 0.7277 |
| 0.1109 | 2.0 | 78 | 0.3916 | 0.8380 | 0.7712 | 0.7692 | 0.7733 | 0.7733 |
### 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-detests-wandb24", "results": []}]} | text-classification | Pablo94/racism-finetuned-detests-wandb24 | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:davidmasip/racism",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T09:48:15+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-detests-wandb24
================================
This model is a fine-tuned version of davidmasip/racism on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3916
* Accuracy: 0.8380
* F1-score: 0.7712
* Precision: 0.7692
* Recall: 0.7733
* Auc: 0.7733
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* 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 4.37.2
* 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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"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: 3e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### 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. -->
# DH_ONM_WORDS
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0021
- Accuracy: 0.9998
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: tpu
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0251 | 1.0 | 263 | 0.0150 | 0.9993 |
| 0.0085 | 2.0 | 527 | 0.0045 | 0.9997 |
| 0.0026 | 3.0 | 791 | 0.0029 | 0.9998 |
| 0.0018 | 4.0 | 1055 | 0.0023 | 0.9998 |
| 0.0019 | 4.99 | 1315 | 0.0021 | 0.9998 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["audiofolder"], "metrics": ["accuracy"], "base_model": "ntu-spml/distilhubert", "model-index": [{"name": "DH_ONM_WORDS", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "audiofolder", "type": "audiofolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.999762962962963, "name": "Accuracy"}]}]}]} | audio-classification | iamhack/DH_ONM_WORDS | [
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:audiofolder",
"base_model:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-02-12T09:48:56+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #hubert #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-ntu-spml/distilhubert #license-apache-2.0 #model-index #endpoints_compatible #region-us
| DH\_ONM\_WORDS
==============
This model is a fine-tuned version of ntu-spml/distilhubert on the audiofolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0021
* Accuracy: 0.9998
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* distributed\_type: tpu
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.0.0+cu118
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* distributed\\_type: tpu\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu118\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #hubert #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-ntu-spml/distilhubert #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: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* distributed\\_type: tpu\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu118\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
76,
153,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #hubert #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-ntu-spml/distilhubert #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: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* distributed\\_type: tpu\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu118\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 Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# SayeghJS/my_awesome_model2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0723
- Validation Loss: 0.2143
- Train Accuracy: 0.9258
- 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.2584 | 0.1973 | 0.9243 | 0 |
| 0.1387 | 0.2056 | 0.9270 | 1 |
| 0.0723 | 0.2143 | 0.9258 | 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": "distilbert-base-uncased", "model-index": [{"name": "SayeghJS/my_awesome_model2", "results": []}]} | text-classification | SayeghJS/my_awesome_model2 | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T09:50:00+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| SayeghJS/my\_awesome\_model2
============================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0723
* Validation Loss: 0.2143
* Train Accuracy: 0.9258
* 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': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 7810, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32
### 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': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 7810, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### 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 #distilbert #text-classification #generated_from_keras_callback #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* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 7810, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### 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"
] | [
70,
304,
4,
31
] | [
"passage: TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #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* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 7810, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32### 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 |
# SOLAR-10.7B
### Model Details
- Base Model: [yanolja/KoSOLAR-10.7B-v0.2](https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.2)
### Datasets
- sampling and translate [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca)
- sampling and translate [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf)
- translate [GAIR/lima](https://huggingface.co/datasets/GAIR/lima)
- [jojo0217/korean_rlhf_dataset](https://huggingface.co/datasets/jojo0217/korean_rlhf_dataset)
### Benchmark | {"language": ["ko"], "license": "cc-by-nc-4.0", "tags": ["SOLAR-10.7B"], "pipeline_tag": "text-generation"} | text-generation | ONS-AI-RESEARCH/ONS-SOLAR-10.7B-v1.1 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"SOLAR-10.7B",
"conversational",
