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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. -->
# videomae-base-finetuned
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2738
- Accuracy: 0.9360
## 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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 184
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2081 | 0.13 | 24 | 0.8609 | 0.8488 |
| 0.9402 | 1.13 | 48 | 1.6943 | 0.3314 |
| 0.7736 | 2.13 | 72 | 0.2337 | 0.9535 |
| 0.9935 | 3.13 | 96 | 0.2556 | 0.9535 |
| 0.4901 | 4.13 | 120 | 0.3547 | 0.8837 |
| 0.43 | 5.13 | 144 | 0.7274 | 0.6919 |
| 0.4121 | 6.13 | 168 | 0.2916 | 0.9244 |
| 0.3765 | 7.09 | 184 | 0.2738 | 0.9360 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.13.3
| {"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "MCG-NJU/videomae-base", "model-index": [{"name": "videomae-base-finetuned", "results": []}]} | video-classification | rudeuns/videomae-base-finetuned | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | 2024-02-14T08:00:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #videomae #video-classification #generated_from_trainer #base_model-MCG-NJU/videomae-base #license-cc-by-nc-4.0 #endpoints_compatible #region-us
| videomae-base-finetuned
=======================
This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2738
* Accuracy: 0.9360
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
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 184
### Training results
### Framework versions
* Transformers 4.33.1
* Pytorch 2.1.2
* Datasets 2.1.0
* 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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 184",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.33.1\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #videomae #video-classification #generated_from_trainer #base_model-MCG-NJU/videomae-base #license-cc-by-nc-4.0 #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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 184",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.33.1\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.13.3"
] | [
73,
115,
4,
30
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #videomae #video-classification #generated_from_trainer #base_model-MCG-NJU/videomae-base #license-cc-by-nc-4.0 #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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 184### Training results### Framework versions\n\n\n* Transformers 4.33.1\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.13.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small testing
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the testing dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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: 500
- 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
| {"language": ["ara"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["uoseftalaat/testing"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small testing", "results": []}]} | automatic-speech-recognition | uoseftalaat/whisper-small-hi | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"ara",
"dataset:uoseftalaat/testing",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-14T08:02:32+00:00 | [] | [
"ara"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #ara #dataset-uoseftalaat/testing #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us
|
# Whisper Small testing
This model is a fine-tuned version of openai/whisper-small on the testing dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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: 500
- 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
| [
"# Whisper Small testing\n\nThis model is a fine-tuned version of openai/whisper-small on the testing dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 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: 500\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\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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"TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #ara #dataset-uoseftalaat/testing #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Whisper Small testing\n\nThis model is a fine-tuned version of openai/whisper-small on the testing dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 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: 500\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
91,
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38
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"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #ara #dataset-uoseftalaat/testing #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us \n# Whisper Small testing\n\nThis model is a fine-tuned version of openai/whisper-small on the testing dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 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: 500\n- mixed_precision_training: Native AMP### Training results### Framework versions\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 | mlx |
# reim-ai-chat-mlx
This model was converted to MLX format from [`mistralai/Mistral-7B-Instruct-v0.2`]().
Refer to the [original model card](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) for more details on the model.
## Use with mlx
```bash
pip install mlx
git clone https://github.com/ml-explore/mlx-examples.git
cd mlx-examples/llms/hf_llm
python generate.py --model mlx-community/reim-ai-chat-mlx --prompt "My name is"
```
| {"license": "apache-2.0", "tags": ["finetuned", "mlx"], "pipeline_tag": "text-generation", "inference": false} | text-generation | mlx-community/reim-ai-chat-mlx | [
"mlx",
"mistral",
"finetuned",
"text-generation",
"conversational",
"license:apache-2.0",
"region:us"
] | 2024-02-14T08:02:34+00:00 | [] | [] | TAGS
#mlx #mistral #finetuned #text-generation #conversational #license-apache-2.0 #region-us
|
# reim-ai-chat-mlx
This model was converted to MLX format from ['mistralai/Mistral-7B-Instruct-v0.2']().
Refer to the original model card for more details on the model.
## Use with mlx
| [
"# reim-ai-chat-mlx\nThis model was converted to MLX format from ['mistralai/Mistral-7B-Instruct-v0.2']().\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
"TAGS\n#mlx #mistral #finetuned #text-generation #conversational #license-apache-2.0 #region-us \n",
"# reim-ai-chat-mlx\nThis model was converted to MLX format from ['mistralai/Mistral-7B-Instruct-v0.2']().\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
33,
53,
5
] | [
"passage: TAGS\n#mlx #mistral #finetuned #text-generation #conversational #license-apache-2.0 #region-us \n# reim-ai-chat-mlx\nThis model was converted to MLX format from ['mistralai/Mistral-7B-Instruct-v0.2']().\nRefer to the original model card for more details on the model.## Use with mlx"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | IlyasMoutawwakil/benchmarks | [
"transformers",
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"1910.09700"
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#transformers #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
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] |
null | null | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
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: mathreader/ppo-Pyramid
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | reinforcement-learning | mathreader/ppo-Pyramid | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | 2024-02-14T08:06:48+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids
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: mathreader/ppo-Pyramid
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\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: mathreader/ppo-Pyramid\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\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: mathreader/ppo-Pyramid\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
48,
202
] | [
"passage: TAGS\n#ml-agents #tensorboard #onnx #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\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: mathreader/ppo-Pyramid\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 | B2111797/recipe_gener_v6 | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
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|
# Model Card for Model ID
## Model Details
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- Developed by:
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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## Glossary [optional]
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] |
null | null | peft |
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[More Information Needed]
### Downstream Use [optional]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Technical Specifications [optional]
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "ybelkada/blip2-opt-2.7b-fp16-sharded"} | null | sashika/blip2-opt-2.7b-mydata-captions-adapters | [
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# Model Card for Model ID
## Model Details
### Model Description
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- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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- Compute Region:
- Carbon Emitted:
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | huangyt/home-ccp4-r64-q_v_k_o_gate_down_up-2 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T08:14:57+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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## How to Get Started with the Model
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BibTeX:
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## 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:",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
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"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | MaggieZhang/try-bloomz-1b7-2 | [
"transformers",
"safetensors",
"bloom",
"text-generation",
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#transformers #safetensors #bloom #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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## Uses
### Direct Use
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## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### 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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#### Hardware
#### Software
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BibTeX:
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## Glossary [optional]
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## Model Card Contact
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null | null | null | # BLIP Nightshade Detection
This is a finetuned version of Salesforce/blip-image-captioning-base. The current version is only trained on images that were edited by Nightshade with the setting "high". It will either return "nightshade _ poisoned" or "nightshade _ clean" as a result.
---
license: cc-by-4.0
---
| {} | null | Feroc/blip-image-captioning-nightshade | [
"region:us"
] | 2024-02-14T08:16:06+00:00 | [] | [] | TAGS
#region-us
| # BLIP Nightshade Detection
This is a finetuned version of Salesforce/blip-image-captioning-base. The current version is only trained on images that were edited by Nightshade with the setting "high". It will either return "nightshade _ poisoned" or "nightshade _ clean" as a result.
---
license: cc-by-4.0
---
| [
"# BLIP Nightshade Detection\n\nThis is a finetuned version of Salesforce/blip-image-captioning-base. The current version is only trained on images that were edited by Nightshade with the setting \"high\". It will either return \"nightshade _ poisoned\" or \"nightshade _ clean\" as a result.\n\n---\nlicense: cc-by-4.0\n---"
] | [
"TAGS\n#region-us \n",
"# BLIP Nightshade Detection\n\nThis is a finetuned version of Salesforce/blip-image-captioning-base. The current version is only trained on images that were edited by Nightshade with the setting \"high\". It will either return \"nightshade _ poisoned\" or \"nightshade _ clean\" as a result.\n\n---\nlicense: cc-by-4.0\n---"
] | [
6,
88
] | [
"passage: TAGS\n#region-us \n# BLIP Nightshade Detection\n\nThis is a finetuned version of Salesforce/blip-image-captioning-base. The current version is only trained on images that were edited by Nightshade with the setting \"high\". It will either return \"nightshade _ poisoned\" or \"nightshade _ clean\" as a result.\n\n---\nlicense: cc-by-4.0\n---"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-commonvoice13-dv
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1693
- Wer Ortho: 62.5392
- Wer: 12.8960
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.1231 | 1.63 | 500 | 0.1693 | 62.5392 | 12.8960 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"language": ["dv"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_13_0"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "whisper-small-commonvoice13-dv", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 13", "type": "mozilla-foundation/common_voice_13_0", "config": "dv", "split": "test", "args": "dv"}, "metrics": [{"type": "wer", "value": 12.895990541433392, "name": "Wer"}]}]}]} | automatic-speech-recognition | ChuGyouk/whisper-small-dv | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dv",
"dataset:mozilla-foundation/common_voice_13_0",
"base_model:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-02-14T08:19:24+00:00 | [] | [
"dv"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #dv #dataset-mozilla-foundation/common_voice_13_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us
| whisper-small-commonvoice13-dv
==============================
This model is a fine-tuned version of openai/whisper-small on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1693
* Wer Ortho: 62.5392
* Wer: 12.8960
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant\_with\_warmup
* lr\_scheduler\_warmup\_steps: 50
* training\_steps: 500
* 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: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\\_with\\_warmup\n* lr\\_scheduler\\_warmup\\_steps: 50\n* training\\_steps: 500\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: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\\_with\\_warmup\n* lr\\_scheduler\\_warmup\\_steps: 50\n* training\\_steps: 500\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"
] | [
94,
137,
4,
33
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"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #dv #dataset-mozilla-foundation/common_voice_13_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: 16\n* seed: 42\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: 50\n* training\\_steps: 500\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 | diffusers | ### My-Pet-cat-xzg Dreambooth model trained by AKKUs following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: AM.EN.U4EEE20004
Sample pictures of this concept:
| {"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]} | text-to-image | AKKUs/my-pet-cat-xzg | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-14T08:19:44+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-cat-xzg Dreambooth model trained by AKKUs following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: AM.EN.U4EEE20004
Sample pictures of this concept:
!0
| [
"### My-Pet-cat-xzg Dreambooth model trained by AKKUs following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: AM.EN.U4EEE20004\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-cat-xzg Dreambooth model trained by AKKUs following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: AM.EN.U4EEE20004\n\nSample pictures of this concept:\n\n !0"
] | [
73,
61
] | [
"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-cat-xzg Dreambooth model trained by AKKUs following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: AM.EN.U4EEE20004\n\nSample pictures of this concept:\n\n !0"
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] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [pascalrai/nep-summ-BART](https://huggingface.co/pascalrai/nep-summ-BART) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
- label_smoothing_factor: 0.1
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "pascalrai/nep-summ-BART", "model-index": [{"name": "results", "results": []}]} | text2text-generation | sanjeev-bhandari01/results | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:pascalrai/nep-summ-BART",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T08:24:11+00:00 | [] | [] | TAGS
#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-pascalrai/nep-summ-BART #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# results
This model is a fine-tuned version of pascalrai/nep-summ-BART on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
- label_smoothing_factor: 0.1
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.2
| [
"# results\n\nThis model is a fine-tuned version of pascalrai/nep-summ-BART on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-05\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 4\n- label_smoothing_factor: 0.1",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-pascalrai/nep-summ-BART #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# results\n\nThis model is a fine-tuned version of pascalrai/nep-summ-BART on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-05\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 4\n- label_smoothing_factor: 0.1",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Tokenizers 0.15.2"
] | [
68,
32,
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"passage: TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-pascalrai/nep-summ-BART #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# results\n\nThis model is a fine-tuned version of pascalrai/nep-summ-BART on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-05\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 4\n- label_smoothing_factor: 0.1### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\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 Large V3 Vi - Prateek Jain
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the google/fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2355
- Wer: 218.8330
## 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: 250
- training_steps: 1500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0148 | 2.66 | 500 | 0.2193 | 80.1012 |
| 0.0014 | 5.32 | 1000 | 0.2275 | 247.5556 |
| 0.0004 | 7.98 | 1500 | 0.2355 | 218.8330 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"language": ["vi"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["google/fleurs"], "metrics": ["wer"], "base_model": "openai/whisper-large-v3", "model-index": [{"name": "Whisper Large V3 Vi - Prateek Jain", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "google/fleurs", "type": "google/fleurs", "config": "vi_vn", "split": "None", "args": "config: vi, split: test"}, "metrics": [{"type": "wer", "value": 218.83302440531355, "name": "Wer"}]}]}]} | automatic-speech-recognition | Prateekjain24/whisper-large-v3.vi | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"vi",
"dataset:google/fleurs",
"base_model:openai/whisper-large-v3",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-02-14T08:26:33+00:00 | [] | [
"vi"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #vi #dataset-google/fleurs #base_model-openai/whisper-large-v3 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Whisper Large V3 Vi - Prateek Jain
==================================
This model is a fine-tuned version of openai/whisper-large-v3 on the google/fleurs dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2355
* Wer: 218.8330
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: 250
* training\_steps: 1500
* 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: 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: 250\n* training\\_steps: 1500\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 #vi #dataset-google/fleurs #base_model-openai/whisper-large-v3 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 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: 250\n* training\\_steps: 1500\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"
] | [
96,
130,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #vi #dataset-google/fleurs #base_model-openai/whisper-large-v3 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 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: 250\n* training\\_steps: 1500\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 | null |
<br>
<br>
# LWM-Text-Chat-512K Model Card
## Model details
**Model type:**
LWM-Text-Chat-512K is an open-source model trained from LLaMA-2 on a subset of Books3 filtered data. It is an auto-regressive language model, based on the transformer architecture.
**Model date:**
LWM-Text-Chat-512K was trained in December 2023.
**Paper or resources for more information:**
https://largeworldmodel.github.io/
## License
Llama 2 is licensed under the LLAMA 2 Community License,
Copyright (c) Meta Platforms, Inc. All Rights Reserved.
**Where to send questions or comments about the model:**
https://github.com/LargeWorldModel/lwm/issues
## Training dataset
- 3500 subset of Books3 documents with 500K to 1M tokens
***
Vanilla Quantization by [nold](https://huggingface.co/nold), Original Model [LargeWorldModel/LWM-Text-Chat-512K](https://huggingface.co/LargeWorldModel/LWM-Text-Chat-512K). Created using [llm-quantizer](https://github.com/Nold360/llm-quantizer) Pipeline - c4aee1019a651a28a4f89587b96894ae7188734f
| {"inference": false} | null | nold/LWM-Text-Chat-512K-GGUF | [
"gguf",
"region:us"
] | 2024-02-14T08:31:18+00:00 | [] | [] | TAGS
#gguf #region-us
|
<br>
<br>
# LWM-Text-Chat-512K Model Card
## Model details
Model type:
LWM-Text-Chat-512K is an open-source model trained from LLaMA-2 on a subset of Books3 filtered data. It is an auto-regressive language model, based on the transformer architecture.
Model date:
LWM-Text-Chat-512K was trained in December 2023.
Paper or resources for more information:
URL
## License
Llama 2 is licensed under the LLAMA 2 Community License,
Copyright (c) Meta Platforms, Inc. All Rights Reserved.
