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reinforcement-learning | 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": "-165.02 +/- 40.95", "name": "mean_reward", "verified": false}]}]}]} | Ferocious0xide/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-30T16:31:01+00:00 |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-pixel_copter-001", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "36.80 +/- 14.90", "name": "mean_reward", "verified": false}]}]}]} | Edgar404/Reinforce-pixel_copter-001 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2024-04-30T16:31:43+00:00 |
null | transformers |
# 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. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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
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[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] | {"library_name": "transformers", "tags": ["trl", "sft"]} | EdBerg/mistral_b_finance_finetuned_test | null | [
"transformers",
"safetensors",
"trl",
"sft",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:32:07+00:00 |
null | null | {} | arysttoto/covid-pneumonia-healthy-classifier | null | [
"region:us"
] | null | 2024-04-30T16:34:50+00:00 |
|
fill-mask | 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. -->
# final-ft__beto-clinical-wl-es__70k-ultrasounds
This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5271
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 254 | 0.8908 |
| No log | 2.0 | 508 | 0.7526 |
| No log | 3.0 | 762 | 0.6801 |
| 0.9011 | 4.0 | 1016 | 0.6608 |
| 0.9011 | 5.0 | 1270 | 0.6265 |
| 0.9011 | 6.0 | 1524 | 0.6014 |
| 0.9011 | 7.0 | 1778 | 0.5934 |
| 0.6433 | 8.0 | 2032 | 0.5762 |
| 0.6433 | 9.0 | 2286 | 0.5650 |
| 0.6433 | 10.0 | 2540 | 0.5667 |
| 0.6433 | 11.0 | 2794 | 0.5629 |
| 0.5899 | 12.0 | 3048 | 0.5446 |
| 0.5899 | 13.0 | 3302 | 0.5390 |
| 0.5899 | 14.0 | 3556 | 0.5454 |
| 0.5899 | 15.0 | 3810 | 0.5270 |
| 0.5625 | 16.0 | 4064 | 0.5277 |
| 0.5625 | 17.0 | 4318 | 0.5387 |
| 0.5625 | 18.0 | 4572 | 0.5206 |
| 0.5625 | 19.0 | 4826 | 0.5150 |
| 0.5508 | 20.0 | 5080 | 0.5271 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_trainer"], "base_model": "plncmm/beto-clinical-wl-es", "model-index": [{"name": "final-ft__beto-clinical-wl-es__70k-ultrasounds", "results": []}]} | manucos/final-ft__beto-clinical-wl-es__70k-ultrasounds | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:plncmm/beto-clinical-wl-es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:34:51+00:00 |
automatic-speech-recognition | transformers |
# 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. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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] | {"library_name": "transformers", "tags": []} | adrianmedinav/whisper-large-v3_ro_epochs_1_2024-04-30_15-16-50 | null | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:34:54+00:00 |
text-generation | transformers | {} | duydatnguyen/vi_opt_poem_generation | null | [
"transformers",
"tensorboard",
"safetensors",
"opt",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T16:35:00+00:00 |
|
text-generation | transformers |
# VILA Model Card
## Model details
**Model type:**
VILA is a visual language model (VLM) pretrained with interleaved image-text data at scale, enabling multi-image VLM. VILA is deployable on the edge, including Jetson Orin and laptop by AWQ 4bit quantization through TinyChat framework. We find: (1) image-text pairs are not enough, interleaved image-text is essential; (2) unfreezing LLM during interleaved image-text pre-training enables in-context learning; (3)re-blending text-only instruction data is crucial to boost both VLM and text-only performance. VILA unveils appealing capabilities, including: multi-image reasoning, in-context learning, visual chain-of-thought, and better world knowledge.
**Model date:**
VILA1.5-40b was trained in May 2024.