"ko",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T09:50:43+00:00 | [] | [
"ko"
] | TAGS
#transformers #safetensors #llama #text-generation #SOLAR-10.7B #conversational #ko #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# SOLAR-10.7B
### Model Details
- Base Model: yanolja/KoSOLAR-10.7B-v0.2
### Datasets
- sampling and translate Open-Orca/SlimOrca
- sampling and translate Anthropic/hh-rlhf
- translate GAIR/lima
- jojo0217/korean_rlhf_dataset
### Benchmark | [
"# SOLAR-10.7B",
"### Model Details\n- Base Model: yanolja/KoSOLAR-10.7B-v0.2",
"### Datasets\n- sampling and translate Open-Orca/SlimOrca\n- sampling and translate Anthropic/hh-rlhf\n- translate GAIR/lima\n- jojo0217/korean_rlhf_dataset",
"### Benchmark"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #SOLAR-10.7B #conversational #ko #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# SOLAR-10.7B",
"### Model Details\n- Base Model: yanolja/KoSOLAR-10.7B-v0.2",
"### Datasets\n- sampling and translate Open-Orca/SlimOrca\n- sampling and translate Anthropic/hh-rlhf\n- translate GAIR/lima\n- jojo0217/korean_rlhf_dataset",
"### Benchmark"
] | [
71,
7,
21,
59,
5
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #SOLAR-10.7B #conversational #ko #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# SOLAR-10.7B### Model Details\n- Base Model: yanolja/KoSOLAR-10.7B-v0.2### Datasets\n- sampling and translate Open-Orca/SlimOrca\n- sampling and translate Anthropic/hh-rlhf\n- translate GAIR/lima\n- jojo0217/korean_rlhf_dataset### Benchmark"
] | [
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null | null | peft |
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
### Framework versions
- PEFT 0.8.2 | {"license": "apache-2.0", "library_name": "peft", "title": "Malawi", "emoji": "\ud83e\udd80", "colorFrom": "blue", "colorTo": "yellow", "sdk": "streamlit", "sdk_version": "1.31.0", "app_file": "app.py", "pinned": false, "base_model": "distilbert-base-uncased-finetuned-sst-2-english"} | null | mcarthuradal/malawi | [
"peft",
"safetensors",
"base_model:distilbert-base-uncased-finetuned-sst-2-english",
"license:apache-2.0",
"region:us"
] | 2024-02-12T09:55:10+00:00 | [] | [] | TAGS
#peft #safetensors #base_model-distilbert-base-uncased-finetuned-sst-2-english #license-apache-2.0 #region-us
|
Check out the configuration reference at URL
### Framework versions
- PEFT 0.8.2 | [
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11
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-hf"} | null | sravaniayyagari/llama2-finetuned-model | [
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|
# Model Card for Model ID
## Model Details
### Model Description
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### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
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APA:
## Glossary [optional]
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null | null | transformers |
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| {"library_name": "transformers", "tags": []} | null | tommymarto/LernnaviBERT_mcqbert3_students_answers_4096_mistral_seq_len_40 | [
"transformers",
"safetensors",
"bert",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-12T09:56:58+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us
|
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"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### 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 | 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. -->
# DreamBooth - doroshroman/finetuned_sd_v1_5
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of guy raise money for army using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
## 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": ["text-to-image", "dreambooth", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "stable-diffusion", "stable-diffusion-diffusers"], "inference": true, "base_model": "runwayml/stable-diffusion-v1-5", "instance_prompt": "a photo of guy raise money for army"} | text-to-image | doroshroman/finetuned_sd_v1_5 | [
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-12T09:57:16+00:00 | [] | [] | TAGS
#diffusers #tensorboard #safetensors #text-to-image #dreambooth #stable-diffusion #stable-diffusion-diffusers #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
|
# DreamBooth - doroshroman/finetuned_sd_v1_5
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of guy raise money for army using DreamBooth.
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
## 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] | [
"# DreamBooth - doroshroman/finetuned_sd_v1_5\n\nThis is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of guy raise money for army using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Training details\n\n[TODO: describe the data used to train the model]"
] | [
"TAGS\n#diffusers #tensorboard #safetensors #text-to-image #dreambooth #stable-diffusion #stable-diffusion-diffusers #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"# DreamBooth - doroshroman/finetuned_sd_v1_5\n\nThis is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of guy raise money for army using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False.",
"## 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]"
] | [
99,
91,
9,
5,
24,
16
] | [
"passage: TAGS\n#diffusers #tensorboard #safetensors #text-to-image #dreambooth #stable-diffusion #stable-diffusion-diffusers #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n# DreamBooth - doroshroman/finetuned_sd_v1_5\n\nThis is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of guy raise money for army using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False.## 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 | CODER: Knowledge infused cross-lingual medical term embedding for term normalization.
English Version.