Where to send questions or comments about the model:
URL
## Training dataset
- 3500 subset of Books3 documents with 500K to 1M tokens
*
Vanilla Quantization by nold, Original Model LargeWorldModel/LWM-Text-Chat-512K. Created using llm-quantizer Pipeline - c4aee1019a651a28a4f89587b96894ae7188734f
| [
"# LWM-Text-Chat-512K Model Card",
"## Model details\n\nModel type:\nLWM-Text-Chat-512K is an open-source model trained from LLaMA-2 on a subset of Books3 filtered data. It is an auto-regressive language model, based on the transformer architecture.\n\nModel date:\nLWM-Text-Chat-512K was trained in December 2023.\n\nPaper or resources for more information:\nURL",
"## License\nLlama 2 is licensed under the LLAMA 2 Community License, \nCopyright (c) Meta Platforms, Inc. All Rights Reserved.\n\nWhere to send questions or comments about the model:\nURL",
"## Training dataset\n- 3500 subset of Books3 documents with 500K to 1M tokens\n\n*\n\nVanilla Quantization by nold, Original Model LargeWorldModel/LWM-Text-Chat-512K. Created using llm-quantizer Pipeline - c4aee1019a651a28a4f89587b96894ae7188734f"
] | [
"TAGS\n#gguf #region-us \n",
"# LWM-Text-Chat-512K Model Card",
"## Model details\n\nModel type:\nLWM-Text-Chat-512K is an open-source model trained from LLaMA-2 on a subset of Books3 filtered data. It is an auto-regressive language model, based on the transformer architecture.\n\nModel date:\nLWM-Text-Chat-512K was trained in December 2023.\n\nPaper or resources for more information:\nURL",
"## License\nLlama 2 is licensed under the LLAMA 2 Community License, \nCopyright (c) Meta Platforms, Inc. All Rights Reserved.\n\nWhere to send questions or comments about the model:\nURL",
"## Training dataset\n- 3500 subset of Books3 documents with 500K to 1M tokens\n\n*\n\nVanilla Quantization by nold, Original Model LargeWorldModel/LWM-Text-Chat-512K. Created using llm-quantizer Pipeline - c4aee1019a651a28a4f89587b96894ae7188734f"
] | [
9,
12,
85,
41,
81
] | [
"passage: TAGS\n#gguf #region-us \n# LWM-Text-Chat-512K Model Card## Model details\n\nModel type:\nLWM-Text-Chat-512K is an open-source model trained from LLaMA-2 on a subset of Books3 filtered data. It is an auto-regressive language model, based on the transformer architecture.\n\nModel date:\nLWM-Text-Chat-512K was trained in December 2023.\n\nPaper or resources for more information:\nURL## License\nLlama 2 is licensed under the LLAMA 2 Community License, \nCopyright (c) Meta Platforms, Inc. All Rights Reserved.\n\nWhere to send questions or comments about the model:\nURL## Training dataset\n- 3500 subset of Books3 documents with 500K to 1M tokens\n\n*\n\nVanilla Quantization by nold, Original Model LargeWorldModel/LWM-Text-Chat-512K. Created using llm-quantizer Pipeline - c4aee1019a651a28a4f89587b96894ae7188734f"
] | [
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null | null | null |
# StyleBertVITS2向けの事前学習モデル
[StyleBertVITS2](https://github.com/litagin02/Style-Bert-VITS2)で使用できる事前学習データになります
<!-- TOC -->
* [StyleBertVITS2向けの事前学習モデル](#stylebertvits2向けの事前学習モデル)
* [学習データセット](#学習データセット)
* [学習パラメータ](#学習パラメータ)
* [SpeechMOSによる自然性評価](#speechmosによる自然性評価)
* [loss](#loss)
* [ライセンス](#ライセンス)
<!-- TOC -->
## 学習データセット
* [つくよみちゃんコーパス│声優統計コーパス（JVSコーパス準拠）](https://tyc.rei-yumesaki.net/material/corpus/)
* [みんなで作るJSUTコーパスbasic5000 BASIC5000_0001～BASIC5000_0600](https://tyc.rei-yumesaki.net/material/minnade-jsut/) （夢前黎担当部分を許可を得て使用）
## 学習パラメータ
* 最終ステップ数 : 375k step
* 学習時間 100 ~ 200時間程度
* bfloat16 : false
## 学習済みモデル
学習済みモデルには、pthとsafetensorsの二つをアップロードしています。
pthからsafetensorsへの変換には、[学習したpthファイルから事前学習モデルsafetensorsを作るやつ](https://gist.github.com/litagin02/c6ab8a35c2b2b779c632ca820b805267)を参考にこちらで改変したものを使用しました
## SpeechMOSによる自然性評価
## loss
# ライセンス
ライセンスは、[つくよみちゃんコーパス│声優統計コーパス（JVSコーパス準拠）](https://tyc.rei-yumesaki.net/material/corpus/)に準じます | {"language": ["jp"], "license": "other", "tags": ["\u3064\u304f\u3088\u307f\u3061\u3083\u3093", "StyleBertVITS2"]} | null | ayousanz/style-bert-vits2-pretraing | [
"つくよみちゃん",
"StyleBertVITS2",
"jp",
"license:other",
"region:us"
] | 2024-02-14T08:33:58+00:00 | [] | [
"jp"
] | TAGS
#つくよみちゃん #StyleBertVITS2 #jp #license-other #region-us
|
# StyleBertVITS2向けの事前学習モデル
StyleBertVITS2で使用できる事前学習データになります
* StyleBertVITS2向けの事前学習モデル
* 学習データセット
* 学習パラメータ
* SpeechMOSによる自然性評価
* loss
* ライセンス
## 学習データセット
* つくよみちゃんコーパス│声優統計コーパス（JVSコーパス準拠）
* みんなで作るJSUTコーパスbasic5000 BASIC5000_0001～BASIC5000_0600 （夢前黎担当部分を許可を得て使用）
## 学習パラメータ
* 最終ステップ数 : 375k step
* 学習時間 100 ~ 200時間程度
* bfloat16 : false
## 学習済みモデル
学習済みモデルには、pthとsafetensorsの二つをアップロードしています。
pthからsafetensorsへの変換には、学習したpthファイルから事前学習モデルsafetensorsを作るやつを参考にこちらで改変したものを使用しました
## SpeechMOSによる自然性評価
## loss
# ライセンス
ライセンスは、つくよみちゃんコーパス│声優統計コーパス（JVSコーパス準拠）に準じます | [
"# StyleBertVITS2向けの事前学習モデル\n\nStyleBertVITS2で使用できる事前学習データになります\n\n\n* StyleBertVITS2向けの事前学習モデル\n * 学習データセット\n * 学習パラメータ\n * SpeechMOSによる自然性評価\n * loss\n* ライセンス",
"## 学習データセット\n* つくよみちゃんコーパス│声優統計コーパス（JVSコーパス準拠） \n* みんなで作るJSUTコーパスbasic5000 BASIC5000_0001～BASIC5000_0600 （夢前黎担当部分を許可を得て使用）",
"## 学習パラメータ\n* 最終ステップ数 : 375k step\n* 学習時間 100 ~ 200時間程度\n* bfloat16 : false",
"## 学習済みモデル\n\n学習済みモデルには、pthとsafetensorsの二つをアップロードしています。\n\npthからsafetensorsへの変換には、学習したpthファイルから事前学習モデルsafetensorsを作るやつを参考にこちらで改変したものを使用しました",
"## SpeechMOSによる自然性評価\n\n",
"## loss\n\n",
"# ライセンス\nライセンスは、つくよみちゃんコーパス│声優統計コーパス（JVSコーパス準拠）に準じます"
] | [
"TAGS\n#つくよみちゃん #StyleBertVITS2 #jp #license-other #region-us \n",
"# StyleBertVITS2向けの事前学習モデル\n\nStyleBertVITS2で使用できる事前学習データになります\n\n\n* StyleBertVITS2向けの事前学習モデル\n * 学習データセット\n * 学習パラメータ\n * SpeechMOSによる自然性評価\n * loss\n* ライセンス",
"## 学習データセット\n* つくよみちゃんコーパス│声優統計コーパス（JVSコーパス準拠） \n* みんなで作るJSUTコーパスbasic5000 BASIC5000_0001～BASIC5000_0600 （夢前黎担当部分を許可を得て使用）",
"## 学習パラメータ\n* 最終ステップ数 : 375k step\n* 学習時間 100 ~ 200時間程度\n* bfloat16 : false",
"## 学習済みモデル\n\n学習済みモデルには、pthとsafetensorsの二つをアップロードしています。\n\npthからsafetensorsへの変換には、学習したpthファイルから事前学習モデルsafetensorsを作るやつを参考にこちらで改変したものを使用しました",
"## SpeechMOSによる自然性評価\n\n",
"## loss\n\n",
"# ライセンス\nライセンスは、つくよみちゃんコーパス│声優統計コーパス（JVSコーパス準拠）に準じます"
] | [
25,
64,
63,
33,
60,
19,
8,
35
] | [
"passage: TAGS\n#つくよみちゃん #StyleBertVITS2 #jp #license-other #region-us \n# StyleBertVITS2向けの事前学習モデル\n\nStyleBertVITS2で使用できる事前学習データになります\n\n\n* StyleBertVITS2向けの事前学習モデル\n * 学習データセット\n * 学習パラメータ\n * SpeechMOSによる自然性評価\n * loss\n* ライセンス## 学習データセット\n* つくよみちゃんコーパス│声優統計コーパス（JVSコーパス準拠） \n* みんなで作るJSUTコーパスbasic5000 BASIC5000_0001～BASIC5000_0600 （夢前黎担当部分を許可を得て使用）## 学習パラメータ\n* 最終ステップ数 : 375k step\n* 学習時間 100 ~ 200時間程度\n* bfloat16 : false## 学習済みモデル\n\n学習済みモデルには、pthとsafetensorsの二つをアップロードしています。\n\npthからsafetensorsへの変換には、学習したpthファイルから事前学習モデルsafetensorsを作るやつを参考にこちらで改変したものを使用しました## SpeechMOSによる自然性評価\n\n## loss\n\n# ライセンス\nライセンスは、つくよみちゃんコーパス│声優統計コーパス（JVSコーパス準拠）に準じます"
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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. -->
# falcon7binstruct_mentalhealthmodel_oct23
This model is a fine-tuned version of [vilsonrodrigues/falcon-7b-instruct-sharded](https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded) 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 180
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "vilsonrodrigues/falcon-7b-instruct-sharded", "model-index": [{"name": "falcon7binstruct_mentalhealthmodel_oct23", "results": []}]} | null | RajChejara/falcon7binstruct_mentalhealthmodel_oct23 | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:vilsonrodrigues/falcon-7b-instruct-sharded",
"license:apache-2.0",
"region:us"
] | 2024-02-14T08:34:30+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-vilsonrodrigues/falcon-7b-instruct-sharded #license-apache-2.0 #region-us
|
# falcon7binstruct_mentalhealthmodel_oct23
This model is a fine-tuned version of vilsonrodrigues/falcon-7b-instruct-sharded 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 180
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1 | [
"# falcon7binstruct_mentalhealthmodel_oct23\n\nThis model is a fine-tuned version of vilsonrodrigues/falcon-7b-instruct-sharded 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: 0.0002\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- training_steps: 180\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-vilsonrodrigues/falcon-7b-instruct-sharded #license-apache-2.0 #region-us \n",
"# falcon7binstruct_mentalhealthmodel_oct23\n\nThis model is a fine-tuned version of vilsonrodrigues/falcon-7b-instruct-sharded 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: 0.0002\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- training_steps: 180\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
61,
49,
6,
12,
8,
3,
141,
4,
39
] | [
"passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-vilsonrodrigues/falcon-7b-instruct-sharded #license-apache-2.0 #region-us \n# falcon7binstruct_mentalhealthmodel_oct23\n\nThis model is a fine-tuned version of vilsonrodrigues/falcon-7b-instruct-sharded 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: 0.0002\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- training_steps: 180\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
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null | null | transformers | # Chinese MentalBERT, a pre-trained language model specifically designed for mental tasks.
In this study, we employ a domain-adaptive pretraining model, and introduce a novel lexicon guided masking machanism strategy based on the Chinese depression lexicon.
## How to use
```bash
from transformers import BertTokenizer, BertForMaskedLM
tokenizer = BertTokenizer.from_pretrained('zwzzz/Chinese-MentalBERT')
model = BertForMaskedLM.from_pretrained('zwzzz/Chinese-MentalBERT')
```
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
Article address：[https://arxiv.org/pdf/2402.09151.pdf](https://arxiv.org/pdf/2402.09151.pdf)
```bash
@misc{zhai2024chinese,
title={Chinese MentalBERT: Domain-Adaptive Pre-training on Social Media for Chinese Mental Health Text Analysis},
author={Wei Zhai and Hongzhi Qi and Qing Zhao and Jianqiang Li and Ziqi Wang and Han Wang and Bing Xiang Yang and Guanghui Fu},
year={2024},
eprint={2402.09151},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | {"language": ["zh"], "license": "apache-2.0"} | fill-mask | zwzzz/Chinese-MentalBERT | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"zh",
"arxiv:2402.09151",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T08:39:26+00:00 | [
"2402.09151"
] | [
"zh"
] | TAGS
#transformers #pytorch #bert #fill-mask #zh #arxiv-2402.09151 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # Chinese MentalBERT, a pre-trained language model specifically designed for mental tasks.
In this study, we employ a domain-adaptive pretraining model, and introduce a novel lexicon guided masking machanism strategy based on the Chinese depression lexicon.
## How to use
If you find the technical report or resource is useful, please cite the following technical report in your paper.
Article address：URL
| [
"# Chinese MentalBERT, a pre-trained language model specifically designed for mental tasks.\n\nIn this study, we employ a domain-adaptive pretraining model, and introduce a novel lexicon guided masking machanism strategy based on the Chinese depression lexicon.",
"## How to use\n\n\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n\nArticle address：URL"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #zh #arxiv-2402.09151 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Chinese MentalBERT, a pre-trained language model specifically designed for mental tasks.\n\nIn this study, we employ a domain-adaptive pretraining model, and introduce a novel lexicon guided masking machanism strategy based on the Chinese depression lexicon.",
"## How to use\n\n\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n\nArticle address：URL"
] | [
54,
59,
29
] | [
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #zh #arxiv-2402.09151 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Chinese MentalBERT, a pre-trained language model specifically designed for mental tasks.\n\nIn this study, we employ a domain-adaptive pretraining model, and introduce a novel lexicon guided masking machanism strategy based on the Chinese depression lexicon.## How to use\n\n\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n\nArticle address：URL"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | mahiatlinux/redpajama_mahibot | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-14T08:43:55+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
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"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"### Model Architecture and Objective",
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] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"### 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 #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "250.16 +/- 12.93", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Kamaljp/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-14T08:45:37+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
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
Configs
```
name: llama
model:
pretrained_model_name_or_path: 'mistralai/Mistral-7B-v0.1'
cache_dir: '/juice/scr/scr110/scr/nlp/data/neo/hub/'
return_dict: true
quantization: false
device_map: auto # null
low_cpu_mem_usage: true # false
torch_dtype: bfloat16
attn_implementation: eager # so we can load attention weights
rope_theta: 10000.0
attention:
attention_type: hedgehog_llama
feature_map: softmax_dim
feature_map_kwargs:
input_dim: 128
eps: 1e-12
# mlp: null # to set
fullspace: true
layer_idx: null # to set
learned_kernel: untied_head
learned_kernel_kwargs:
feature_dim: 128
skip_connection: false
bias: false
zero_init: false
tie_qk_kernels: false
train_qk: true
peft:
method: lora
kwargs:
r: 8 # 256
lora_alpha: 16 # 512
lora_dropout: 0.1 # 0.05
target_modules: ['self_attn.q_proj', 'self_attn.k_proj']
dataset:
name: alpaca_clean
dataset_config:
name: alpaca
path: yahma/alpaca-cleaned
chunk_size: 1024 # 2048
concat_data: true
cache_dir: '/u/scr/nlp/data/alpaca'
pretrained_model_config:
pretrained_model_name_or_path: 'mistralai/Mistral-7B-v0.1'
cache_dir: '/juice/scr/scr110/scr/nlp/data/neo/hub/'
preprocess_config: null
dataloader:
batch_size: 1
num_workers: 2
drop_last: false
pin_memory: true
optimizer:
optim: adamw_torch_fused
lr: 0.001
weight_decay: 0.0
lr_scheduler:
lr_scheduler_type: reduce_lr_on_plateau
mode: min
factor: 0.1
patience: 10
min_lr: 0.00001
trainer: # HuggingFace Trainer-like arguments
name: distill_attention
token_reduce: true
bottom_attention_only: false
reverse_kl: false
bf16: true
train_split: train
val_split: validation
num_train_epochs: 2
gradient_accumulation_steps: 8
seed: 42
batch_size: 1
load_best_model_at_end: true
greater_is_better: false
metric_for_best_model: distill/eval/loss
logging_steps: 100
evaluation_strategy: steps
max_steps: -1
eval_steps: 100
max_eval_batches: null
dataset:
name: alpaca_clean
dataset_config:
name: alpaca
path: yahma/alpaca-cleaned
chunk_size: 1024 # 2048
concat_data: true
cache_dir: '/u/scr/nlp/data/alpaca'
pretrained_model_config:
pretrained_model_name_or_path: 'mistralai/Mistral-7B-v0.1'
cache_dir: '/juice/scr/scr110/scr/nlp/data/neo/hub/'
preprocess_config: null
dataloader:
batch_size: 1
num_workers: 2
drop_last: false
pin_memory: true
optimizer:
optim: adamw_torch_fused
lr: 1e-4
weight_decay: 0.0
lr_scheduler:
lr_scheduler_type: reduce_lr_on_plateau
mode: min
factor: 0.1
patience: 10
min_lr: 0.00001
trainer: # HuggingFace Trainer-like arguments
name: default
bf16: true
train_split: train
val_split: validation
num_train_epochs: 2
gradient_accumulation_steps: 8
seed: 42
batch_size: 1
load_best_model_at_end: true
greater_is_better: false
metric_for_best_model: eval/loss # eval/rouge/geometric_mean
logging_steps: 100
evaluation_strategy: steps
max_steps: -1
eval_steps: 100
max_eval_batches: null
finetune:
method: lora
kwargs:
r: 8
lora_alpha: 16 # 32
lora_dropout: 0 # 0.05
target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj']
```
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"license": "apache-2.0", "datasets": ["yahma/alpaca-cleaned"]} | text-generation | mzio/hedgehog-mistral_7b-alpaca_clean-smd_lora_1e_3 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"dataset:yahma/alpaca-cleaned",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T08:49:46+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #dataset-yahma/alpaca-cleaned #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Model Card for Model ID
This modelcard aims to be a base template for new models. It has been generated using this raw template.
## Model Details
Configs
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.",
"## Model Details\n\nConfigs",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
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"#### 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 #dataset-yahma/alpaca-cleaned #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.",
"## Model Details\n\nConfigs",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #mistral #text-generation #dataset-yahma/alpaca-cleaned #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.## Model Details\n\nConfigs### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | IBM-DTT/sap_hana_ds_FT_Model_10k_includes_0.1 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
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"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Uses
### Direct Use
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### Out-of-Scope Use
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### Recommendations
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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### Training Procedure
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- 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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## Technical Specifications [optional]
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### Compute Infrastructure
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## Glossary [optional]
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null | null | diffusers | ### My-Pet-Cat Dreambooth model trained by Muthuranjani following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: GoX19932gAS
Sample pictures of this concept:
.jpg)
| {"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]} | text-to-image | Muthran/My-Pet-CAT | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-14T08:53:05+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-Cat Dreambooth model trained by Muthuranjani following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: GoX19932gAS
Sample pictures of this concept:
!0.jpg)
| [
"### My-Pet-Cat Dreambooth model trained by Muthuranjani following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: GoX19932gAS\n\nSample pictures of this concept:\n\n !0.jpg)"
] | [
"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-Cat Dreambooth model trained by Muthuranjani following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: GoX19932gAS\n\nSample pictures of this concept:\n\n !0.jpg)"
] | [
73,
58
] | [
"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-Cat Dreambooth model trained by Muthuranjani following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: GoX19932gAS\n\nSample pictures of this concept:\n\n !0.jpg)"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v0.1"} | null | sajjadamjad/quiz_llm_tinyllama | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v0.1",
"region:us"
] | 2024-02-14T09:00:52+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v0.1 #region-us
|
# Model Card for Model ID
## Model Details
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- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
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[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
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## Model Card Contact
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec-reptiles
This model is a fine-tuned version of [vitouphy/wav2vec2-xls-r-300m-english](https://huggingface.co/vitouphy/wav2vec2-xls-r-300m-english) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2223.3787
- Pcc Accuracy: 0.2187
- Pcc Fluency: 0.0834
- Pcc Total Score: 0.1532
- Pcc Content: 0.2235
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.4
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pcc Accuracy | Pcc Fluency | Pcc Total Score | Pcc Content |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:-----------:|:---------------:|:-----------:|
| 2728.7846 | 5.0 | 100 | 3196.0576 | 0.3476 | -0.2327 | -0.3110 | 0.2340 |
| 2491.7434 | 10.0 | 200 | 2875.6025 | 0.2791 | -0.0388 | -0.0724 | 0.2475 |
| 1926.2301 | 15.0 | 300 | 2480.8772 | 0.2280 | 0.0499 | 0.1131 | 0.2334 |
| 2065.1381 | 20.0 | 400 | 2265.0391 | 0.2201 | 0.0799 | 0.1478 | 0.2238 |
| 1903.073 | 25.0 | 500 | 2223.3787 | 0.2187 | 0.0834 | 0.1532 | 0.2235 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "vitouphy/wav2vec2-xls-r-300m-english", "model-index": [{"name": "wav2vec-reptiles", "results": []}]} | null | arslanarjumand/wav2vec-reptiles | [
"transformers",
"safetensors",
"wav2vec2",
"generated_from_trainer",
"base_model:vitouphy/wav2vec2-xls-r-300m-english",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-14T09:01:54+00:00 | [] | [] | TAGS
#transformers #safetensors #wav2vec2 #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec-reptiles
================
This model is a fine-tuned version of vitouphy/wav2vec2-xls-r-300m-english on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2223.3787
* Pcc Accuracy: 0.2187
* Pcc Fluency: 0.0834
* Pcc Total Score: 0.1532
* Pcc Content: 0.2235
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 6
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.4
* num\_epochs: 25
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.0
* Pytorch 2.1.2
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 6\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.4\n* num\\_epochs: 25\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #wav2vec2 #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #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: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 6\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.4\n* num\\_epochs: 25\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
66,
161,
4,
30
] | [
"passage: TAGS\n#transformers #safetensors #wav2vec2 #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #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: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 6\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.4\n* num\\_epochs: 25\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | l3loom/openai-whisper-large-v2-LORA | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-14T09:02:41+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
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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
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- Training regime:
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## Evaluation
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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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"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | diffusers |
# Model Card for Model ID
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This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
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| {"library_name": "diffusers"} | null | kvpratama/ddpm-pokemon-128 | [
"diffusers",
"arxiv:1910.09700",
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"1910.09700"
] | [] | TAGS
#diffusers #arxiv-1910.09700 #diffusers-UNet2DModel #region-us
|
# 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:
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- 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
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APA:
## Glossary [optional]
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null | null | transformers |
# Model Card for Model ID
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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| {"library_name": "transformers", "tags": []} | text-generation | ssoh/llama-2-7b-mcq_2_beta | [
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# Model Card for Model ID
## Model Details
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## Uses
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## How to Get Started with the Model
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## Training Details
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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APA:
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null | null | transformers |
# Model Card for Mistral-7B-Instruct-v0.2
The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an improved instruct fine-tuned version of [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1).