**Paper or resources for more information:**
https://github.com/Efficient-Large-Model/VILA
```
@misc{lin2023vila,
title={VILA: On Pre-training for Visual Language Models},
author={Ji Lin and Hongxu Yin and Wei Ping and Yao Lu and Pavlo Molchanov and Andrew Tao and Huizi Mao and Jan Kautz and Mohammad Shoeybi and Song Han},
year={2023},
eprint={2312.07533},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## License
- The code is released under the Apache 2.0 license as found in the [LICENSE](./LICENSE) file.
- The pretrained weights are released under the [CC-BY-NC-SA-4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en).
- The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms:
- [Model License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA
- [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI
- [Dataset Licenses](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/LICENSE) for each one used during training.
**Where to send questions or comments about the model:**
https://github.com/Efficient-Large-Model/VILA/issues
## Intended use
**Primary intended uses:**
The primary use of VILA 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
See [Dataset Preparation](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/README.md) for more details.
## Evaluation dataset
A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs. | {"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["VILA", "VLM"], "pipeline_tag": "text-generation"} | Efficient-Large-Model/VILA1.5-40b | null | [
"transformers",
"safetensors",
"llava_llama",
"VILA",
"VLM",
"text-generation",
"arxiv:2312.07533",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:35:36+00:00 |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "251.17 +/- 22.98", "name": "mean_reward", "verified": false}]}]}]} | AlkQ/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-30T16:35:49+00:00 |
text-generation | transformers | {} | Weni/WeniGPT-Agents-Llama3-5.0.16-SFT-AWQ | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-30T16:36:24+00:00 |
|
text-generation | transformers |
# Uploaded model
- **Developed by:** Cognitus-Stuti
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "orpo"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | Cognitus-Stuti/llama3-8b-orpo-unsloth-merged_v2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"orpo",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:36:30+00:00 |
null | null | {} | pianonyy/whisper-small-hi | null | [
"region:us"
] | null | 2024-04-30T16:36:50+00:00 |
|
visual-question-answering | transformers | {} | ag9900/vilt_finetuned_200 | null | [
"transformers",
"tensorboard",
"safetensors",
"vilt",
"visual-question-answering",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:36:51+00:00 |
|
automatic-speech-recognition | transformers | {"license": "apache-2.0"} | AmirrezaV1/ASR-persian | null | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:36:58+00:00 |
|
reinforcement-learning | 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": "272.37 +/- 19.18", "name": "mean_reward", "verified": false}]}]}]} | Ferocious0xide/ppo-LunarLander-v2.1 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-30T16:37:17+00:00 |
multiple-choice | transformers | {} | Sage91/bert-tiny-swag-tuned-iith | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"multiple-choice",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:37:38+00:00 |
|
null | null | {"license": "apache-2.0"} | shanchen/llama3x8-MoE | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T16:38:57+00:00 |
|
text-generation | transformers |
# 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. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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] | {"library_name": "transformers", "tags": []} | pbelcak/gemma_2b_pmc_short7 | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T16:40:02+00:00 |
null | null | {"license": "mit"} | eovdedn/essay_score_llama3 | null | [
"safetensors",
"license:mit",
"region:us"
] | null | 2024-04-30T16:40:07+00:00 |
|
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4833
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.6599 | 0.5430 | 500 | 1.4833 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_trainer"], "base_model": "google/pegasus-cnn_dailymail", "model-index": [{"name": "pegasus-samsum", "results": []}]} | Arjun9/pegasus-samsum | null | [
"transformers",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:40:55+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** Cognitus-Stuti
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | Cognitus-Stuti/llama3-8b-orpo-unsloth_v2 | null | [
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:41:17+00:00 |
text-generation | transformers |
# Model Card
<p align="center">
<img src="./icon.png" alt="Logo" width="350">
</p>
π [Technical report](https://arxiv.org/abs/2402.11530) | π [Code](https://github.com/BAAI-DCAI/Bunny) | π° [Demo](https://wisemodel.cn/spaces/baai/Bunny)
This is Bunny-v1.0-4B.