Github Link: https://github.com/GanjinZero/CODER
```
@article{
YUAN2022103983,
title = {CODER: Knowledge-infused cross-lingual medical term embedding for term normalization},
journal = {Journal of Biomedical Informatics},
pages = {103983},
year = {2022},
issn = {1532-0464},
doi = {https://doi.org/10.1016/j.jbi.2021.103983},
url = {https://www.sciencedirect.com/science/article/pii/S1532046421003129},
author = {Zheng Yuan and Zhengyun Zhao and Haixia Sun and Jiao Li and Fei Wang and Sheng Yu},
keywords = {medical term normalization, cross-lingual, medical term representation, knowledge graph embedding, contrastive learning}
}
``` | {"language": ["en"], "license": "apache-2.0", "tags": ["bert", "biomedical"]} | feature-extraction | balzanilo/UMLSBert_ENG | [
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"biomedical",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-12T09:58:21+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #feature-extraction #biomedical #en #license-apache-2.0 #endpoints_compatible #region-us
| CODER: Knowledge infused cross-lingual medical term embedding for term normalization.
English Version.
Github Link: URL
| [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #biomedical #en #license-apache-2.0 #endpoints_compatible #region-us \n"
] | [
48
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #biomedical #en #license-apache-2.0 #endpoints_compatible #region-us \n"
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null | null | transformers | # Daybreak-Mixtral-8x7b v24.02-8
An experimental model trained on a (currently) private ERP dataset with niche content (`crestfall/daybreak` as of 2024-02-10).
Not suitable for any audience.
Model was finetuned on top of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2), and follows that model's instruction format.
## Prompt format:
The model uses the Mixtral-8x7b-instruct format:
```
text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"
```
## Training details:
The model was trained for 1.83 epochs (eval minima based on 1% of dataset) using Axolotl.
See [axolotl.yml](https://huggingface.co/crestf411/crestfall-mixtral-8x7b-hf/blob/main/axolotl/axolotl.yml) for details.
## Changelog:
* 2024-02-15: model renamed to daybreak, as it is using a new dataset
| {"language": ["en"], "tags": ["not-for-all-audiences"]} | text-generation | crestf411/daybreak-mixtral-8x7b-hf | [
"transformers",
"pytorch",
"mixtral",
"text-generation",
"not-for-all-audiences",
"conversational",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T10:00:32+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #mixtral #text-generation #not-for-all-audiences #conversational #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Daybreak-Mixtral-8x7b v24.02-8
An experimental model trained on a (currently) private ERP dataset with niche content ('crestfall/daybreak' as of 2024-02-10).
Not suitable for any audience.
Model was finetuned on top of mistralai/Mistral-7B-Instruct-v0.2, and follows that model's instruction format.
## Prompt format:
The model uses the Mixtral-8x7b-instruct format:
## Training details:
The model was trained for 1.83 epochs (eval minima based on 1% of dataset) using Axolotl.
See URL for details.
## Changelog:
* 2024-02-15: model renamed to daybreak, as it is using a new dataset
| [
"# Daybreak-Mixtral-8x7b v24.02-8\n\nAn experimental model trained on a (currently) private ERP dataset with niche content ('crestfall/daybreak' as of 2024-02-10).\n\nNot suitable for any audience.\n\nModel was finetuned on top of mistralai/Mistral-7B-Instruct-v0.2, and follows that model's instruction format.",
"## Prompt format:\n\nThe model uses the Mixtral-8x7b-instruct format:",
"## Training details:\n\nThe model was trained for 1.83 epochs (eval minima based on 1% of dataset) using Axolotl.\n\nSee URL for details.",
"## Changelog:\n\n* 2024-02-15: model renamed to daybreak, as it is using a new dataset"
] | [
"TAGS\n#transformers #pytorch #mixtral #text-generation #not-for-all-audiences #conversational #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Daybreak-Mixtral-8x7b v24.02-8\n\nAn experimental model trained on a (currently) private ERP dataset with niche content ('crestfall/daybreak' as of 2024-02-10).\n\nNot suitable for any audience.\n\nModel was finetuned on top of mistralai/Mistral-7B-Instruct-v0.2, and follows that model's instruction format.",
"## Prompt format:\n\nThe model uses the Mixtral-8x7b-instruct format:",
"## Training details:\n\nThe model was trained for 1.83 epochs (eval minima based on 1% of dataset) using Axolotl.\n\nSee URL for details.",
"## Changelog:\n\n* 2024-02-15: model renamed to daybreak, as it is using a new dataset"
] | [
61,
90,
22,
38,
25
] | [
"passage: TAGS\n#transformers #pytorch #mixtral #text-generation #not-for-all-audiences #conversational #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Daybreak-Mixtral-8x7b v24.02-8\n\nAn experimental model trained on a (currently) private ERP dataset with niche content ('crestfall/daybreak' as of 2024-02-10).\n\nNot suitable for any audience.\n\nModel was finetuned on top of mistralai/Mistral-7B-Instruct-v0.2, and follows that model's instruction format.## Prompt format:\n\nThe model uses the Mixtral-8x7b-instruct format:## Training details:\n\nThe model was trained for 1.83 epochs (eval minima based on 1% of dataset) using Axolotl.\n\nSee URL for details.## Changelog:\n\n* 2024-02-15: model renamed to daybreak, as it is using a new dataset"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Image-Valence
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 custom dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4464
- Accuracy: 0.5863
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2256 | 0.78 | 100 | 1.0936 | 0.5451 |
| 0.7315 | 1.56 | 200 | 0.9981 | 0.5882 |
| 0.2118 | 2.34 | 300 | 1.1650 | 0.5902 |
| 0.1119 | 3.12 | 400 | 1.2864 | 0.5863 |
| 0.1116 | 3.91 | 500 | 1.4464 | 0.5863 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "vit-Valence", "results": []}]} | image-classification | SeyedAli/Image-Valence | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T10:03:53+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Image-Valence
=============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the custom dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4464
* Accuracy: 0.5863
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 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #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: 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 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
75,
97,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #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: 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 4.35.2\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-detests-wandb24
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: 0.4371
- Accuracy: 0.7938
- F1-score: 0.7241
- Precision: 0.7136
- Recall: 0.7396
- Auc: 0.7396
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|:------:|