For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/la-plateforme/).
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
```
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]"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "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!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## Troubleshooting
- If you see the following error:
```
Traceback (most recent call last):
File "", line 1, in
File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
config_class = CONFIG_MAPPING[config_dict["model_type"]]
File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
raise KeyError(key)
KeyError: 'mistral'
```
Installing transformers from source should solve the issue
pip install git+https://github.com/huggingface/transformers
This should not be required after transformers-v4.33.4.
## Limitations
The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. | {"license": "apache-2.0", "tags": ["finetuned"], "pipeline_tag": "text-generation", "inference": false} | text-generation | moc1pher/mistral-orient-fine-tune-2 | [
"transformers",
"pytorch",
"safetensors",
"mistral",
"text-generation",
"finetuned",
"conversational",
"arxiv:2310.06825",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T09:11:27+00:00 | [
"2310.06825"
] | [] | TAGS
#transformers #pytorch #safetensors #mistral #text-generation #finetuned #conversational #arxiv-2310.06825 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
|
# Model Card for Mistral-7B-Instruct-v0.2
The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an improved instruct fine-tuned version of Mistral-7B-Instruct-v0.1.
For full details of this model please read our paper and release blog post.
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
This format is available as a chat template via the 'apply_chat_template()' method:
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## Troubleshooting
- If you see the following error:
Installing transformers from source should solve the issue
pip install git+URL
This should not be required after transformers-v4.33.4.
## Limitations
The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. | [
"# Model Card for Mistral-7B-Instruct-v0.2\n\nThe Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an improved instruct fine-tuned version of Mistral-7B-Instruct-v0.1.\n\nFor full details of this model please read our paper and release blog post.",
"## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:",
"## Model Architecture\nThis instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer",
"## Troubleshooting\n- If you see the following error:\n\n\nInstalling transformers from source should solve the issue\npip install git+URL\n\nThis should not be required after transformers-v4.33.4.",
"## Limitations\n\nThe Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. \nIt does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to\nmake the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.",
"## The Mistral AI Team\n\nAlbert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed."
] | [
"TAGS\n#transformers #pytorch #safetensors #mistral #text-generation #finetuned #conversational #arxiv-2310.06825 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n",
"# Model Card for Mistral-7B-Instruct-v0.2\n\nThe Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an improved instruct fine-tuned version of Mistral-7B-Instruct-v0.1.\n\nFor full details of this model please read our paper and release blog post.",
"## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:",
"## Model Architecture\nThis instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer",
"## Troubleshooting\n- If you see the following error:\n\n\nInstalling transformers from source should solve the issue\npip install git+URL\n\nThis should not be required after transformers-v4.33.4.",
"## Limitations\n\nThe Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. \nIt does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to\nmake the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.",
"## The Mistral AI Team\n\nAlbert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed."
] | [
68,
70,
105,
56,
42,
85,
125
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #mistral #text-generation #finetuned #conversational #arxiv-2310.06825 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n# Model Card for Mistral-7B-Instruct-v0.2\n\nThe Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an improved instruct fine-tuned version of Mistral-7B-Instruct-v0.1.\n\nFor full details of this model please read our paper and release blog post.## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:## Model Architecture\nThis instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer## Troubleshooting\n- If you see the following error:\n\n\nInstalling transformers from source should solve the issue\npip install git+URL\n\nThis should not be required after transformers-v4.33.4.## Limitations\n\nThe Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. \nIt does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to\nmake the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs."
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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. -->
# long-t5-tglobal-base-boardpapers-4096
This model is a fine-tuned version of [RMWeerasinghe/long-t5-tglobal-base-finetuned-govReport-4096](https://huggingface.co/RMWeerasinghe/long-t5-tglobal-base-finetuned-govReport-4096) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5356
- Rouge1: 0.0844
- Rouge2: 0.0543
- Rougel: 0.0716
- Rougelsum: 0.0842
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 0.67 | 1 | 0.6583 | 0.0647 | 0.03 | 0.0504 | 0.0595 |
| No log | 2.0 | 3 | 0.6232 | 0.067 | 0.036 | 0.0527 | 0.0643 |
| No log | 2.67 | 4 | 0.6134 | 0.067 | 0.036 | 0.0527 | 0.0643 |
| No log | 4.0 | 6 | 0.5971 | 0.0742 | 0.0426 | 0.0654 | 0.0735 |
| No log | 4.67 | 7 | 0.5897 | 0.0765 | 0.0462 | 0.0654 | 0.0762 |
| No log | 6.0 | 9 | 0.5777 | 0.0803 | 0.0486 | 0.0665 | 0.0802 |
| No log | 6.67 | 10 | 0.5729 | 0.0813 | 0.0498 | 0.0677 | 0.0801 |
| No log | 8.0 | 12 | 0.5652 | 0.0813 | 0.0498 | 0.0677 | 0.0801 |
| No log | 8.67 | 13 | 0.5622 | 0.0823 | 0.0544 | 0.0685 | 0.0811 |
| No log | 10.0 | 15 | 0.5575 | 0.0823 | 0.0544 | 0.0685 | 0.0811 |
| No log | 10.67 | 16 | 0.5559 | 0.0823 | 0.0544 | 0.0685 | 0.0811 |
| No log | 12.0 | 18 | 0.5528 | 0.0823 | 0.0544 | 0.0685 | 0.0811 |
| No log | 12.67 | 19 | 0.5513 | 0.0823 | 0.0544 | 0.0685 | 0.0811 |
| 0.7235 | 14.0 | 21 | 0.5488 | 0.0823 | 0.0544 | 0.0685 | 0.0811 |
| 0.7235 | 14.67 | 22 | 0.5476 | 0.0811 | 0.0544 | 0.0674 | 0.0794 |
| 0.7235 | 16.0 | 24 | 0.5451 | 0.086 | 0.0574 | 0.074 | 0.0841 |
| 0.7235 | 16.67 | 25 | 0.5438 | 0.086 | 0.0574 | 0.074 | 0.0841 |
| 0.7235 | 18.0 | 27 | 0.5420 | 0.086 | 0.0574 | 0.074 | 0.0841 |
| 0.7235 | 18.67 | 28 | 0.5412 | 0.086 | 0.0574 | 0.074 | 0.0841 |
| 0.7235 | 20.0 | 30 | 0.5397 | 0.086 | 0.0574 | 0.074 | 0.0841 |
| 0.7235 | 20.67 | 31 | 0.5390 | 0.086 | 0.0574 | 0.074 | 0.0841 |
| 0.7235 | 22.0 | 33 | 0.5377 | 0.0844 | 0.0543 | 0.0716 | 0.0842 |
| 0.7235 | 22.67 | 34 | 0.5372 | 0.0844 | 0.0543 | 0.0716 | 0.0842 |
| 0.7235 | 24.0 | 36 | 0.5363 | 0.0844 | 0.0543 | 0.0716 | 0.0842 |
| 0.7235 | 24.67 | 37 | 0.5360 | 0.0844 | 0.0543 | 0.0716 | 0.0842 |
| 0.7235 | 26.0 | 39 | 0.5357 | 0.0844 | 0.0543 | 0.0716 | 0.0842 |
| 0.6478 | 26.67 | 40 | 0.5356 | 0.0844 | 0.0543 | 0.0716 | 0.0842 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.17.0
- Tokenizers 0.15.1 | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "base_model": "RMWeerasinghe/long-t5-tglobal-base-finetuned-govReport-4096", "pipeline_tag": "summarization", "model-index": [{"name": "long-t5-tglobal-base-boardpapers-4096", "results": []}]} | summarization | RMWeerasinghe/long-t5-tglobal-base-boardpapers-4096 | [
"transformers",
"safetensors",
"longt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"base_model:RMWeerasinghe/long-t5-tglobal-base-finetuned-govReport-4096",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T09:14:14+00:00 | [] | [] | TAGS
#transformers #safetensors #longt5 #text2text-generation #summarization #generated_from_trainer #base_model-RMWeerasinghe/long-t5-tglobal-base-finetuned-govReport-4096 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| long-t5-tglobal-base-boardpapers-4096
=====================================
This model is a fine-tuned version of RMWeerasinghe/long-t5-tglobal-base-finetuned-govReport-4096 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5356
* Rouge1: 0.0844
* Rouge2: 0.0543
* Rougel: 0.0716
* Rougelsum: 0.0842
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: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40
### Training results
### Framework versions
* Transformers 4.37.0
* Pytorch 2.1.2
* 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 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#transformers #safetensors #longt5 #text2text-generation #summarization #generated_from_trainer #base_model-RMWeerasinghe/long-t5-tglobal-base-finetuned-govReport-4096 #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: 4e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40### Training results### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers |
Where does it come from?
Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32.
The standard chunk of Lorem Ipsum used since the 1500s is reproduced below for those interested. Sections 1.10.32 and 1.10.33 from "de Finibus Bonorum et Malorum" by Cicero are also reproduced in their exact original form, accompanied by English versions from the 1914 translation by H. Rackham. | {"license": "apache-2.0"} | text-generation | RaduGabriel/MUZD | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T09:15:59+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Where does it come from?
Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32.
The standard chunk of Lorem Ipsum used since the 1500s is reproduced below for those interested. Sections 1.10.32 and 1.10.33 from "de Finibus Bonorum et Malorum" by Cicero are also reproduced in their exact original form, accompanied by English versions from the 1914 translation by H. Rackham. | [] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
59
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_distil_huner_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the transformer_dataset_ner_kaggle 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: 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: 50
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["transformer_dataset_ner_kaggle"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "my_awesome_distil_huner_model", "results": []}]} | token-classification | Balu94pratap/my_awesome_distil_huner_model | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:transformer_dataset_ner_kaggle",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T09:16:35+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #dataset-transformer_dataset_ner_kaggle #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# my_awesome_distil_huner_model
This model is a fine-tuned version of distilbert-base-uncased on the transformer_dataset_ner_kaggle 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: 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: 50
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# my_awesome_distil_huner_model\n\nThis model is a fine-tuned version of distilbert-base-uncased on the transformer_dataset_ner_kaggle 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: 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: 50",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.0\n- Pytorch 2.1.2\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #dataset-transformer_dataset_ner_kaggle #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# my_awesome_distil_huner_model\n\nThis model is a fine-tuned version of distilbert-base-uncased on the transformer_dataset_ner_kaggle 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: 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: 50",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.0\n- Pytorch 2.1.2\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
87,
48,
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90,
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] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #dataset-transformer_dataset_ner_kaggle #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# my_awesome_distil_huner_model\n\nThis model is a fine-tuned version of distilbert-base-uncased on the transformer_dataset_ner_kaggle 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: 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: 50### Training results### Framework versions\n\n- Transformers 4.37.0\n- Pytorch 2.1.2\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | automatic-speech-recognition | BlahBlah314/Whisper_LargeV3FR_V3-7 | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #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
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## Environmental Impact
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[optional]
BibTeX:
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"passage: TAGS\n#transformers #safetensors #whisper #automatic-speech-recognition #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 |
## National Climate Targets Classifier - Climate Policy Radar
A multi-label text-classifier trained on the National Climate Targets dataset by Climate Policy Radar.
Using the [climatebert/distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) model as a starting point, this classifier is trained on the [ClimatePolicyRadar/national-climate-targets](https://huggingface.co/datasets/ClimatePolicyRadar/national-climate-targets) dataset to predict Net Zero ("NZT")
, "Reduction" and "Other" targets in a multi-label setting. The training data is an expert annotated subset of national laws, policies and UNFCCC submissions.
For more information on the annotation methodology and classifier training see __our paper TBA__.
## Getting started
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "ClimatePolicyRadar/national-climate-targets"
example = "The Net Zero Strategy, published in October 2021, was the first "\
"document of its kind for a major economy. It set out the government’s "\
"vision for a market-led, technology-driven transition to decarbonise "\
"the UK economy and reach net zero by 2050."
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# using sigmoid because the model is multi-label
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, function_to_apply="sigmoid")
pipe(example)
>>> [{'label': 'NZT', 'score': 0.9142044186592102}]
```
## Licence
Our classifier is licensed as [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
Please read our [Terms of Use](https://app.climatepolicyradar.org/terms-of-use), including any specific terms relevant to commercial use. Contact [email protected] with any questions.
## Links
- __Repository__: [coming soon]
- __Paper__: [coming soon]
## Citation
[coming soon]
## Authors & Contact
Climate Policy Radar team: Matyas Juhasz, Tina Marchand, Roshan Melwani, Kalyan Dutia, Sarah Goodenough, Harrison Pim, and Henry Franks.
[email protected]
https://climatepolicyradar.org
| {"language": ["en"], "license": "apache-2.0", "tags": ["climate"], "datasets": ["ClimatePolicyRadar/national-climate-targets"], "pipeline_tag": "text-classification", "widget": [{"text": "The Net Zero Strategy, published in October 2021, was the first document of its kind for a major economy. It set out the government\u2019s vision for a market-led, technology-driven transition to decarbonise the UK economy and reach net zero by 2050."}], "inference": {"parameters": {"function_to_apply": "sigmoid"}}} | text-classification | ClimatePolicyRadar/national-climate-targets | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"climate",
"en",
"dataset:ClimatePolicyRadar/national-climate-targets",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T09:20:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #climate #en #dataset-ClimatePolicyRadar/national-climate-targets #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## National Climate Targets Classifier - Climate Policy Radar
A multi-label text-classifier trained on the National Climate Targets dataset by Climate Policy Radar.
Using the climatebert/distilroberta-base-climate-f model as a starting point, this classifier is trained on the ClimatePolicyRadar/national-climate-targets dataset to predict Net Zero ("NZT")
, "Reduction" and "Other" targets in a multi-label setting. The training data is an expert annotated subset of national laws, policies and UNFCCC submissions.
For more information on the annotation methodology and classifier training see __our paper TBA__.
## Getting started
## Licence
Our classifier is licensed as Apache 2.0.
Please read our Terms of Use, including any specific terms relevant to commercial use. Contact partners@URL with any questions.
## Links
- __Repository__: [coming soon]
- __Paper__: [coming soon]
[coming soon]
## Authors & Contact
Climate Policy Radar team: Matyas Juhasz, Tina Marchand, Roshan Melwani, Kalyan Dutia, Sarah Goodenough, Harrison Pim, and Henry Franks.
dsci@URL
URL
| [
"## National Climate Targets Classifier - Climate Policy Radar\n\nA multi-label text-classifier trained on the National Climate Targets dataset by Climate Policy Radar.\n\nUsing the climatebert/distilroberta-base-climate-f model as a starting point, this classifier is trained on the ClimatePolicyRadar/national-climate-targets dataset to predict Net Zero (\"NZT\")\n, \"Reduction\" and \"Other\" targets in a multi-label setting. The training data is an expert annotated subset of national laws, policies and UNFCCC submissions.\n\n\nFor more information on the annotation methodology and classifier training see __our paper TBA__.",
"## Getting started",
"## Licence\n\nOur classifier is licensed as Apache 2.0.\n\nPlease read our Terms of Use, including any specific terms relevant to commercial use. Contact partners@URL with any questions."
] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #climate #en #dataset-ClimatePolicyRadar/national-climate-targets #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## National Climate Targets Classifier - Climate Policy Radar\n\nA multi-label text-classifier trained on the National Climate Targets dataset by Climate Policy Radar.\n\nUsing the climatebert/distilroberta-base-climate-f model as a starting point, this classifier is trained on the ClimatePolicyRadar/national-climate-targets dataset to predict Net Zero (\"NZT\")\n, \"Reduction\" and \"Other\" targets in a multi-label setting. The training data is an expert annotated subset of national laws, policies and UNFCCC submissions.\n\n\nFor more information on the annotation methodology and classifier training see __our paper TBA__.",
"## Getting started",
"## Licence\n\nOur classifier is licensed as Apache 2.0.\n\nPlease read our Terms of Use, including any specific terms relevant to commercial use. Contact partners@URL with any questions."
] | [
72,
162,
3,
38
] | [
"passage: TAGS\n#transformers #pytorch #roberta #text-classification #climate #en #dataset-ClimatePolicyRadar/national-climate-targets #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n## National Climate Targets Classifier - Climate Policy Radar\n\nA multi-label text-classifier trained on the National Climate Targets dataset by Climate Policy Radar.\n\nUsing the climatebert/distilroberta-base-climate-f model as a starting point, this classifier is trained on the ClimatePolicyRadar/national-climate-targets dataset to predict Net Zero (\"NZT\")\n, \"Reduction\" and \"Other\" targets in a multi-label setting. The training data is an expert annotated subset of national laws, policies and UNFCCC submissions.\n\n\nFor more information on the annotation methodology and classifier training see __our paper TBA__.## Getting started## Licence\n\nOur classifier is licensed as Apache 2.0.\n\nPlease read our Terms of Use, including any specific terms relevant to commercial use. Contact partners@URL with any questions."
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | daila/whisper-large-v3_LoRA_Common-Vi_WER | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-14T09:22:44+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #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 #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 #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 |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | SuYee189/gpt2-health-qa | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
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#transformers #safetensors #gpt2 #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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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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## Technical Specifications [optional]
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### Compute Infrastructure
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APA:
## Glossary [optional]
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | Imran1/AyaChatM | [
"transformers",
"safetensors",
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"1910.09700"
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#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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### Model Sources [optional]
- Repository:
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- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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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 0.7.1.dev0 | {"library_name": "peft", "base_model": "TheBloke/Mistral-7B-Instruct-v0.2-GPTQ"} | null | seatond/EXTRACTION_rank32_lr2.2e-05_target7_epochs1.7_laplha64_batch1_gradacc4 | [
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ",
"region:us"
] | 2024-02-14T09:24:15+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-TheBloke/Mistral-7B-Instruct-v0.2-GPTQ #region-us
|
# Model Card for Model ID
## Model Details
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## Uses
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## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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## Technical Specifications [optional]
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[optional]
BibTeX:
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## Glossary [optional]
## More Information [optional]
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## Model Card Contact
### Framework versions
- PEFT 0.7.1.dev0 | [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.1.dev0"
] | [
"TAGS\n#peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-TheBloke/Mistral-7B-Instruct-v0.2-GPTQ #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.1.dev0"
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"passage: TAGS\n#peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-TheBloke/Mistral-7B-Instruct-v0.2-GPTQ #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.7.1.dev0"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_qa_model
This model is a fine-tuned version of [timpal0l/mdeberta-v3-base-squad2](https://huggingface.co/timpal0l/mdeberta-v3-base-squad2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5254
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 44 | 1.5503 |
| No log | 2.0 | 88 | 1.5143 |
| No log | 3.0 | 132 | 1.5254 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "timpal0l/mdeberta-v3-base-squad2", "model-index": [{"name": "my_awesome_qa_model", "results": []}]} | question-answering | shahadotb/my_awesome_qa_model | [
"transformers",
"tensorboard",
"safetensors",
"deberta-v2",
"question-answering",
"generated_from_trainer",
"base_model:timpal0l/mdeberta-v3-base-squad2",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-14T09:24:30+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #deberta-v2 #question-answering #generated_from_trainer #base_model-timpal0l/mdeberta-v3-base-squad2 #license-mit #endpoints_compatible #region-us
| my\_awesome\_qa\_model
======================
This model is a fine-tuned version of timpal0l/mdeberta-v3-base-squad2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5254
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
73,
98,
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"passage: TAGS\n#transformers #tensorboard #safetensors #deberta-v2 #question-answering #generated_from_trainer #base_model-timpal0l/mdeberta-v3-base-squad2 #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: NousResearch/Llama-2-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true
load_in_8bit: false
load_in_4bit: true
strict: false
# datasets:
# - path: mhenrichsen/alpaca_2k_test
# type: alpaca
# dataset_prepared_path:
# val_set_size: 0.05
datasets:
- path: /home/ubuntu/Project_Files/Finetune/Data/json_files/combined_sentences.json
type: completion
ds_type: json
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./qlora-out_2
adapter: qlora
lora_model_dir:
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 2
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 10
eval_table_size:
saves_per_epoch: 2
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
```
</details><br>
# qlora-out_2
This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5346
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7065 | 0.0 | 1 | 3.7244 |
| 0.6608 | 0.1 | 95 | 0.5627 |
| 0.6181 | 0.2 | 190 | 0.5419 |
| 0.6037 | 0.3 | 285 | 0.5333 |
| 0.5919 | 0.4 | 380 | 0.5290 |
| 0.5845 | 0.5 | 475 | 0.5295 |
| 0.5779 | 0.6 | 570 | 0.5274 |
| 0.5754 | 0.7 | 665 | 0.5292 |
| 0.5724 | 0.8 | 760 | 0.5300 |
| 0.5702 | 0.9 | 855 | 0.5256 |
| 0.5662 | 1.0 | 950 | 0.5284 |
| 0.5665 | 1.09 | 1045 | 0.5313 |
| 0.5643 | 1.19 | 1140 | 0.5325 |
| 0.5599 | 1.29 | 1235 | 0.5291 |
| 0.5607 | 1.39 | 1330 | 0.5318 |
| 0.5584 | 1.49 | 1425 | 0.5323 |
| 0.5574 | 1.59 | 1520 | 0.5324 |
| 0.5568 | 1.69 | 1615 | 0.5329 |
| 0.5586 | 1.8 | 1710 | 0.5346 |
| 0.5572 | 1.9 | 1805 | 0.5346 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0 | {"library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "NousResearch/Llama-2-7b-hf", "model-index": [{"name": "qlora-out_2", "results": []}]} | null | rajeev-dw9/med_llama | [
"peft",
"tensorboard",
"safetensors",
"llama",
"generated_from_trainer",
"base_model:NousResearch/Llama-2-7b-hf",
"has_space",
"4-bit",
"region:us"
] | 2024-02-14T09:25:14+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #llama #generated_from_trainer #base_model-NousResearch/Llama-2-7b-hf #has_space #4-bit #region-us
| <img src="URL alt="Built with Axolotl" width="200" height="32"/>
See axolotl config
axolotl version: '0.4.0'
qlora-out\_2
============
This model is a fine-tuned version of NousResearch/Llama-2-7b-hf on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5346
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 4
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 10
* num\_epochs: 2
### Training results
### Framework versions
* PEFT 0.8.2
* Transformers 4.38.0.dev0
* Pytorch 2.1.2+cu121
* Datasets 2.16.1
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_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\\_steps: 10\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
"TAGS\n#peft #tensorboard #safetensors #llama #generated_from_trainer #base_model-NousResearch/Llama-2-7b-hf #has_space #4-bit #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_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\\_steps: 10\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
53,
178,
4,
44
] | [
"passage: TAGS\n#peft #tensorboard #safetensors #llama #generated_from_trainer #base_model-NousResearch/Llama-2-7b-hf #has_space #4-bit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_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\\_steps: 10\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | Quzovsky/Maral | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
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"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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] |
null | null | transformers |
This is a 4-bit BitsAndBytes quantized version of ruGPT-3.5 13B, based on [Gaivoronsky/ruGPT-3.5-13B-fp16](https://huggingface.co/Gaivoronsky/ruGPT-3.5-13B-fp16) | {"language": ["en", "ru"], "license": "mit", "library_name": "transformers", "tags": ["gpt3", "transformers", "pytorch", "text-generation-inference", "bitsandbytes"], "pipeline_tag": "text-generation"} | text-generation | WaveCut/ruGPT-3.5-13B-4bit-bnb | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"gpt3",
"pytorch",
"text-generation-inference",
"bitsandbytes",
"en",
"ru",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
] | 2024-02-14T09:28:36+00:00 | [] | [
"en",
"ru"
] | TAGS
#transformers #safetensors #gpt2 #text-generation #gpt3 #pytorch #text-generation-inference #bitsandbytes #en #ru #license-mit #autotrain_compatible #endpoints_compatible #4-bit #region-us
|
This is a 4-bit BitsAndBytes quantized version of ruGPT-3.5 13B, based on Gaivoronsky/ruGPT-3.5-13B-fp16 | [] | [
"TAGS\n#transformers #safetensors #gpt2 #text-generation #gpt3 #pytorch #text-generation-inference #bitsandbytes #en #ru #license-mit #autotrain_compatible #endpoints_compatible #4-bit #region-us \n"
] | [
73
] | [
"passage: TAGS\n#transformers #safetensors #gpt2 #text-generation #gpt3 #pytorch #text-generation-inference #bitsandbytes #en #ru #license-mit #autotrain_compatible #endpoints_compatible #4-bit #region-us \n"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MeMo_BERT-SA
This model is a fine-tuned version of [MiMe-MeMo/MeMo-BERT-03](https://huggingface.co/MiMe-MeMo/MeMo-BERT-03) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5938
- F1-score: 0.7727
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1-score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 265 | 0.5938 | 0.7727 |
| 0.591 | 2.0 | 530 | 0.9487 | 0.7515 |
| 0.591 | 3.0 | 795 | 1.1555 | 0.7375 |
| 0.1867 | 4.0 | 1060 | 1.2892 | 0.7773 |
| 0.1867 | 5.0 | 1325 | 1.3791 | 0.7929 |
| 0.0582 | 6.0 | 1590 | 1.5941 | 0.7810 |
| 0.0582 | 7.0 | 1855 | 1.8173 | 0.7751 |
| 0.0166 | 8.0 | 2120 | 1.7725 | 0.7885 |
| 0.0166 | 9.0 | 2385 | 1.7669 | 0.7939 |
| 0.0102 | 10.0 | 2650 | 1.7915 | 0.7933 |
| 0.0102 | 11.0 | 2915 | 1.9139 | 0.7848 |
| 0.0088 | 12.0 | 3180 | 1.9446 | 0.7816 |
| 0.0088 | 13.0 | 3445 | 1.9794 | 0.7793 |
| 0.0124 | 14.0 | 3710 | 1.9904 | 0.7946 |
| 0.0124 | 15.0 | 3975 | 2.0188 | 0.7831 |
| 0.0095 | 16.0 | 4240 | 2.0517 | 0.7850 |
| 0.0001 | 17.0 | 4505 | 2.0427 | 0.7793 |
| 0.0001 | 18.0 | 4770 | 2.0205 | 0.7902 |
| 0.0 | 19.0 | 5035 | 2.0280 | 0.7847 |
| 0.0 | 20.0 | 5300 | 2.0466 | 0.7793 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"language": "da", "tags": ["generated_from_trainer"], "base_model": "MiMe-MeMo/MeMo-BERT-03", "widget": [{"text": "Men endnu sad hun , bleg og sk\u00e6lvende , ved vinduet og lyttede med r\u00e6dsel i sit blikat en den urolige i de !"}], "model-index": [{"name": "MeMo_BERT-SA", "results": []}]} | text-classification | MiMe-MeMo/MeMo_BERT-SA | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"da",
"base_model:MiMe-MeMo/MeMo-BERT-03",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T09:30:41+00:00 | [] | [
"da"
] | TAGS
#transformers #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #da #base_model-MiMe-MeMo/MeMo-BERT-03 #autotrain_compatible #endpoints_compatible #region-us
| MeMo\_BERT-SA
=============
This model is a fine-tuned version of MiMe-MeMo/MeMo-BERT-03 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5938
* F1-score: 0.7727
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
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"### Training results",
"### 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: 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: 20",
"### 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"
] | [
71,
98,
4,
33
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"passage: TAGS\n#transformers #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #da #base_model-MiMe-MeMo/MeMo-BERT-03 #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: 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: 20### 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 | diffusers |
# InterDiffusion-3
- Size: 6.9 GB
- Text: Partly
- Prompt: 150 words - sentences
- Base: InterDiffusion-2.5
- Demo: [https://discord.gg/jBgU949BJY](https://discord.gg/jBgU949BJY)
## Examples
### Example 1
**Prompt:**
a vivid image of a surreal landscape where the sky is a blend of colors, and floating islands are surrounded by mist
**Result:**
### Example 2
**Prompt:**
imaginative scene featuring a futuristic cityscape with towering skyscrapers, flying cars, and neon lights illuminating the night
**Result:**
| {"license": "mit", "library_name": "diffusers", "tags": ["diffusion", "free", "freeai", "ai", "interdiffusion"], "datasets": ["wanng/midjourney-v5-202304-clean", "ShoukanLabs/LAION-DallE-3-Local", "kakaobrain/coyo-700m", "YaYaB/onepiece-blip-captions"], "pipeline_tag": "text-to-image"} | text-to-image | cutycat2000x/InterDiffusion-3 | [
"diffusers",
"safetensors",
"diffusion",
"free",
"freeai",
"ai",
"interdiffusion",
"text-to-image",
"dataset:wanng/midjourney-v5-202304-clean",
"dataset:ShoukanLabs/LAION-DallE-3-Local",
"dataset:kakaobrain/coyo-700m",
"dataset:YaYaB/onepiece-blip-captions",
"license:mit",
"region:us"
] | 2024-02-14T09:36:20+00:00 | [] | [] | TAGS
#diffusers #safetensors #diffusion #free #freeai #ai #interdiffusion #text-to-image #dataset-wanng/midjourney-v5-202304-clean #dataset-ShoukanLabs/LAION-DallE-3-Local #dataset-kakaobrain/coyo-700m #dataset-YaYaB/onepiece-blip-captions #license-mit #region-us
|
# InterDiffusion-3
- Size: 6.9 GB
- Text: Partly
- Prompt: 150 words - sentences
- Base: InterDiffusion-2.5
- Demo: URL
## Examples
### Example 1
Prompt:
a vivid image of a surreal landscape where the sky is a blend of colors, and floating islands are surrounded by mist
Result:
!image/png
### Example 2
Prompt:
imaginative scene featuring a futuristic cityscape with towering skyscrapers, flying cars, and neon lights illuminating the night
Result:
!image/png
| [
"# InterDiffusion-3\n- Size: 6.9 GB\n- Text: Partly\n- Prompt: 150 words - sentences\n- Base: InterDiffusion-2.5\n- Demo: URL",
"## Examples",
"### Example 1\n\nPrompt:\na vivid image of a surreal landscape where the sky is a blend of colors, and floating islands are surrounded by mist\n\nResult:\n!image/png",
"### Example 2\n\nPrompt:\nimaginative scene featuring a futuristic cityscape with towering skyscrapers, flying cars, and neon lights illuminating the night\n\nResult:\n!image/png"
] | [
"TAGS\n#diffusers #safetensors #diffusion #free #freeai #ai #interdiffusion #text-to-image #dataset-wanng/midjourney-v5-202304-clean #dataset-ShoukanLabs/LAION-DallE-3-Local #dataset-kakaobrain/coyo-700m #dataset-YaYaB/onepiece-blip-captions #license-mit #region-us \n",
"# InterDiffusion-3\n- Size: 6.9 GB\n- Text: Partly\n- Prompt: 150 words - sentences\n- Base: InterDiffusion-2.5\n- Demo: URL",
"## Examples",
"### Example 1\n\nPrompt:\na vivid image of a surreal landscape where the sky is a blend of colors, and floating islands are surrounded by mist\n\nResult:\n!image/png",
"### Example 2\n\nPrompt:\nimaginative scene featuring a futuristic cityscape with towering skyscrapers, flying cars, and neon lights illuminating the night\n\nResult:\n!image/png"
] | [
107,
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3,
44,
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] | [
"passage: TAGS\n#diffusers #safetensors #diffusion #free #freeai #ai #interdiffusion #text-to-image #dataset-wanng/midjourney-v5-202304-clean #dataset-ShoukanLabs/LAION-DallE-3-Local #dataset-kakaobrain/coyo-700m #dataset-YaYaB/onepiece-blip-captions #license-mit #region-us \n# InterDiffusion-3\n- Size: 6.9 GB\n- Text: Partly\n- Prompt: 150 words - sentences\n- Base: InterDiffusion-2.5\n- Demo: URL## Examples### Example 1\n\nPrompt:\na vivid image of a surreal landscape where the sky is a blend of colors, and floating islands are surrounded by mist\n\nResult:\n!image/png### Example 2\n\nPrompt:\nimaginative scene featuring a futuristic cityscape with towering skyscrapers, flying cars, and neon lights illuminating the night\n\nResult:\n!image/png"
] | [
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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. -->
# revenue_streams
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- 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 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/flan-t5-small", "model-index": [{"name": "revenue_streams", "results": []}]} | text2text-generation | fliarbi/revenue_streams | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T09:37:46+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/flan-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# revenue_streams
This model is a fine-tuned version of google/flan-t5-small on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- 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 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2",
"### Training results",
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"# revenue_streams\n\nThis model is a fine-tuned version of google/flan-t5-small on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
81,
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"passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/flan-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# revenue_streams\n\nThis model is a fine-tuned version of google/flan-t5-small on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | kenchenxingyu/flan-large-single-label-emotion-human6 | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-14T09:38:11+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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## Evaluation
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"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral_hinglish_instruct_poc
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3546
## 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: 0.0005
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.89 | 4 | 1.9711 |
| 3.4079 | 2.0 | 9 | 1.3071 |
| 1.5848 | 2.89 | 13 | 1.1747 |
| 1.0874 | 4.0 | 18 | 1.1408 |
| 0.7777 | 4.89 | 22 | 1.1872 |
| 0.5225 | 6.0 | 27 | 1.2562 |
| 0.3572 | 6.89 | 31 | 1.3203 |
| 0.2562 | 8.0 | 36 | 1.3472 |
| 0.2202 | 8.89 | 40 | 1.3546 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "mistral_hinglish_instruct_poc", "results": []}]} | null | smangrul/mistral_hinglish_instruct_poc | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
] | 2024-02-14T09:46:11+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-Instruct-v0.2 #license-apache-2.0 #region-us
| mistral\_hinglish\_instruct\_poc
================================
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the generator dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3546
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: 0.0005
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* PEFT 0.8.2
* Transformers 4.38.0.dev0
* Pytorch 2.2.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
"TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-Instruct-v0.2 #license-apache-2.0 #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
64,
159,
4,
44
] | [
"passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-Instruct-v0.2 #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
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null | null | stable-baselines3 |
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.15 +/- 0.06", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | mathreader/a2c-PandaReachDense-v3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-14T09:51:07+00:00 | [] | [] | TAGS
#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing PandaReachDense-v3
This is a trained model of a A2C agent playing PandaReachDense-v3
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
41,
45,
17
] | [
"passage: TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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] |
null | null | null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="AromaticHydrocarbon/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | AromaticHydrocarbon/q-FrozenLake-v1-4x4-noSlippery | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-14T09:51:58+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
40,
39
] | [
"passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
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null | null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
i think the fine tuning process has some issues, i will working in solving them soon ISA.
## Model Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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| {"library_name": "transformers", "tags": []} | text2text-generation | tareky/flan-t5-base-SQuDA-fine-tune-qs | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #t5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
i think the fine tuning process has some issues, i will working in solving them soon ISA.