| 0.458 | 1.0 | 153 | 0.4512 | 0.7725 | 0.4358 | 0.3863 | 0.5 | 0.5 |
| 0.4262 | 2.0 | 306 | 0.4371 | 0.7938 | 0.7241 | 0.7136 | 0.7396 | 0.7396 |
### 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-detests-wandb24", "results": []}]} | text-classification | Pablo94/xlm-roberta-base-finetuned-detests-wandb24 | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T10:06:40+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-detests-wandb24
==========================================
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: 0.4371
* Accuracy: 0.7938
* F1-score: 0.7241
* Precision: 0.7136
* Recall: 0.7396
* Auc: 0.7396
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### 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: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### 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 #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: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### 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"
] | [
69,
98,
4,
33
] | [
"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: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### 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": []} | null | isha1/mistral_lora_model | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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## Technical Specifications [optional]
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] |
null | null | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga arekpaterak -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga arekpaterak -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga arekpaterak
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1200000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "544.50 +/- 98.47", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | arekpaterak/dqn-SpaceInvadersNoFrameskip-v4 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-12T10:10:33+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: URL
SB3: URL
SB3 Contrib: URL
Install the RL Zoo (with SB3 and SB3-Contrib):
If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:
## Training (with the RL Zoo)
## Hyperparameters
# Environment Arguments
| [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
43,
90,
73,
9,
5,
7
] | [
"passage: TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:## Training (with the RL Zoo)## Hyperparameters# Environment Arguments"
] | [
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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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4894
## 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: 1
- seed: 42
- gradient_accumulation_steps: 16
- 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: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6489 | 0.54 | 500 | 1.4894 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "base_model": "google/pegasus-cnn_dailymail", "model-index": [{"name": "pegasus-samsum", "results": []}]} | text2text-generation | youngbreadho/pegasus-samsum | [
"transformers",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T10:11:39+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4894
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: 1
* seed: 42
* gradient\_accumulation\_steps: 16
* 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: 500
* num\_epochs: 1
### 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: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\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: 500\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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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\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: 500\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"
] | [
68,
144,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-cnn_dailymail #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: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\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: 500\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. -->
# beto-sentiment-analysis-finetuned-detests-wandb24
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: 0.6204
- Accuracy: 0.8674
- F1-score: 0.7993
- Precision: 0.8225
- Recall: 0.7822
- Auc: 0.7822
## 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: 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|:------:|
| 0.393 | 1.0 | 77 | 0.3365 | 0.8592 | 0.7633 | 0.8424 | 0.7287 | 0.7287 |
| 0.1947 | 2.0 | 154 | 0.3843 | 0.8396 | 0.7845 | 0.7716 | 0.8023 | 0.8023 |
| 0.0597 | 3.0 | 231 | 0.5486 | 0.8740 | 0.8046 | 0.8398 | 0.7814 | 0.7814 |
| 0.0028 | 4.0 | 308 | 0.6204 | 0.8674 | 0.7993 | 0.8225 | 0.7822 | 0.7822 |
### 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-detests-wandb24", "results": []}]} | text-classification | Pablo94/beto-sentiment-analysis-finetuned-detests-wandb24 | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:finiteautomata/beto-sentiment-analysis",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T10:15:18+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-detests-wandb24
=================================================
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: 0.6204
* Accuracy: 0.8674
* F1-score: 0.7993
* Precision: 0.8225
* Recall: 0.7822
* Auc: 0.7822
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: 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: 4
### 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: 5e-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: 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"
] | [
"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: 5e-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: 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"
] | [
66,
98,
4,
33
] | [
"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: 5e-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: 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 | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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### Framework versions
- PEFT 0.7.2.dev0 | {"library_name": "peft", "base_model": "google/flan-t5-large"} | null | HeydarS/flan-t5-large_peft_v12 | [
"peft",
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"1910.09700"
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#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-large #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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- Language(s) (NLP):
- License:
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### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
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APA:
## Glossary [optional]
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] |
null | null | null | # Daybreak-Mixtral-8x7b v24.02-7
An experimental model trained on a (currently) private ERP dataset of highly curated niche content (`crestfall/daybreak` as of 2024-02-10).
Not suitable for any audience.
Model was fine tuned on top of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2), and follows that model's instruction format.
## Prompt format:
The model uses the Mixtral-8x7b-instruct format:
```
text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"
```
## Training details:
The model was trained for 1.83 epochs (eval minima based on 1% of dataset) using Axolotl.
See [axolotl.yml](https://huggingface.co/crestf411/crestfall-mixtral-8x7b-hf/blob/main/axolotl/axolotl.yml) for details.
| {"language": ["en"], "tags": ["not-for-all-audiences"]} | null | crestf411/daybreak-mixtral-8x7b-gguf | [
"gguf",
"not-for-all-audiences",
"en",
"region:us"
] | 2024-02-12T10:16:05+00:00 | [] | [
"en"
] | TAGS
#gguf #not-for-all-audiences #en #region-us
| # Daybreak-Mixtral-8x7b v24.02-7
An experimental model trained on a (currently) private ERP dataset of highly curated niche content ('crestfall/daybreak' as of 2024-02-10).
Not suitable for any audience.
Model was fine tuned on top of mistralai/Mistral-7B-Instruct-v0.2, and follows that model's instruction format.
## Prompt format:
The model uses the Mixtral-8x7b-instruct format:
## Training details:
The model was trained for 1.83 epochs (eval minima based on 1% of dataset) using Axolotl.
See URL for details.
| [
"# Daybreak-Mixtral-8x7b v24.02-7\n\nAn experimental model trained on a (currently) private ERP dataset of highly curated niche content ('crestfall/daybreak' as of 2024-02-10).\n\nNot suitable for any audience.\n\nModel was fine tuned on top of mistralai/Mistral-7B-Instruct-v0.2, and follows that model's instruction format.",
"## Prompt format:\n\nThe model uses the Mixtral-8x7b-instruct format:",
"## Training details:\n\nThe model was trained for 1.83 epochs (eval minima based on 1% of dataset) using Axolotl.\n\nSee URL for details."
] | [
"TAGS\n#gguf #not-for-all-audiences #en #region-us \n",
"# Daybreak-Mixtral-8x7b v24.02-7\n\nAn experimental model trained on a (currently) private ERP dataset of highly curated niche content ('crestfall/daybreak' as of 2024-02-10).\n\nNot suitable for any audience.\n\nModel was fine tuned on top of mistralai/Mistral-7B-Instruct-v0.2, and follows that model's instruction format.",
"## Prompt format:\n\nThe model uses the Mixtral-8x7b-instruct format:",
"## Training details:\n\nThe model was trained for 1.83 epochs (eval minima based on 1% of dataset) using Axolotl.\n\nSee URL for details."
] | [
20,
93,
22,
38
] | [
"passage: TAGS\n#gguf #not-for-all-audiences #en #region-us \n# Daybreak-Mixtral-8x7b v24.02-7\n\nAn experimental model trained on a (currently) private ERP dataset of highly curated niche content ('crestfall/daybreak' as of 2024-02-10).\n\nNot suitable for any audience.\n\nModel was fine tuned on top of mistralai/Mistral-7B-Instruct-v0.2, and follows that model's instruction format.## Prompt format:\n\nThe model uses the Mixtral-8x7b-instruct format:## Training details:\n\nThe model was trained for 1.83 epochs (eval minima based on 1% of dataset) using Axolotl.\n\nSee URL for details."