## 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:
- 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]
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null | null | diffusers | ### MyPetDog Dreambooth model trained by praveen85 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 21KT1A0572
Sample pictures of this concept:
| {"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]} | text-to-image | praveen85/mypetdog | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-14T09:54:45+00:00 | [] | [] | TAGS
#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
| ### MyPetDog Dreambooth model trained by praveen85 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 21KT1A0572
Sample pictures of this concept:
| [
"### MyPetDog Dreambooth model trained by praveen85 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 21KT1A0572\n\nSample pictures of this concept:"
] | [
"TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"### MyPetDog Dreambooth model trained by praveen85 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 21KT1A0572\n\nSample pictures of this concept:"
] | [
73,
52
] | [
"passage: TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n### MyPetDog Dreambooth model trained by praveen85 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 21KT1A0572\n\nSample pictures of this concept:"
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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/MistPhotoCheckTest | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T09:56:55+00:00 | [] | [] | TAGS
#transformers #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 #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"
] | [
60,
29,
3
] | [
"passage: TAGS\n#transformers #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 | peft |
<img src="https://huggingface.co/Menouar/fennec-7b-alpha/resolve/main/fennec.jpg" alt="Fennec Logo" width="800" height="400" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# fennec-7b-beta
**Training in progress! you can check this version [fennec-7b-alpha](https://huggingface.co/Menouar/fennec-7b-alpha)** | {"language": ["en"], "license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer", "pytorch", "Mistral"], "datasets": ["HuggingFaceH4/ultrachat_200k", "openbmb/UltraFeedback", "gsm8k"], "base_model": "mistralai/Mistral-7B-v0.1", "pipeline_tag": "text-generation", "model-index": [{"name": "fennec-7b-beta", "results": []}]} | text-generation | Menouar/fennec-7b-beta | [
"peft",
"trl",
"sft",
"generated_from_trainer",
"pytorch",
"Mistral",
"text-generation",
"en",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:openbmb/UltraFeedback",
"dataset:gsm8k",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | 2024-02-14T09:59:23+00:00 | [] | [
"en"
] | TAGS
#peft #trl #sft #generated_from_trainer #pytorch #Mistral #text-generation #en #dataset-HuggingFaceH4/ultrachat_200k #dataset-openbmb/UltraFeedback #dataset-gsm8k #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us
|
<img src="URL alt="Fennec Logo" width="800" height="400" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# fennec-7b-beta
Training in progress! you can check this version fennec-7b-alpha | [
"# fennec-7b-beta\n\nTraining in progress! you can check this version fennec-7b-alpha"
] | [
"TAGS\n#peft #trl #sft #generated_from_trainer #pytorch #Mistral #text-generation #en #dataset-HuggingFaceH4/ultrachat_200k #dataset-openbmb/UltraFeedback #dataset-gsm8k #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n",
"# fennec-7b-beta\n\nTraining in progress! you can check this version fennec-7b-alpha"
] | [
97,
23
] | [
"passage: TAGS\n#peft #trl #sft #generated_from_trainer #pytorch #Mistral #text-generation #en #dataset-HuggingFaceH4/ultrachat_200k #dataset-openbmb/UltraFeedback #dataset-gsm8k #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n# fennec-7b-beta\n\nTraining in progress! you can check this version fennec-7b-alpha"
] | [
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null | null | transformers |
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# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
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## Evaluation
### Testing Data, Factors & Metrics
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null | null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - Lava26/wagonr
<Gallery />
## Model description
These are Lava26/wagonr LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of wagonr car to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](Lava26/wagonr/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of wagonr car", "widget": []} | text-to-image | Lava26/wagonr | [
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | 2024-02-14T10:04:03+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion-xl #stable-diffusion-xl-diffusers #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us
|
# SDXL LoRA DreamBooth - Lava26/wagonr
<Gallery />
## Model description
These are Lava26/wagonr LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using DreamBooth.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of wagonr car to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | [
"# SDXL LoRA DreamBooth - Lava26/wagonr\n\n<Gallery />",
"## Model description\n\nThese are Lava26/wagonr LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.",
"## Trigger words\n\nYou should use a photo of wagonr car to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Training details\n\n[TODO: describe the data used to train the model]"
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion-xl #stable-diffusion-xl-diffusers #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us \n",
"# SDXL LoRA DreamBooth - Lava26/wagonr\n\n<Gallery />",
"## Model description\n\nThese are Lava26/wagonr LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.",
"## Trigger words\n\nYou should use a photo of wagonr car to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Training details\n\n[TODO: describe the data used to train the model]"
] | [
78,
20,
85,
20,
28,
9,
5,
24,
16
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion-xl #stable-diffusion-xl-diffusers #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us \n# SDXL LoRA DreamBooth - Lava26/wagonr\n\n<Gallery />## Model description\n\nThese are Lava26/wagonr LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.## Trigger words\n\nYou should use a photo of wagonr car to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.## Intended uses & limitations#### How to use#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]## Training details\n\n[TODO: describe the data used to train the model]"
] | [
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null | null | 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 0.7.1 | {"library_name": "peft", "base_model": "distilbert-base-uncased"} | null | Boksosung/my_awesome_qa_model | [
"peft",
"tensorboard",
"safetensors",
"distilbert",
"arxiv:1910.09700",
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"region:us"
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"1910.09700"
] | [] | TAGS
#peft #tensorboard #safetensors #distilbert #arxiv-1910.09700 #base_model-distilbert-base-uncased #region-us
|
# Model Card for Model ID
## Model Details
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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]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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null | null | null |
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="AromaticHydrocarbon/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/- 2.71", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | AromaticHydrocarbon/Taxi-v3 | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-14T10:06:32+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
32,
33
] | [
"passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
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null | null | transformers |
# Model Card for Model ID
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- 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
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
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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. -->
# chat_200STEPS_1e6_01beta
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6840
- Rewards/chosen: -0.0632
- Rewards/rejected: -0.0877
- Rewards/accuracies: 0.4637
- Rewards/margins: 0.0245
- Logps/rejected: -19.6678
- Logps/chosen: -17.3765
- Logits/rejected: -0.6331
- Logits/chosen: -0.6330
## 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-06
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6939 | 0.1 | 50 | 0.6917 | -0.0037 | -0.0069 | 0.4901 | 0.0032 | -18.8600 | -16.7813 | -0.5975 | -0.5973 |
| 0.6902 | 0.2 | 100 | 0.6919 | -0.1261 | -0.1323 | 0.4440 | 0.0063 | -20.1147 | -18.0054 | -0.6143 | -0.6142 |
| 0.691 | 0.29 | 150 | 0.6846 | -0.0911 | -0.1153 | 0.4615 | 0.0242 | -19.9439 | -17.6551 | -0.6419 | -0.6418 |
| 0.6838 | 0.39 | 200 | 0.6840 | -0.0632 | -0.0877 | 0.4637 | 0.0245 | -19.6678 | -17.3765 | -0.6331 | -0.6330 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.0+cu117
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-chat-hf", "model-index": [{"name": "chat_200STEPS_1e6_01beta", "results": []}]} | text-generation | tsavage68/chat_200STEPS_1e6_01beta | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T10:10:13+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| chat\_200STEPS\_1e6\_01beta
===========================
This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6840
* Rewards/chosen: -0.0632
* Rewards/rejected: -0.0877
* Rewards/accuracies: 0.4637
* Rewards/margins: 0.0245
* Logps/rejected: -19.6678
* Logps/chosen: -17.3765
* Logits/rejected: -0.6331
* Logits/chosen: -0.6330
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-06
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 200
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.0.0+cu117
* 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-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 200",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 200",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
80,
144,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 200### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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] |
null | null | transformers |
# Brunhilde-13b
## Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "arlineka/Brunhilde-13b"
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=1024, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
## Alpaca Template
```
### Instruction:
{prompt}
### Response:
``` | {"license": "cc-by-nc-4.0", "tags": ["roleplay", "merge"]} | text-generation | arlineka/Brunhilde-13b | [
"transformers",
"safetensors",
"llama",
"text-generation",
"roleplay",
"merge",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T10:11:20+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #roleplay #merge #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Brunhilde-13b
## Usage
## Alpaca Template
| [
"# Brunhilde-13b",
"## Usage",
"## Alpaca Template"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #roleplay #merge #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Brunhilde-13b",
"## Usage",
"## Alpaca Template"
] | [
65,
6,
3,
4
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #roleplay #merge #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Brunhilde-13b## Usage## Alpaca Template"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | kaushalpowar/llama2_finetuned_4_merged | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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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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### Compute Infrastructure
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## Glossary [optional]
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null | null | transformers |
# bert-base-uncased-finetuned-mrpc-v2
BERT (`"bert-base-uncased"`) finetuned on MRPC (Microsoft Research Paraphrase Corpus).
The model predicts whether two sentences are semantically equivalent. It pertains to section 4 of chapter 3 of the Hugging Face "NLP Course" (https://huggingface.co/learn/nlp-course/chapter3/4).
It was trained using a custom PyTorch loop with Hugging Face Accelerate.
Code: https://github.com/sambitmukherjee/huggingface-notebooks/blob/main/course/en/chapter3/section4.ipynb
Experiment tracking: https://wandb.ai/sadhaklal/bert-base-uncased-finetuned-mrpc-v2
## Usage
```
from transformers import pipeline
classifier = pipeline("text-classification", model="sadhaklal/bert-base-uncased-finetuned-mrpc-v2")
sentence1 = "A tropical storm rapidly developed in the Gulf of Mexico Sunday and was expected to hit somewhere along the Texas or Louisiana coasts by Monday night ."
sentence2 = "A tropical storm rapidly developed in the Gulf of Mexico on Sunday and could have hurricane-force winds when it hits land somewhere along the Louisiana coast Monday night ."
sentence_pair = sentence1 + " [SEP] " + sentence2
print(classifier(sentence_pair))
sentence1 = "The settling companies would also assign their possible claims against the underwriters to the investor plaintiffs , he added ."
sentence2 = "Under the agreement , the settling companies will also assign their potential claims against the underwriters to the investors , he added ."
sentence_pair = sentence1 + " [SEP] " + sentence2
print(classifier(sentence_pair))
```
## Dataset
From the dataset page:
> The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.
Examples: https://huggingface.co/datasets/glue/viewer/mrpc
## Metrics
Accuracy on the 'validation' split of MRPC: 0.875
F1 on the 'validation' split of MRPC: 0.9128
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "datasets": ["glue"], "metrics": ["accuracy", "f1"], "pipeline_tag": "text-classification", "widget": [{"text": "The company didn 't detail the costs of the replacement and repairs . [SEP] But company officials expect the costs of the replacement work to run into the millions of dollars .", "example_title": "not_equivalent"}, {"text": "According to the federal Centers for Disease Control and Prevention ( news - web sites ) , there were 19 reported cases of measles in the United States in 2002 . [SEP] The Centers for Disease Control and Prevention said there were 19 reported cases of measles in the United States in 2002 .", "example_title": "equivalent"}]} | text-classification | sadhaklal/bert-base-uncased-finetuned-mrpc-v2 | [
"transformers",
"safetensors",
"bert",
"text-classification",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T10:14:44+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #bert #text-classification #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-uncased-finetuned-mrpc-v2
BERT ('"bert-base-uncased"') finetuned on MRPC (Microsoft Research Paraphrase Corpus).
The model predicts whether two sentences are semantically equivalent. It pertains to section 4 of chapter 3 of the Hugging Face "NLP Course" (URL
It was trained using a custom PyTorch loop with Hugging Face Accelerate.
Code: URL
Experiment tracking: URL
## Usage
## Dataset
From the dataset page:
> The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.
Examples: URL
## Metrics
Accuracy on the 'validation' split of MRPC: 0.875
F1 on the 'validation' split of MRPC: 0.9128
| [
"# bert-base-uncased-finetuned-mrpc-v2\n\nBERT ('\"bert-base-uncased\"') finetuned on MRPC (Microsoft Research Paraphrase Corpus).\n\nThe model predicts whether two sentences are semantically equivalent. It pertains to section 4 of chapter 3 of the Hugging Face \"NLP Course\" (URL\n\nIt was trained using a custom PyTorch loop with Hugging Face Accelerate.\n\nCode: URL\n\nExperiment tracking: URL",
"## Usage",
"## Dataset\n\nFrom the dataset page:\n\n> The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.\n\nExamples: URL",
"## Metrics\n\nAccuracy on the 'validation' split of MRPC: 0.875\n\nF1 on the 'validation' split of MRPC: 0.9128"
] | [
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"# bert-base-uncased-finetuned-mrpc-v2\n\nBERT ('\"bert-base-uncased\"') finetuned on MRPC (Microsoft Research Paraphrase Corpus).\n\nThe model predicts whether two sentences are semantically equivalent. It pertains to section 4 of chapter 3 of the Hugging Face \"NLP Course\" (URL\n\nIt was trained using a custom PyTorch loop with Hugging Face Accelerate.\n\nCode: URL\n\nExperiment tracking: URL",
"## Usage",
"## Dataset\n\nFrom the dataset page:\n\n> The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.\n\nExamples: URL",
"## Metrics\n\nAccuracy on the 'validation' split of MRPC: 0.875\n\nF1 on the 'validation' split of MRPC: 0.9128"
] | [
53,
111,
3,
65,
39
] | [
"passage: TAGS\n#transformers #safetensors #bert #text-classification #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-uncased-finetuned-mrpc-v2\n\nBERT ('\"bert-base-uncased\"') finetuned on MRPC (Microsoft Research Paraphrase Corpus).\n\nThe model predicts whether two sentences are semantically equivalent. It pertains to section 4 of chapter 3 of the Hugging Face \"NLP Course\" (URL\n\nIt was trained using a custom PyTorch loop with Hugging Face Accelerate.\n\nCode: URL\n\nExperiment tracking: URL## Usage## Dataset\n\nFrom the dataset page:\n\n> The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.\n\nExamples: URL## Metrics\n\nAccuracy on the 'validation' split of MRPC: 0.875\n\nF1 on the 'validation' split of MRPC: 0.9128"
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null | null | transformers | GGUF version for [Test157t/Echidna-7b-128k](https://huggingface.co/Test157t/Echidna-7b-128k)
| {"library_name": "transformers", "pipeline_tag": "text-generation"} | text-generation | konz00/Echidna-7b-128k-GGUF | [
"transformers",
"gguf",
"text-generation",
"endpoints_compatible",
"region:us"
] | 2024-02-14T10:18:12+00:00 | [] | [] | TAGS
#transformers #gguf #text-generation #endpoints_compatible #region-us
| GGUF version for Test157t/Echidna-7b-128k
!URL
!URL
| [] | [
"TAGS\n#transformers #gguf #text-generation #endpoints_compatible #region-us \n"
] | [
25
] | [
"passage: TAGS\n#transformers #gguf #text-generation #endpoints_compatible #region-us \n"
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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. -->
# badokorach/roberta-base-agric-trans-140224
This model is a fine-tuned version of [badokorach/roberta-base-agric-trans-290124](https://huggingface.co/badokorach/roberta-base-agric-trans-290124) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0063
- Validation Loss: 0.0
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3040, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.02}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.0751 | 0.0 | 0 |
| 0.0092 | 0.0 | 1 |
| 0.0082 | 0.0 | 2 |
| 0.0066 | 0.0 | 3 |
| 0.0098 | 0.0 | 4 |
| 0.0063 | 0.0 | 5 |
| 0.0034 | 0.0 | 6 |
| 0.0058 | 0.0 | 7 |
| 0.0053 | 0.0 | 8 |
| 0.0063 | 0.0 | 9 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_keras_callback"], "base_model": "badokorach/roberta-base-agric-trans-290124", "model-index": [{"name": "badokorach/roberta-base-agric-trans-140224", "results": []}]} | question-answering | badokorach/roberta-base-agric-trans-140224 | [
"transformers",
"tf",
"xlm-roberta",
"question-answering",
"generated_from_keras_callback",
"base_model:badokorach/roberta-base-agric-trans-290124",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-14T10:23:04+00:00 | [] | [] | TAGS
#transformers #tf #xlm-roberta #question-answering #generated_from_keras_callback #base_model-badokorach/roberta-base-agric-trans-290124 #license-mit #endpoints_compatible #region-us
| badokorach/roberta-base-agric-trans-140224
==========================================
This model is a fine-tuned version of badokorach/roberta-base-agric-trans-290124 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0063
* Validation Loss: 0.0
* Epoch: 9
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 3040, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.02}
* training\_precision: mixed\_float16
### Training results
### Framework versions
* Transformers 4.35.2
* TensorFlow 2.15.0
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 3040, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.02}\n* training\\_precision: mixed\\_float16",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tf #xlm-roberta #question-answering #generated_from_keras_callback #base_model-badokorach/roberta-base-agric-trans-290124 #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 3040, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.02}\n* training\\_precision: mixed\\_float16",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
69,
231,
4,
31
] | [
"passage: TAGS\n#transformers #tf #xlm-roberta #question-answering #generated_from_keras_callback #base_model-badokorach/roberta-base-agric-trans-290124 #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 3040, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.02}\n* training\\_precision: mixed\\_float16### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | Shreyagnani/roberta-large-peft-p-tuning | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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- Demo [optional]:
## Uses
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### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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] |
null | null | transformers |
# Jaskier-7b-dpo-v4.1
**This is work-in-progress model, may not be ready for production use**
Model based on `paulml/OGNO-7B` (downstream version of Mistral7B) finetuned using Direct Preference Optimization on jondurbin/truthy-dpo-v0.1.
## Changelog
- 2024-02-13: Initial release
## About bards.ai
At bards.ai, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: bards.ai
Let us know if you use our model :). Also, if you need any help, feel free to contact us at [email protected]
| {"language": ["en"], "license": "cc-by-4.0", "library_name": "transformers", "tags": ["llm", "7b"], "datasets": ["jondurbin/truthy-dpo-v0.1"]} | text-generation | bardsai/jaskier-7b-dpo-v4.1 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"llm",
"7b",
"en",
"dataset:jondurbin/truthy-dpo-v0.1",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T10:24:30+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #llm #7b #en #dataset-jondurbin/truthy-dpo-v0.1 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Jaskier-7b-dpo-v4.1
This is work-in-progress model, may not be ready for production use
Model based on 'paulml/OGNO-7B' (downstream version of Mistral7B) finetuned using Direct Preference Optimization on jondurbin/truthy-dpo-v0.1.
## Changelog
- 2024-02-13: Initial release
## About URL
At URL, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: URL
Let us know if you use our model :). Also, if you need any help, feel free to contact us at info@URL
| [
"# Jaskier-7b-dpo-v4.1\n\nThis is work-in-progress model, may not be ready for production use\n\nModel based on 'paulml/OGNO-7B' (downstream version of Mistral7B) finetuned using Direct Preference Optimization on jondurbin/truthy-dpo-v0.1.",
"## Changelog\n\n- 2024-02-13: Initial release",
"## About URL\n\nAt URL, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: URL\n\nLet us know if you use our model :). Also, if you need any help, feel free to contact us at info@URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #llm #7b #en #dataset-jondurbin/truthy-dpo-v0.1 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Jaskier-7b-dpo-v4.1\n\nThis is work-in-progress model, may not be ready for production use\n\nModel based on 'paulml/OGNO-7B' (downstream version of Mistral7B) finetuned using Direct Preference Optimization on jondurbin/truthy-dpo-v0.1.",
"## Changelog\n\n- 2024-02-13: Initial release",
"## About URL\n\nAt URL, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: URL\n\nLet us know if you use our model :). Also, if you need any help, feel free to contact us at info@URL"
] | [
80,
75,
13,
81
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #llm #7b #en #dataset-jondurbin/truthy-dpo-v0.1 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Jaskier-7b-dpo-v4.1\n\nThis is work-in-progress model, may not be ready for production use\n\nModel based on 'paulml/OGNO-7B' (downstream version of Mistral7B) finetuned using Direct Preference Optimization on jondurbin/truthy-dpo-v0.1.## Changelog\n\n- 2024-02-13: Initial release## About URL\n\nAt URL, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: URL\n\nLet us know if you use our model :). Also, if you need any help, feel free to contact us at info@URL"
] | [
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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. -->
# finetuned-distilbert-tim-alarms-clusters-ord
This model is a fine-tuned version of [giogenna16/pretrained-distilbert-tim-alarms-clusters-ord](https://huggingface.co/giogenna16/pretrained-distilbert-tim-alarms-clusters-ord) on an unknown dataset.
It achieves the following results on the evaluation set:
## 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': 'AdamW', 'weight_decay': 0.05, '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': 'PiecewiseConstantDecay', 'config': {'boundaries': [45, 90, 135, 180, 225, 270, 315, 360, 405, 450, 495, 540, 585, 630, 675, 720, 765, 810, 855], 'values': [3e-05, 2.8499999999999998e-05, 2.7075e-05, 2.5721249999999997e-05, 2.44351875e-05, 2.3213428124999993e-05, 2.2052756718749993e-05, 2.0950118882812494e-05, 1.9902612938671867e-05, 1.890748229173827e-05, 1.796210817715136e-05, 1.706400276829379e-05, 1.62108026298791e-05, 1.5400262498385145e-05, 1.4630249373465886e-05, 1.3898736904792591e-05, 1.3203800059552961e-05, 1.2543610056575313e-05, 1.1916429553746547e-05, 1.1320608076059218e-05], 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.37.2
- TensorFlow 2.15.0
- Tokenizers 0.15.2
| {"tags": ["generated_from_keras_callback"], "base_model": "giogenna16/pretrained-distilbert-tim-alarms-clusters-ord", "model-index": [{"name": "finetuned-distilbert-tim-alarms-clusters-ord", "results": []}]} | text-classification | giogenna16/finetuned-distilbert-tim-alarms-clusters-ord | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:giogenna16/pretrained-distilbert-tim-alarms-clusters-ord",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T10:29:52+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-giogenna16/pretrained-distilbert-tim-alarms-clusters-ord #autotrain_compatible #endpoints_compatible #region-us
|
# finetuned-distilbert-tim-alarms-clusters-ord
This model is a fine-tuned version of giogenna16/pretrained-distilbert-tim-alarms-clusters-ord on an unknown dataset.
It achieves the following results on the evaluation set:
## 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': 'AdamW', 'weight_decay': 0.05, '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': 'PiecewiseConstantDecay', 'config': {'boundaries': [45, 90, 135, 180, 225, 270, 315, 360, 405, 450, 495, 540, 585, 630, 675, 720, 765, 810, 855], 'values': [3e-05, 2.8499999999999998e-05, 2.7075e-05, 2.5721249999999997e-05, 2.44351875e-05, 2.3213428124999993e-05, 2.2052756718749993e-05, 2.0950118882812494e-05, 1.9902612938671867e-05, 1.890748229173827e-05, 1.796210817715136e-05, 1.706400276829379e-05, 1.62108026298791e-05, 1.5400262498385145e-05, 1.4630249373465886e-05, 1.3898736904792591e-05, 1.3203800059552961e-05, 1.2543610056575313e-05, 1.1916429553746547e-05, 1.1320608076059218e-05], 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.37.2
- TensorFlow 2.15.0
- Tokenizers 0.15.2
| [
"# finetuned-distilbert-tim-alarms-clusters-ord\n\nThis model is a fine-tuned version of giogenna16/pretrained-distilbert-tim-alarms-clusters-ord on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## 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- optimizer: {'name': 'AdamW', 'weight_decay': 0.05, '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': 'PiecewiseConstantDecay', 'config': {'boundaries': [45, 90, 135, 180, 225, 270, 315, 360, 405, 450, 495, 540, 585, 630, 675, 720, 765, 810, 855], 'values': [3e-05, 2.8499999999999998e-05, 2.7075e-05, 2.5721249999999997e-05, 2.44351875e-05, 2.3213428124999993e-05, 2.2052756718749993e-05, 2.0950118882812494e-05, 1.9902612938671867e-05, 1.890748229173827e-05, 1.796210817715136e-05, 1.706400276829379e-05, 1.62108026298791e-05, 1.5400262498385145e-05, 1.4630249373465886e-05, 1.3898736904792591e-05, 1.3203800059552961e-05, 1.2543610056575313e-05, 1.1916429553746547e-05, 1.1320608076059218e-05], 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n- training_precision: float32",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.2\n- TensorFlow 2.15.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-giogenna16/pretrained-distilbert-tim-alarms-clusters-ord #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuned-distilbert-tim-alarms-clusters-ord\n\nThis model is a fine-tuned version of giogenna16/pretrained-distilbert-tim-alarms-clusters-ord on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## 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- optimizer: {'name': 'AdamW', 'weight_decay': 0.05, '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': 'PiecewiseConstantDecay', 'config': {'boundaries': [45, 90, 135, 180, 225, 270, 315, 360, 405, 450, 495, 540, 585, 630, 675, 720, 765, 810, 855], 'values': [3e-05, 2.8499999999999998e-05, 2.7075e-05, 2.5721249999999997e-05, 2.44351875e-05, 2.3213428124999993e-05, 2.2052756718749993e-05, 2.0950118882812494e-05, 1.9902612938671867e-05, 1.890748229173827e-05, 1.796210817715136e-05, 1.706400276829379e-05, 1.62108026298791e-05, 1.5400262498385145e-05, 1.4630249373465886e-05, 1.3898736904792591e-05, 1.3203800059552961e-05, 1.2543610056575313e-05, 1.1916429553746547e-05, 1.1320608076059218e-05], 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n- training_precision: float32",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.2\n- TensorFlow 2.15.0\n- Tokenizers 0.15.2"
] | [
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"passage: TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-giogenna16/pretrained-distilbert-tim-alarms-clusters-ord #autotrain_compatible #endpoints_compatible #region-us \n# finetuned-distilbert-tim-alarms-clusters-ord\n\nThis model is a fine-tuned version of giogenna16/pretrained-distilbert-tim-alarms-clusters-ord on an unknown dataset.\nIt achieves the following results on the evaluation set:## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure"
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null | null | peft |
# Model Card for Model ID
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## Training Details
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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### Testing Data, Factors & Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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#### Software
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## Citation [optional]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "sajjadamjad/quizz_llm_bible_KJ"} | null | sajjadamjad/bible_quiz_prompted | [
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# Model Card for Model ID
## Model Details
### Model Description
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- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
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#### Factors
#### Metrics
### Results
#### Summary
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# image_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2141
- Accuracy: 0.5938
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 1.2602 | 0.5312 |
| No log | 2.0 | 80 | 1.2212 | 0.55 |
| No log | 3.0 | 120 | 1.2422 | 0.5375 |
| No log | 4.0 | 160 | 1.1822 | 0.6 |
| No log | 5.0 | 200 | 1.2218 | 0.55 |
| No log | 6.0 | 240 | 1.1602 | 0.6125 |
| No log | 7.0 | 280 | 1.2598 | 0.5687 |
### 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"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "image_classification", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train[:5000]", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.59375, "name": "Accuracy"}]}]}]} | image-classification | Danung/image_classification | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T10:33:06+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| image\_classification
=====================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2141
* Accuracy: 0.5938
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 7
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0+cu118
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 7",
"### Training results",
"### Framework versions\n\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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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 7",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
86,
97,
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] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 7### Training results### Framework versions\n\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 | transformers |
# CartoonOrNotv2
CartoonOrNot Model using Swin Transformer Architecture | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | image-classification | Libidrave/CartoonOrNotv2 | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T10:35:19+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #swin #image-classification #pytorch #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# CartoonOrNotv2
CartoonOrNot Model using Swin Transformer Architecture | [
"# CartoonOrNotv2\n\n\nCartoonOrNot Model using Swin Transformer Architecture"
] | [
"TAGS\n#transformers #tensorboard #safetensors #swin #image-classification #pytorch #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# CartoonOrNotv2\n\n\nCartoonOrNot Model using Swin Transformer Architecture"
] | [
55,
17
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #swin #image-classification #pytorch #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n# CartoonOrNotv2\n\n\nCartoonOrNot Model using Swin Transformer Architecture"
] | [
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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 0.7.1 | {"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-hf", "pipeline_tag": "text-generation"} | text-generation | sravaniayyagari/new-finetuned-model | [
"peft",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"4-bit",
"region:us"
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"1910.09700"
] | [] | TAGS
#peft #safetensors #llama #text-generation #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #4-bit #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
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- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
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null | null | transformers |
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- Language(s) (NLP):
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### Model Sources [optional]
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- Demo [optional]:
## Uses
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### Downstream Use [optional]
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## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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null | null | peft |
# Model Card for Model ID
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## Model Details
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### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "deepseek-ai/deepseek-coder-33b-instruct"} | null | NikitaZagainov/notebook-generation-deepseek-33b-2ep | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:deepseek-ai/deepseek-coder-33b-instruct",
"region:us"
] | 2024-02-14T10:39:27+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-deepseek-ai/deepseek-coder-33b-instruct #region-us
|
# Model Card for Model ID
## Model Details
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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
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- Training regime:
#### 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:
- Hours used:
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## Technical Specifications [optional]
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[optional]
BibTeX:
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### Framework versions
- PEFT 0.7.1 | [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.1"
] | [
"TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-deepseek-ai/deepseek-coder-33b-instruct #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.1"
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"passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-deepseek-ai/deepseek-coder-33b-instruct #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.7.1"
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-base-finetuned
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu118
- 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-Instruct-v0.2", "model-index": [{"name": "mistral-base-finetuned", "results": []}]} | null | lokaspire/mistral-base-finetuned | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
] | 2024-02-14T10:47:27+00:00 | [] | [] | TAGS
#peft #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-Instruct-v0.2 #license-apache-2.0 #region-us
|
# mistral-base-finetuned
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.2 | [
"# mistral-base-finetuned\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 1000\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu118\n- Datasets 2.17.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-Instruct-v0.2 #license-apache-2.0 #region-us \n",
"# mistral-base-finetuned\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 1000\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu118\n- Datasets 2.17.0\n- Tokenizers 0.15.2"
] | [
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] | [
"passage: TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-Instruct-v0.2 #license-apache-2.0 #region-us \n# mistral-base-finetuned\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 1000\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu118\n- Datasets 2.17.0\n- Tokenizers 0.15.2"
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null | null | peft |
# Model Card for Model ID
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## Model Details
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- PEFT 0.7.2.dev0 | {"library_name": "peft", "base_model": "google/flan-t5-small"} | null | HeydarS/flan-t5-small_peft_v22 | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/flan-t5-small",
"region:us"
] | 2024-02-14T10:48:21+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-small #region-us
|
# Model Card for Model ID
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## Training Details
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## Evaluation
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### Framework versions
- PEFT 0.7.2.dev0 | [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.2.dev0"
] | [
"TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-small #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.2.dev0"
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"passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-small #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.7.2.dev0"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | mtc/meta-llama-Llama-2-7b-hf-arxiv-summarization-1000-last-lora-full-adapter | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Uses
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### Out-of-Scope Use
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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:
- Cloud Provider:
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## Glossary [optional]
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | mtc/meta-llama-Llama-2-7b-hf-arxiv-summarization-1000-last_merged | [
"transformers",
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"arxiv:1910.09700",
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# Model Card for Model ID
## Model Details
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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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
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APA:
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | Kralley/qlora_model_f | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-14T10:54:27+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #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",
"### 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 #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 #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. -->
# videomae-base-finetuned-ucf101-subset-finetuned-ucf101-subset
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5905
- Accuracy: 0.8286
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8005 | 1.0 | 300 | 0.5905 | 0.8286 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "videomae-base-finetuned-ucf101-subset-finetuned-ucf101-subset", "results": []}]} | video-classification | Myaukko/videomae-base-finetuned-ucf101-subset-finetuned-ucf101-subset | [
"transformers",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | 2024-02-14T10:56:42+00:00 | [] | [] | TAGS
#transformers #safetensors #videomae #video-classification #generated_from_trainer #endpoints_compatible #region-us
| videomae-base-finetuned-ucf101-subset-finetuned-ucf101-subset
=============================================================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5905
* Accuracy: 0.8286
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
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 300
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0+cu118
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
"### 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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 300",
"### Training results",
"### Framework versions\n\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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"TAGS\n#transformers #safetensors #videomae #video-classification #generated_from_trainer #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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 300",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
38,
115,
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] | [
"passage: TAGS\n#transformers #safetensors #videomae #video-classification #generated_from_trainer #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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 300### Training results### Framework versions\n\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 | peft |
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- PEFT 0.7.1 | {"library_name": "peft", "base_model": "deepseek-ai/deepseek-coder-33b-instruct"} | null | NikitaZagainov/notebook-generation-deepseek-33b-3ep | [
"peft",
"safetensors",
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"base_model:deepseek-ai/deepseek-coder-33b-instruct",
"region:us"
] | 2024-02-14T11:00:31+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-deepseek-ai/deepseek-coder-33b-instruct #region-us
|
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BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
- PEFT 0.7.1 | [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.1"
] | [
"TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-deepseek-ai/deepseek-coder-33b-instruct #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.1"
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"passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-deepseek-ai/deepseek-coder-33b-instruct #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.7.1"
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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 0.8.2.dev0 | {"library_name": "peft", "base_model": "intelsense/IntelsenseMistral1stPhase"} | null | RadAlienware/mis_mod_bn_3rd_phase | [
"peft",
"safetensors",
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#peft #safetensors #arxiv-1910.09700 #base_model-intelsense/IntelsenseMistral1stPhase #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]
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### Compute Infrastructure
#### Hardware
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[optional]
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APA:
## Glossary [optional]
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# facebook/wav2vec2-base
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) 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: 0.0003
- train_batch_size: 32
- 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: 1000
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.1.dev0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "facebook/wav2vec2-base", "results": []}]} | automatic-speech-recognition | papasega/wav2vec2-base-finetuned | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-14T11:02:18+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #region-us
|
# facebook/wav2vec2-base
This model is a fine-tuned version of facebook/wav2vec2-base 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: 0.0003
- train_batch_size: 32
- 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: 1000
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.1.dev0
- Tokenizers 0.15.1
| [
"# facebook/wav2vec2-base\n\nThis model is a fine-tuned version of facebook/wav2vec2-base 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: 0.0003\n- train_batch_size: 32\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: 1000\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.1.dev0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #region-us \n",
"# facebook/wav2vec2-base\n\nThis model is a fine-tuned version of facebook/wav2vec2-base 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: 0.0003\n- train_batch_size: 32\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: 1000\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.1.dev0\n- Tokenizers 0.15.1"
] | [
70,
35,
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"passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #region-us \n# facebook/wav2vec2-base\n\nThis model is a fine-tuned version of facebook/wav2vec2-base 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: 0.0003\n- train_batch_size: 32\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: 1000\n- num_epochs: 1### Training results### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.1.dev0\n- Tokenizers 0.15.1"
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null | null | transformers | ---
# 🦜 EmertonBeagle-7B-dpo
EmertonOmniBeagle-7B-dpo is a DPO fine-tune of [mlabonne/OmniBeagle14-7B](https://huggingface.co/mlabonne/OmniBeagle-7B) using the [yleo/emerton_dpo_pairs_judge](https://huggingface.co/datasets/yleo/emerton_dpo_pairs_judge) preference dataset created from [Intel/orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs) by replacing gpt 3.5 answer by a gpt4 Turbo answer. Then, gpt4 Turbo is put as chosen whereas gpt4 is put as rejected.
## 🔍 Applications
This model uses a context window of 8k. It is compatible with different templates, like chatml and Llama's chat template.
## 🏆 Evaluation
### Open LLM Leaderboard
To come...
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "yleo/EmertonBeagle-7B"
messages = [{"role": "user", "content": "How to improve LLM fine-tuning?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "cc-by-nc-4.0", "tags": ["dpo"], "datasets": ["yleo/emerton_dpo_pairs"], "base_model": "mlabonne/OmniBeagle14-7B"} | text-generation | yleo/EmertonBeagle-7B-dpo | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"dpo",
"dataset:yleo/emerton_dpo_pairs",
"base_model:mlabonne/OmniBeagle14-7B",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T11:02:58+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #dpo #dataset-yleo/emerton_dpo_pairs #base_model-mlabonne/OmniBeagle14-7B #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ---
# EmertonBeagle-7B-dpo
EmertonOmniBeagle-7B-dpo is a DPO fine-tune of mlabonne/OmniBeagle14-7B using the yleo/emerton_dpo_pairs_judge preference dataset created from Intel/orca_dpo_pairs by replacing gpt 3.5 answer by a gpt4 Turbo answer. Then, gpt4 Turbo is put as chosen whereas gpt4 is put as rejected.
## Applications
This model uses a context window of 8k. It is compatible with different templates, like chatml and Llama's chat template.
## Evaluation
### Open LLM Leaderboard
To come...
## Usage
| [
"# EmertonBeagle-7B-dpo\n\nEmertonOmniBeagle-7B-dpo is a DPO fine-tune of mlabonne/OmniBeagle14-7B using the yleo/emerton_dpo_pairs_judge preference dataset created from Intel/orca_dpo_pairs by replacing gpt 3.5 answer by a gpt4 Turbo answer. Then, gpt4 Turbo is put as chosen whereas gpt4 is put as rejected.",
"## Applications\n\nThis model uses a context window of 8k. It is compatible with different templates, like chatml and Llama's chat template.",
"## Evaluation",
"### Open LLM Leaderboard\n\nTo come...",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #dpo #dataset-yleo/emerton_dpo_pairs #base_model-mlabonne/OmniBeagle14-7B #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# EmertonBeagle-7B-dpo\n\nEmertonOmniBeagle-7B-dpo is a DPO fine-tune of mlabonne/OmniBeagle14-7B using the yleo/emerton_dpo_pairs_judge preference dataset created from Intel/orca_dpo_pairs by replacing gpt 3.5 answer by a gpt4 Turbo answer. Then, gpt4 Turbo is put as chosen whereas gpt4 is put as rejected.",
"## Applications\n\nThis model uses a context window of 8k. It is compatible with different templates, like chatml and Llama's chat template.",
"## Evaluation",
"### Open LLM Leaderboard\n\nTo come...",
"## Usage"
] | [
93,
114,
33,
3,
10,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #dpo #dataset-yleo/emerton_dpo_pairs #base_model-mlabonne/OmniBeagle14-7B #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# EmertonBeagle-7B-dpo\n\nEmertonOmniBeagle-7B-dpo is a DPO fine-tune of mlabonne/OmniBeagle14-7B using the yleo/emerton_dpo_pairs_judge preference dataset created from Intel/orca_dpo_pairs by replacing gpt 3.5 answer by a gpt4 Turbo answer. Then, gpt4 Turbo is put as chosen whereas gpt4 is put as rejected.## Applications\n\nThis model uses a context window of 8k. It is compatible with different templates, like chatml and Llama's chat template.## Evaluation### Open LLM Leaderboard\n\nTo come...## Usage"
] | [
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] |
null | null | null |
# Model Card for Mixtral-Instruct-ITR-8x7B-GGUF
- Model creator: [Envoid](https://huggingface.co/envoid)
- Original model: [Mixtral-Instruct-ITR-8x7B](https://huggingface.co/Envoid/Mixtral-Instruct-ITR-8x7B)
<!-- Provide a quick summary of what the model is/does. -->
Envoid_Mixtral-Instruct-ITR-8x7B quantized with love.