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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": "266.70 +/- 24.10", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | manuelcorsetti/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-12T10:17:39+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | tommymarto/LernnaviBERT_mcqbert3_students_answers_384_lstm_seq_len_20 | [
"transformers",
"safetensors",
"bert",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-12T10:21:57+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us
|
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"### Out-of-Scope Use",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
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] | [
"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]:",
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"### 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 | 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. -->
# electra-base-discriminator-finetuned-detests-wandb24
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4457
- Accuracy: 0.7741
- F1-score: 0.6965
- Precision: 0.6879
- Recall: 0.7092
- Auc: 0.7092
## 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: 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|:------:|
| 0.4719 | 1.0 | 77 | 0.4698 | 0.7971 | 0.6380 | 0.7159 | 0.6199 | 0.6199 |
| 0.4737 | 2.0 | 154 | 0.4457 | 0.7741 | 0.6965 | 0.6879 | 0.7092 | 0.7092 |
### 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"], "metrics": ["accuracy", "precision", "recall"], "base_model": "google/electra-base-discriminator", "model-index": [{"name": "electra-base-discriminator-finetuned-detests-wandb24", "results": []}]} | text-classification | Pablo94/electra-base-discriminator-finetuned-detests-wandb24 | [
"transformers",
"tensorboard",
"safetensors",
"electra",
"text-classification",
"generated_from_trainer",
"base_model:google/electra-base-discriminator",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T10:22:23+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #electra #text-classification #generated_from_trainer #base_model-google/electra-base-discriminator #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| electra-base-discriminator-finetuned-detests-wandb24
====================================================
This model is a fine-tuned version of google/electra-base-discriminator on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4457
* Accuracy: 0.7741
* F1-score: 0.6965
* Precision: 0.6879
* Recall: 0.7092
* Auc: 0.7092
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: 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: 2
### 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 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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98,
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"passage: TAGS\n#transformers #tensorboard #safetensors #electra #text-classification #generated_from_trainer #base_model-google/electra-base-discriminator #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: 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: 2### 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 |
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| {"library_name": "transformers", "tags": []} | null | mertllc/mms-tts-tur-inkilap_modified_100 | [
"transformers",
"safetensors",
"vits",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-12T10:23:18+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #vits #arxiv-1910.09700 #endpoints_compatible #region-us
|
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### 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 #vits #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 #vits #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 | transformers |
# haLLAwa2
haLLAwa2 is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
## 🧩 Configuration
```yaml
slices:
- sources:
- model: OpenPipe/mistral-ft-optimized-1227
layer_range: [0, 32]
- model: machinists/Mistral-7B-SQL
layer_range: [0, 32]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1227
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5 # fallback for rest of tensors
dtype: bfloat16
\```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_AbacusResearch__haLLAwa2)
| Metric |Value|
|---------------------------------|----:|
|Avg. |64.44|
|AI2 Reasoning Challenge (25-Shot)|63.31|
|HellaSwag (10-Shot) |84.51|
|MMLU (5-Shot) |63.52|
|TruthfulQA (0-shot) |47.38|
|Winogrande (5-shot) |75.85|
|GSM8k (5-shot) |52.08|
| {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "OpenPipe/mistral-ft-optimized-1227", "machinists/Mistral-7B-SQL"], "model-index": [{"name": "haLLAwa2", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 63.31, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/haLLAwa2", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 84.51, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/haLLAwa2", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 63.52, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/haLLAwa2", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 47.38}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/haLLAwa2", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 75.85, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/haLLAwa2", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 52.08, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/haLLAwa2", "name": "Open LLM Leaderboard"}}]}]} | text-generation | AbacusResearch/haLLAwa2 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"OpenPipe/mistral-ft-optimized-1227",
"machinists/Mistral-7B-SQL",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T10:25:42+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #OpenPipe/mistral-ft-optimized-1227 #machinists/Mistral-7B-SQL #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| haLLAwa2
========
haLLAwa2 is a merge of the following models using mergekit:
Configuration
-------------
Open LLM Leaderboard Evaluation Results
=======================================
Detailed results can be found here
| [] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #OpenPipe/mistral-ft-optimized-1227 #machinists/Mistral-7B-SQL #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
97
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #OpenPipe/mistral-ft-optimized-1227 #machinists/Mistral-7B-SQL #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "260.26 +/- 15.22", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | hugo-massonnat/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-12T10:30:08+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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