Starting out with Q4_K_M, and iterating from there.
**All quantizations based on original fp16 model.**
Future plans for imatrix/IQ quants (pending compute power).
First time doing quantizations so any feedback is greatly appreciated.
| Name | Quant method | Bits |ppl*
| ---- | ---- | ---- | ---- |
| [Mixtral-Instruct-ITR-8x7B.Q2_K.gguf](https://huggingface.co/InferenceIllusionist/Mixtral-Instruct-ITR-8x7B-GGUF/blob/main/Mixtral-Instruct-ITR-8x7B-Q2_K.gguf) | Q2_K | 2 | +0.6717 ppl|
| [Mixtral-Instruct-ITR-8x7B.Q3_K_S.gguf](https://huggingface.co/InferenceIllusionist/Mixtral-Instruct-ITR-8x7B-GGUF/blob/main/Mixtral-Instruct-ITR-8x7B-Q3_K_S.gguf) | Q3_K_S | 3 | +0.5551 ppl|
| [Mixtral-Instruct-ITR-8x7B.Q3_K_M.gguf](https://huggingface.co/InferenceIllusionist/Mixtral-Instruct-ITR-8x7B-GGUF/blob/main/Mixtral-Instruct-ITR-8x7B-Q3_K_M.gguf) | Q3_K_M | 3 | +0.2496 ppl|
| [Mixtral-Instruct-ITR-8x7B.Q3_K_L.gguf](https://huggingface.co/InferenceIllusionist/Mixtral-Instruct-ITR-8x7B-GGUF/blob/main/Mixtral-Instruct-ITR-8x7B-Q3_K_L.gguf) | Q3_K_L | 4 | +0.1764 ppl|
| [Mixtral-Instruct-ITR-8x7B.Q4_K_M.gguf](https://huggingface.co/InferenceIllusionist/Mixtral-Instruct-ITR-8x7B-GGUF/blob/main/Mixtral-Instruct-ITR-8x7B-Q4_K_M.gguf) | Q4_K_M | 5 | +0.0532 ppl|
| [Mixtral-Instruct-ITR-8x7B.Q5_K_S.gguf](https://huggingface.co/InferenceIllusionist/Mixtral-Instruct-ITR-8x7B-GGUF/blob/main/Mixtral-Instruct-ITR-8x7B-Q5_K_S.gguf) | Q5_K_S | 5 | +0.0400 ppl|
| [Mixtral-Instruct-ITR-8x7B.Q5_K_M.gguf](https://huggingface.co/InferenceIllusionist/Mixtral-Instruct-ITR-8x7B-GGUF/blob/main/Mixtral-Instruct-ITR-8x7B-Q5_K_M.gguf) | Q5_K_M | 6 | +0.0122 ppl|
| [Mixtral-Instruct-ITR-8x7B.Q6_K.gguf](https://huggingface.co/InferenceIllusionist/Mixtral-Instruct-ITR-8x7B-GGUF/blob/main/Mixtral-Instruct-ITR-8x7B-Q6_K.gguf) | Q6_K | 6 | +0.008 ppl|
| [Mixtral-Instruct-ITR-8x7B.Q8_0.gguf](https://huggingface.co/InferenceIllusionist/Mixtral-Instruct-ITR-8x7B-GGUF/blob/main/Mixtral-Instruct-ITR-8x7B-Q8_0.gguf) | Q8_0 | 8 | +0.004 ppl|
*Perplexity @ LLaMA-v1-7B for reference
Original model card below.
---
license: cc-by-nc-4.0
---
# Caution this model may be unpredictable
## Mixtral-Instruct-ITR (Interpolative Training Regression)
We have to go back, edition.
For this model I took what I learned in the making of [Cat-8x7B](https://huggingface.co/Envoid/Cat-8x7B) and went back to the very beginning and SLERP merged [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) onto [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)
While the results aren't perfect the model feels more creative and less overcooked than Mixtral Instruct is often accused of being.
The hopes are that this should also have left the model much more receptive to additional finetuning and I am interested to see what comes of it so please feel free to download it and have fun.
Apologies about the small shard size (keep forgetting to change the mergekit config back)
## The model is a lot less likely to refuse certain requests in this state:
so if you are going to apply additional finetuning to the model you may need to bolster its alignment depending on your use case.
The model still responds well to [INST] Thingie [/INST] formatting quite well.
Or if preferred this can easily be reproduced if you have both base and instruct models handy using mergekit (mixtral branch) with the following config
```
models:
- model: ./mistralai_Mixtral-8x7B-Instruct-v0.1
- model: ./mistralai_Mixtral-8x7B-v0.1
merge_method: slerp
base_model: ./mistralai_Mixtral-8x7B-v0.1
parameters:
t:
- value: 0.5
dtype: float16
``` | {"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences"], "model_name": "Mixtral-Instruct-ITR-8x7B", "base_model": "Envoid/Mixtral-Instruct-ITR-8x7B", "inference": false, "model_creator": "Envoid", "model_type": "mixtral"} | null | InferenceIllusionist/Mixtral-Instruct-ITR-8x7B-GGUF | [
"gguf",
"not-for-all-audiences",
"base_model:Envoid/Mixtral-Instruct-ITR-8x7B",
"license:cc-by-nc-4.0",
"region:us"
] | 2024-02-14T11:03:49+00:00 | [] | [] | TAGS
#gguf #not-for-all-audiences #base_model-Envoid/Mixtral-Instruct-ITR-8x7B #license-cc-by-nc-4.0 #region-us
| Model Card for Mixtral-Instruct-ITR-8x7B-GGUF
=============================================
* Model creator: Envoid
* Original model: Mixtral-Instruct-ITR-8x7B
Envoid\_Mixtral-Instruct-ITR-8x7B quantized with love.
Starting out with Q4\_K\_M, and iterating from there.
All quantizations based on original fp16 model.
Future plans for imatrix/IQ quants (pending compute power).
First time doing quantizations so any feedback is greatly appreciated.
\*Perplexity @ LLaMA-v1-7B for reference
Original model card below.
---
license: cc-by-nc-4.0
---------------------
Caution this model may be unpredictable
=======================================
--------------------------------------------------------
We have to go back, edition.
For this model I took what I learned in the making of Cat-8x7B and went back to the very beginning and SLERP merged mistralai/Mixtral-8x7B-Instruct-v0.1 onto mistralai/Mixtral-8x7B-v0.1
While the results aren't perfect the model feels more creative and less overcooked than Mixtral Instruct is often accused of being.
The hopes are that this should also have left the model much more receptive to additional finetuning and I am interested to see what comes of it so please feel free to download it and have fun.
Apologies about the small shard size (keep forgetting to change the mergekit config back)
The model is a lot less likely to refuse certain requests in this state:
------------------------------------------------------------------------
so if you are going to apply additional finetuning to the model you may need to bolster its alignment depending on your use case.
The model still responds well to [INST] Thingie [/INST] formatting quite well.
Or if preferred this can easily be reproduced if you have both base and instruct models handy using mergekit (mixtral branch) with the following config
| [] | [
"TAGS\n#gguf #not-for-all-audiences #base_model-Envoid/Mixtral-Instruct-ITR-8x7B #license-cc-by-nc-4.0 #region-us \n"
] | [
51
] | [
"passage: TAGS\n#gguf #not-for-all-audiences #base_model-Envoid/Mixtral-Instruct-ITR-8x7B #license-cc-by-nc-4.0 #region-us \n"
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null | null | transformers | # Lumiere-120b
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
* [Undi95/Miqu-70B-Alpaca-DPO](https://huggingface.co/Undi95/Miqu-70B-Alpaca-DPO)
* [Sao10K/Euryale-1.3-L2-70B](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: linear
parameters:
weight: 1.0
slices:
- sources:
- model: Undi95/Miqu-70B-Alpaca-DPO
layer_range: [0, 1]
- model: Sao10K/Euryale-1.3-L2-70B
layer_range: [0, 1]
parameters:
weight: 0
- sources:
- model: Undi95/Miqu-70B-Alpaca-DPO
layer_range: [1, 20]
- sources:
- model: Sao10K/Euryale-1.3-L2-70B
layer_range: [10, 30]
- sources:
- model: Undi95/Miqu-70B-Alpaca-DPO
layer_range: [20, 40]
- sources:
- model: Sao10K/Euryale-1.3-L2-70B
layer_range: [30, 50]
- sources:
- model: Undi95/Miqu-70B-Alpaca-DPO
layer_range: [40, 60]
- sources:
- model: Sao10K/Euryale-1.3-L2-70B
layer_range: [50, 70]
- sources:
- model: Undi95/Miqu-70B-Alpaca-DPO
layer_range: [60, 79]
- sources:
- model: Undi95/Miqu-70B-Alpaca-DPO
layer_range: [79, 80]
- model: Sao10K/Euryale-1.3-L2-70B
layer_range: [79, 80]
parameters:
weight: 0
dtype: float16
tokenizer_source: model:Undi95/Miqu-70B-Alpaca-DPO
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Undi95/Miqu-70B-Alpaca-DPO", "Sao10K/Euryale-1.3-L2-70B"]} | text-generation | bcse/Lumiere-120b | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2203.05482",
"base_model:Undi95/Miqu-70B-Alpaca-DPO",
"base_model:Sao10K/Euryale-1.3-L2-70B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T11:09:35+00:00 | [
"2203.05482"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #arxiv-2203.05482 #base_model-Undi95/Miqu-70B-Alpaca-DPO #base_model-Sao10K/Euryale-1.3-L2-70B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Lumiere-120b
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the linear merge method.
### Models Merged
The following models were included in the merge:
* Undi95/Miqu-70B-Alpaca-DPO
* Sao10K/Euryale-1.3-L2-70B
### Configuration
The following YAML configuration was used to produce this model:
| [
"# Lumiere-120b\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the linear merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Undi95/Miqu-70B-Alpaca-DPO\n* Sao10K/Euryale-1.3-L2-70B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #arxiv-2203.05482 #base_model-Undi95/Miqu-70B-Alpaca-DPO #base_model-Sao10K/Euryale-1.3-L2-70B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Lumiere-120b\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the linear merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Undi95/Miqu-70B-Alpaca-DPO\n* Sao10K/Euryale-1.3-L2-70B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
107,
22,
4,
16,
46,
17
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #arxiv-2203.05482 #base_model-Undi95/Miqu-70B-Alpaca-DPO #base_model-Sao10K/Euryale-1.3-L2-70B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Lumiere-120b\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the linear merge method.### Models Merged\n\nThe following models were included in the merge:\n* Undi95/Miqu-70B-Alpaca-DPO\n* Sao10K/Euryale-1.3-L2-70B### Configuration\n\nThe following YAML configuration was used to produce this model:"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbart-neutralization
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5140
- Bleu: 10.6012
- Gen Len: 21.5854
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 16 | 3.7443 | 7.8006 | 18.3171 |
| No log | 2.0 | 32 | 3.5140 | 10.6012 | 21.5854 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["simplification", "generated_from_trainer"], "metrics": ["bleu"], "base_model": "facebook/mbart-large-50", "model-index": [{"name": "mbart-neutralization", "results": []}]} | text2text-generation | irp1999/mbart-neutralization | [
"transformers",
"tensorboard",
"safetensors",
"mbart",
"text2text-generation",
"simplification",
"generated_from_trainer",
"base_model:facebook/mbart-large-50",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T11:12:52+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #mbart #text2text-generation #simplification #generated_from_trainer #base_model-facebook/mbart-large-50 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| mbart-neutralization
====================
This model is a fine-tuned version of facebook/mbart-large-50 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.5140
* Bleu: 10.6012
* Gen Len: 21.5854
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5.6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 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: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 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 #mbart #text2text-generation #simplification #generated_from_trainer #base_model-facebook/mbart-large-50 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 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"
] | [
73,
99,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #mbart #text2text-generation #simplification #generated_from_trainer #base_model-facebook/mbart-large-50 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 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": []} | text-generation | ISdept/qwen-7b-1_5-hi200-ym-intents-lang-translate | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #qwen2 #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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- License:
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### Model Sources [optional]
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- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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null | null | transformers | # Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [facebook/opt-2.7b](https://huggingface.co/facebook/opt-2.7b)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.29.2
pip install einops==0.6.1
pip install accelerate==0.19.0
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="Shishir1807/Moas_Explicit_OPT_v2",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?</s><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Shishir1807/Moas_Explicit_OPT_v2",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"Shishir1807/Moas_Explicit_OPT_v2",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Shishir1807/Moas_Explicit_OPT_v2" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?</s><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
OPTForCausalLM(
(model): OPTModel(
(decoder): OPTDecoder(
(embed_tokens): Embedding(50272, 2560, padding_idx=1)
(embed_positions): OPTLearnedPositionalEmbedding(2050, 2560)
(final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(layers): ModuleList(
(0-31): 32 x OPTDecoderLayer(
(self_attn): OPTAttention(
(k_proj): Linear(in_features=2560, out_features=2560, bias=True)
(v_proj): Linear(in_features=2560, out_features=2560, bias=True)
(q_proj): Linear(in_features=2560, out_features=2560, bias=True)
(out_proj): Linear(in_features=2560, out_features=2560, bias=True)
)
(activation_fn): ReLU()
(self_attn_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=2560, out_features=10240, bias=True)
(fc2): Linear(in_features=10240, out_features=2560, bias=True)
(final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
)
)
)
)
(lm_head): Linear(in_features=2560, out_features=50272, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. | {"language": ["en"], "library_name": "transformers", "tags": ["gpt", "llm", "large language model", "h2o-llmstudio"], "inference": false, "thumbnail": "https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico"} | text-generation | Shishir1807/Moas_Explicit_OPT_v2 | [
"transformers",
"pytorch",
"opt",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
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"region:us"
] | 2024-02-14T11:16:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #opt #text-generation #gpt #llm #large language model #h2o-llmstudio #en #autotrain_compatible #text-generation-inference #region-us
| # Model Card
## Summary
This model was trained using H2O LLM Studio.
- Base model: facebook/opt-2.7b
## Usage
To use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers', 'accelerate' and 'torch' libraries installed.
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
Alternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
## Quantization and sharding
You can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .
## Model Architecture
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. | [
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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 Hi - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 0
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"language": ["hi"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_11_0"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small Hi - Sanchit Gandhi", "results": []}]} | automatic-speech-recognition | Aditya-1406-Agrawal/output | [
"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",
"endpoints_compatible",
"region:us"
] | 2024-02-14T11:16:22+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 #endpoints_compatible #region-us
|
# Whisper Small Hi - Sanchit Gandhi
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 0
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# Whisper Small Hi - Sanchit Gandhi\n\nThis model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 0\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Framework versions\n\n- Transformers 4.38.0.dev0\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 #hi #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Whisper Small Hi - Sanchit Gandhi\n\nThis model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 0\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
100,
39,
6,
12,
8,
3,
101,
38
] | [
"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 #endpoints_compatible #region-us \n# Whisper Small Hi - Sanchit Gandhi\n\nThis model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 0\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0### Framework versions\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 | diffusers | ### My-fav-pet-USS Dreambooth model trained by sindhujauppaluri123 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 223C1A0538
Sample pictures of this concept:
| {"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]} | text-to-image | sindhujauppaluri123/my-fav-pet-uss | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-14T11:19:08+00:00 | [] | [] | TAGS
#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
| ### My-fav-pet-USS Dreambooth model trained by sindhujauppaluri123 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 223C1A0538
Sample pictures of this concept:
!0
| [
"### My-fav-pet-USS Dreambooth model trained by sindhujauppaluri123 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 223C1A0538\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-fav-pet-USS Dreambooth model trained by sindhujauppaluri123 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 223C1A0538\n\nSample pictures of this concept:\n\n !0"
] | [
73,
62
] | [
"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-fav-pet-USS Dreambooth model trained by sindhujauppaluri123 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 223C1A0538\n\nSample pictures of this concept:\n\n !0"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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- PEFT 0.7.1 | {"library_name": "peft", "base_model": "deepseek-ai/deepseek-coder-33b-instruct"} | null | NikitaZagainov/notebook-generation-deepseek-33b-4ep | [
"peft",
"safetensors",
"arxiv:1910.09700",
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"region:us"
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"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-deepseek-ai/deepseek-coder-33b-instruct #region-us
|
# Model Card for Model ID
## Model Details
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- Demo [optional]:
## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
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#### 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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## Technical Specifications [optional]
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[optional]
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APA:
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null | null | transformers |
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| {"library_name": "transformers", "tags": []} | text-generation | ISdept/qwen-7b-1_5-hi200-ym-intents-lang-translate-4bit | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
] | 2024-02-14T11:21:54+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #qwen2 #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #4-bit #region-us
|
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- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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"# Model Card for Model ID",
"## Model Details",
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"### Training Procedure",
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"## 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
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### Framework versions
- PEFT 0.8.2 | {"license": "apache-2.0", "library_name": "peft", "base_model": "NeuralNovel/Tiger-7B-v0.1-LaserRMT-Math-5-10-15"} | null | NovoCode/Tiger-7B-v0.1-LaserRMT-Math-5-10-15-Neural-DPO | [
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#peft #safetensors #arxiv-1910.09700 #base_model-NeuralNovel/Tiger-7B-v0.1-LaserRMT-Math-5-10-15 #license-apache-2.0 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## 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 | 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. -->
# wizcode1b-finetuned-codex
This model is a fine-tuned version of [WizardLM/WizardCoder-1B-V1.0](https://huggingface.co/WizardLM/WizardCoder-1B-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: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 100
- 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": "bigcode-openrail-m", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "WizardLM/WizardCoder-1B-V1.0", "model-index": [{"name": "wizcode1b-finetuned-codex", "results": []}]} | null | mjc19/wizcode1b-finetuned-codex | [
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:WizardLM/WizardCoder-1B-V1.0",
"license:bigcode-openrail-m",
"region:us"
] | 2024-02-14T11:28:42+00:00 | [] | [] | TAGS
#tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-WizardLM/WizardCoder-1B-V1.0 #license-bigcode-openrail-m #region-us
|
# wizcode1b-finetuned-codex
This model is a fine-tuned version of WizardLM/WizardCoder-1B-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: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 100
- 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
| [
"# wizcode1b-finetuned-codex\n\nThis model is a fine-tuned version of WizardLM/WizardCoder-1B-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: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 100\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-WizardLM/WizardCoder-1B-V1.0 #license-bigcode-openrail-m #region-us \n",
"# wizcode1b-finetuned-codex\n\nThis model is a fine-tuned version of WizardLM/WizardCoder-1B-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: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 100\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"
] | [
59,
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"passage: TAGS\n#tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-WizardLM/WizardCoder-1B-V1.0 #license-bigcode-openrail-m #region-us \n# wizcode1b-finetuned-codex\n\nThis model is a fine-tuned version of WizardLM/WizardCoder-1B-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: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 100\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 | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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('{MODEL_NAME}')
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('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# 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={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 15 with parameters:
```
{'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`__main__.LoggingCosineSim` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 15,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 150, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 256, '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 --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | omniamnaeem/semantic_sim_ner_with_MultipleNegativesRankingLoss | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | 2024-02-14T11:31:55+00:00 | [] | [] | TAGS
#sentence-transformers #safetensors #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 256 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:
'URL.dataloader.DataLoader' of length 15 with parameters:
Loss:
'__main__.LoggingCosineSim' with parameters:
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
| [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 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'URL.dataloader.DataLoader' of length 15 with parameters:\n\n\nLoss:\n\n'__main__.LoggingCosineSim' 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 #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 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'URL.dataloader.DataLoader' of length 15 with parameters:\n\n\nLoss:\n\n'__main__.LoggingCosineSim' with parameters:\n \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
43,
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38,
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5,
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] | [
"passage: TAGS\n#sentence-transformers #safetensors #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 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'URL.dataloader.DataLoader' of length 15 with parameters:\n\n\nLoss:\n\n'__main__.LoggingCosineSim' with parameters:\n \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7057
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.2484 |
| 2.6767 | 2.0 | 500 | 1.7610 |
| 2.6767 | 3.0 | 750 | 1.7057 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cpu
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "my_awesome_qa_model", "results": []}]} | question-answering | dedemilano/my_awesome_qa_model | [
"transformers",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-14T11:32:56+00:00 | [] | [] | TAGS
#transformers #safetensors #distilbert #question-answering #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us
| my\_awesome\_qa\_model
======================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7057
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0+cpu
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cpu\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cpu\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
61,
98,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #distilbert #question-answering #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cpu\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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null | null | transformers |
<p align="center">
<img src="https://huggingface.co/BioMistral/BioMistral-7B/resolve/main/wordart_blue_m_rectangle.png?download=true" alt="drawing" width="250"/>
</p>
# BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains
**Abstract:**
Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. Despite the availability of various open-source LLMs tailored for health contexts, adapting general-purpose LLMs to the medical domain presents significant challenges.
In this paper, we introduce BioMistral, an open-source LLM tailored for the biomedical domain, utilizing Mistral as its foundation model and further pre-trained on PubMed Central. We conduct a comprehensive evaluation of BioMistral on a benchmark comprising 10 established medical question-answering (QA) tasks in English. We also explore lightweight models obtained through quantization and model merging approaches. Our results demonstrate BioMistral's superior performance compared to existing open-source medical models and its competitive edge against proprietary counterparts. Finally, to address the limited availability of data beyond English and to assess the multilingual generalization of medical LLMs, we automatically translated and evaluated this benchmark into 7 other languages. This marks the first large-scale multilingual evaluation of LLMs in the medical domain. Datasets, multilingual evaluation benchmarks, scripts, and all the models obtained during our experiments are freely released.
# 1. BioMistral models
**BioMistral** is a suite of Mistral-based further pre-trained open source models suited for the medical domains and pre-trained using textual data from PubMed Central Open Access (CC0, CC BY, CC BY-SA, and CC BY-ND). All the models are trained using the CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/jean-zay/) French HPC.
| Model Name | Base Model | Model Type | Sequence Length | Download |
|:-------------------:|:----------------------------------:|:-------------------:|:---------------:|:-----------------------------------------------------:|
| BioMistral-7B | [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) | Further Pre-trained | 2048 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B) |
| BioMistral-7B-DARE | [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) | Merge DARE | 2048 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-DARE) |
| BioMistral-7B-TIES | [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) | Merge TIES | 2048 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-TIES) |
| BioMistral-7B-SLERP | [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) | Merge SLERP | 2048 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-SLERP) |
# 2. Quantized Models
| Base Model | Method | q_group_size | w_bit | version | VRAM GB | Time | Download |
|:-------------------:|:------:|:------------:|:-----:|:-------:|:-------:|:------:|:--------:|
| BioMistral-7B | FP16/BF16 | | | | 15.02 | x1.00 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B) |
| BioMistral-7B | AWQ | 128 | 4 | GEMM | 4.68 | x1.41 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-AWQ-QGS128-W4-GEMM) |
| BioMistral-7B | AWQ | 128 | 4 | GEMV | 4.68 | x10.30 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-AWQ-QGS128-W4-GEMV) |
| BioMistral-7B | BnB.4 | | 4 | | 5.03 | x3.25 | [HuggingFace](blank) |
| BioMistral-7B | BnB.8 | | 8 | | 8.04 | x4.34 | [HuggingFace](blank) |
| BioMistral-7B-DARE | AWQ | 128 | 4 | GEMM | 4.68 | x1.41 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-DARE-AWQ-QGS128-W4-GEMM) |
| BioMistral-7B-TIES | AWQ | 128 | 4 | GEMM | 4.68 | x1.41 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-TIES-AWQ-QGS128-W4-GEMM) |
| BioMistral-7B-SLERP | AWQ | 128 | 4 | GEMM | 4.68 | x1.41 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-SLERP-AWQ-QGS128-W4-GEMM) |
# 2. Using BioMistral
You can use BioMistral with [Hugging Face's Transformers library](https://github.com/huggingface/transformers) as follow.
Loading the model and tokenizer :
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("BioMistral/BioMistral-7B")
model = AutoModel.from_pretrained("BioMistral/BioMistral-7B")
```
# 3. Supervised Fine-tuning Benchmark
| | Clinical KG | Medical Genetics | Anatomy | Pro Medicine | College Biology | College Medicine | MedQA | MedQA 5 opts | PubMedQA | MedMCQA | Avg. |
|-------------------------------------------|:---------------------------------------------:|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|------------------|
| **BioMistral 7B** | 59.9 | 64.0 | 56.5 | 60.4 | 59.0 | 54.7 | 50.6 | 42.8 | 77.5 | 48.1 | 57.3 |
| **Mistral 7B Instruct** | **62.9** | 57.0 | 55.6 | 59.4 | 62.5 | <u>57.2</u> | 42.0 | 40.9 | 75.7 | 46.1 | 55.9 |
| | | | | | | | | | | | |
| **BioMistral 7B Ensemble** | <u>62.8</u> | 62.7 | <u>57.5</u> | **63.5** | 64.3 | 55.7 | 50.6 | 43.6 | 77.5 | **48.8** | 58.7 |
| **BioMistral 7B DARE** | 62.3 | **67.0** | 55.8 | 61.4 | **66.9** | **58.0** | **51.1** | **45.2** | <u>77.7</u> | <u>48.7</u> | **59.4** |
| **BioMistral 7B TIES** | 60.1 | <u>65.0</u> | **58.5** | 60.5 | 60.4 | 56.5 | 49.5 | 43.2 | 77.5 | 48.1 | 57.9 |
| **BioMistral 7B SLERP** | 62.5 | 64.7 | 55.8 | <u>62.7</u> | <u>64.8</u> | 56.3 | <u>50.8</u> | <u>44.3</u> | **77.8** | 48.6 | <u>58.8</u> |
| | | | | | | | | | | | |
| **MedAlpaca 7B** | 53.1 | 58.0 | 54.1 | 58.8 | 58.1 | 48.6 | 40.1 | 33.7 | 73.6 | 37.0 | 51.5 |
| **PMC-LLaMA 7B** | 24.5 | 27.7 | 35.3 | 17.4 | 30.3 | 23.3 | 25.5 | 20.2 | 72.9 | 26.6 | 30.4 |
| **MediTron-7B** | 41.6 | 50.3 | 46.4 | 27.9 | 44.4 | 30.8 | 41.6 | 28.1 | 74.9 | 41.3 | 42.7 |
| **BioMedGPT-LM-7B** | 51.4 | 52.0 | 49.4 | 53.3 | 50.7 | 49.1 | 42.5 | 33.9 | 76.8 | 37.6 | 49.7 |
| | | | | | | | | | | | |
| **GPT-3.5 Turbo 1106*** | 74.71 | 74.00 | 65.92 | 72.79 | 72.91 | 64.73 | 57.71 | 50.82 | 72.66 | 53.79 | 66.0 |
Supervised Fine-Tuning (SFT) performance of BioMistral 7B models compared to baselines, measured by accuracy (↑) and averaged across 3 random seeds of 3-shot. DARE, TIES, and SLERP are model merging strategies that combine BioMistral 7B and Mistral 7B Instruct. Best model in bold, and second-best underlined. *GPT-3.5 Turbo performances are reported from the 3-shot results without SFT.
# Citation BibTeX
```bibtex
Coming Soon
```
| {"language": ["fr", "en", "de", "nl", "es", "pt", "pl", "ro", "it"], "license": "apache-2.0", "tags": ["medical", "biology"], "datasets": ["pubmed"], "pipeline_tag": "text-generation"} | text-generation | BioMistral/BioMistral-7B | [
"transformers",
"pytorch",
"tensorboard",
"mistral",
"text-generation",
"medical",
"biology",
"conversational",
"fr",
"en",
"de",
"nl",
"es",
"pt",
"pl",
"ro",
"it",
"dataset:pubmed",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T11:33:32+00:00 | [] | [
"fr",
"en",
"de",
"nl",
"es",
"pt",
"pl",
"ro",
"it"
] | TAGS
#transformers #pytorch #tensorboard #mistral #text-generation #medical #biology #conversational #fr #en #de #nl #es #pt #pl #ro #it #dataset-pubmed #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains
============================================================================================
Abstract:
Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. Despite the availability of various open-source LLMs tailored for health contexts, adapting general-purpose LLMs to the medical domain presents significant challenges.
In this paper, we introduce BioMistral, an open-source LLM tailored for the biomedical domain, utilizing Mistral as its foundation model and further pre-trained on PubMed Central. We conduct a comprehensive evaluation of BioMistral on a benchmark comprising 10 established medical question-answering (QA) tasks in English. We also explore lightweight models obtained through quantization and model merging approaches. Our results demonstrate BioMistral's superior performance compared to existing open-source medical models and its competitive edge against proprietary counterparts. Finally, to address the limited availability of data beyond English and to assess the multilingual generalization of medical LLMs, we automatically translated and evaluated this benchmark into 7 other languages. This marks the first large-scale multilingual evaluation of LLMs in the medical domain. Datasets, multilingual evaluation benchmarks, scripts, and all the models obtained during our experiments are freely released.
1. BioMistral models
====================
BioMistral is a suite of Mistral-based further pre-trained open source models suited for the medical domains and pre-trained using textual data from PubMed Central Open Access (CC0, CC BY, CC BY-SA, and CC BY-ND). All the models are trained using the CNRS (French National Centre for Scientific Research) Jean Zay French HPC.
2. Quantized Models
===================
2. Using BioMistral
===================
You can use BioMistral with Hugging Face's Transformers library as follow.
Loading the model and tokenizer :
3. Supervised Fine-tuning Benchmark
===================================
Supervised Fine-Tuning (SFT) performance of BioMistral 7B models compared to baselines, measured by accuracy (↑) and averaged across 3 random seeds of 3-shot. DARE, TIES, and SLERP are model merging strategies that combine BioMistral 7B and Mistral 7B Instruct. Best model in bold, and second-best underlined. \*GPT-3.5 Turbo performances are reported from the 3-shot results without SFT.
BibTeX
| [] | [
"TAGS\n#transformers #pytorch #tensorboard #mistral #text-generation #medical #biology #conversational #fr #en #de #nl #es #pt #pl #ro #it #dataset-pubmed #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
92
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #mistral #text-generation #medical #biology #conversational #fr #en #de #nl #es #pt #pl #ro #it #dataset-pubmed #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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null | null | transformers | # Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [EleutherAI/pythia-2.8b-deduped](https://huggingface.co/EleutherAI/pythia-2.8b-deduped)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.29.2
pip install einops==0.6.1
pip install accelerate==0.19.0
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="Shishir1807/Moas_Explicit_PYT_v2",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Shishir1807/Moas_Explicit_PYT_v2",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"Shishir1807/Moas_Explicit_PYT_v2",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Shishir1807/Moas_Explicit_PYT_v2" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
GPTNeoXForCausalLM(
(gpt_neox): GPTNeoXModel(
(embed_in): Embedding(50304, 2560)
(layers): ModuleList(
(0-31): 32 x GPTNeoXLayer(
(input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(post_attention_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(attention): GPTNeoXAttention(
(rotary_emb): RotaryEmbedding()
(query_key_value): Linear(in_features=2560, out_features=7680, bias=True)
(dense): Linear(in_features=2560, out_features=2560, bias=True)
)
(mlp): GPTNeoXMLP(
(dense_h_to_4h): Linear(in_features=2560, out_features=10240, bias=True)
(dense_4h_to_h): Linear(in_features=10240, out_features=2560, bias=True)
(act): GELUActivation()
)
)
)
(final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
)
(embed_out): Linear(in_features=2560, out_features=50304, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. | {"language": ["en"], "library_name": "transformers", "tags": ["gpt", "llm", "large language model", "h2o-llmstudio"], "inference": false, "thumbnail": "https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico"} | text-generation | Shishir1807/Moas_Explicit_PYT_v2 | [
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"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T11:36:11+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt_neox #text-generation #gpt #llm #large language model #h2o-llmstudio #en #autotrain_compatible #text-generation-inference #region-us
| # Model Card
## Summary
This model was trained using H2O LLM Studio.
- Base model: EleutherAI/pythia-2.8b-deduped
## Usage
To use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers', 'accelerate' and 'torch' libraries installed.
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
Alternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
## Quantization and sharding
You can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .
## Model Architecture
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. | [
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null | null | transformers |
<br>
<br>
# LLaVA Model Card - PATCHED!
This is a patched version of the original model, with patches from aliencaocao applied from [here](https://github.com/haotian-liu/LLaVA/pull/1115).
## Model details
**Model type:**
LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data.
It is an auto-regressive language model, based on the transformer architecture.
Base LLM: [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
**Model date:**
LLaVA-v1.6-Mistral-7B was trained in December 2023.
**Paper or resources for more information:**
https://llava-vl.github.io/
## License
[mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) license.
**Where to send questions or comments about the model:**
https://github.com/haotian-liu/LLaVA/issues
## Intended use
**Primary intended uses:**
The primary use of LLaVA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
- 158K GPT-generated multimodal instruction-following data.
- 500K academic-task-oriented VQA data mixture.
- 50K GPT-4V data mixture.
- 40K ShareGPT data.
## Evaluation dataset
A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs. | {"license": "apache-2.0", "inference": false} | text-generation | Trelis/llava-v1.6-mistral-7b-PATCHED | [
"transformers",
"safetensors",
"llava",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | 2024-02-14T11:36:42+00:00 | [] | [] | TAGS
#transformers #safetensors #llava #text-generation #conversational #license-apache-2.0 #autotrain_compatible #region-us
|
<br>
<br>
# LLaVA Model Card - PATCHED!
This is a patched version of the original model, with patches from aliencaocao applied from here.
## Model details
Model type:
LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data.
It is an auto-regressive language model, based on the transformer architecture.
Base LLM: mistralai/Mistral-7B-Instruct-v0.2
Model date:
LLaVA-v1.6-Mistral-7B was trained in December 2023.
Paper or resources for more information:
URL
## License
mistralai/Mistral-7B-Instruct-v0.2 license.
Where to send questions or comments about the model:
URL
## Intended use
Primary intended uses:
The primary use of LLaVA is research on large multimodal models and chatbots.
Primary intended users:
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
- 158K GPT-generated multimodal instruction-following data.
- 500K academic-task-oriented VQA data mixture.
- 50K GPT-4V data mixture.
- 40K ShareGPT data.
## Evaluation dataset
A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs. | [
"# LLaVA Model Card - PATCHED!\n\nThis is a patched version of the original model, with patches from aliencaocao applied from here.",
"## Model details\n\nModel type:\nLLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data.\nIt is an auto-regressive language model, based on the transformer architecture.\nBase LLM: mistralai/Mistral-7B-Instruct-v0.2\n\nModel date:\nLLaVA-v1.6-Mistral-7B was trained in December 2023.\n\nPaper or resources for more information:\nURL",
"## License\nmistralai/Mistral-7B-Instruct-v0.2 license.\n\nWhere to send questions or comments about the model:\nURL",
"## Intended use\nPrimary intended uses:\nThe primary use of LLaVA is research on large multimodal models and chatbots.\n\nPrimary intended users:\nThe primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.",
"## Training dataset\n- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.\n- 158K GPT-generated multimodal instruction-following data.\n- 500K academic-task-oriented VQA data mixture.\n- 50K GPT-4V data mixture.\n- 40K ShareGPT data.",
"## Evaluation dataset\nA collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs."
] | [
"TAGS\n#transformers #safetensors #llava #text-generation #conversational #license-apache-2.0 #autotrain_compatible #region-us \n",
"# LLaVA Model Card - PATCHED!\n\nThis is a patched version of the original model, with patches from aliencaocao applied from here.",
"## Model details\n\nModel type:\nLLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data.\nIt is an auto-regressive language model, based on the transformer architecture.\nBase LLM: mistralai/Mistral-7B-Instruct-v0.2\n\nModel date:\nLLaVA-v1.6-Mistral-7B was trained in December 2023.\n\nPaper or resources for more information:\nURL",
"## License\nmistralai/Mistral-7B-Instruct-v0.2 license.\n\nWhere to send questions or comments about the model:\nURL",
"## Intended use\nPrimary intended uses:\nThe primary use of LLaVA is research on large multimodal models and chatbots.\n\nPrimary intended users:\nThe primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.",
"## Training dataset\n- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.\n- 158K GPT-generated multimodal instruction-following data.\n- 500K academic-task-oriented VQA data mixture.\n- 50K GPT-4V data mixture.\n- 40K ShareGPT data.",
"## Evaluation dataset\nA collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs."
] | [
42,
33,
102,
29,
66,
83,
37
] | [
"passage: TAGS\n#transformers #safetensors #llava #text-generation #conversational #license-apache-2.0 #autotrain_compatible #region-us \n# LLaVA Model Card - PATCHED!\n\nThis is a patched version of the original model, with patches from aliencaocao applied from here.## Model details\n\nModel type:\nLLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data.\nIt is an auto-regressive language model, based on the transformer architecture.\nBase LLM: mistralai/Mistral-7B-Instruct-v0.2\n\nModel date:\nLLaVA-v1.6-Mistral-7B was trained in December 2023.\n\nPaper or resources for more information:\nURL## License\nmistralai/Mistral-7B-Instruct-v0.2 license.\n\nWhere to send questions or comments about the model:\nURL## Intended use\nPrimary intended uses:\nThe primary use of LLaVA is research on large multimodal models and chatbots.\n\nPrimary intended users:\nThe primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.## Training dataset\n- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.\n- 158K GPT-generated multimodal instruction-following data.\n- 500K academic-task-oriented VQA data mixture.\n- 50K GPT-4V data mixture.\n- 40K ShareGPT data.## Evaluation dataset\nA collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs."
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Subsets and Splits