Bunny is a family of lightweight but powerful multimodal models. It offers multiple plug-and-play vision encoders, like EVA-CLIP, SigLIP and language backbones, including Phi-3-mini, Llama-3-8B, Phi-1.5, StableLM-2 and Phi-2. To compensate for the decrease in model size, we construct more informative training data by curated selection from a broader data source.
We provide Bunny-v1.0-4B, which is built upon [SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384) and [Phi-3-Mini-4K-Instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct). More details about this model can be found in [GitHub](https://github.com/BAAI-DCAI/Bunny).
| | MME \\(^{\text{P}}\\) | MME \\(^{\text{C}}\\) | MMB \\(^{\text{T/D}}\\) | SEED(-IMG) | MMMU \\(^{\text{V/T}}\\) | VQA \\(^{\text{v2}}\\) | GQA | SQA \\(^{\text{I}}\\) | POPE |
| ------------------ | :--------------: | :--------------: | :----------------: | :--: | :-----------------: | :---------------: | :--: | :--------------: | :--: |
| Bunny-v1.0-4B | 1495.2 | 338.9 | 74.0/73.5 | 64.5(72.1) | 40.1/39.1 | 81.5 | 63.5 | 75.1 | 86.7 |
# Quickstart
Here we show a code snippet to show you how to use the model with transformers.
Before running the snippet, you need to install the following dependencies:
```shell
pip install torch transformers accelerate pillow
```
If the CUDA memory is enough, it would be faster to execute this snippet by setting `CUDA_VISIBLE_DEVICES=0`.
Users especially those in Chinese mainland may want to refer to a HuggingFace [mirror site](https://hf-mirror.com).
```python
import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import warnings
# disable some warnings
transformers.logging.set_verbosity_error()
transformers.logging.disable_progress_bar()
warnings.filterwarnings('ignore')
# set device
device = 'cuda' # or cpu
torch.set_default_device(device)
# create model
model = AutoModelForCausalLM.from_pretrained(
'BAAI/Bunny-v1_0-4B',
torch_dtype=torch.float16, # float32 for cpu
device_map='auto',
trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(
'BAAI/Bunny-v1_0-4B',
trust_remote_code=True)
# text prompt
prompt = 'Why is the image funny?'
text = f"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\n{prompt} ASSISTANT:"
text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image>')]
input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1][1:], dtype=torch.long).unsqueeze(0).to(device)
# image, sample images can be found in images folder
image = Image.open('example_2.png')
image_tensor = model.process_images([image], model.config).to(dtype=model.dtype, device=device)
# generate
output_ids = model.generate(
input_ids,
images=image_tensor,
max_new_tokens=100,
use_cache=True)[0]
print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip())
```
| {"license": "apache-2.0", "inference": false} | BAAI/Bunny-v1_0-4B | null | [
"transformers",
"safetensors",
"bunny-phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:2402.11530",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | null | 2024-04-30T16:43:30+00:00 |
text-generation | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/FallenMerick/Smart-Lemon-Cookie-7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Smart-Lemon-Cookie-7B-GGUF/resolve/main/Smart-Lemon-Cookie-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Smart-Lemon-Cookie-7B-GGUF/resolve/main/Smart-Lemon-Cookie-7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Smart-Lemon-Cookie-7B-GGUF/resolve/main/Smart-Lemon-Cookie-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Smart-Lemon-Cookie-7B-GGUF/resolve/main/Smart-Lemon-Cookie-7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Smart-Lemon-Cookie-7B-GGUF/resolve/main/Smart-Lemon-Cookie-7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Smart-Lemon-Cookie-7B-GGUF/resolve/main/Smart-Lemon-Cookie-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Smart-Lemon-Cookie-7B-GGUF/resolve/main/Smart-Lemon-Cookie-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Smart-Lemon-Cookie-7B-GGUF/resolve/main/Smart-Lemon-Cookie-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Smart-Lemon-Cookie-7B-GGUF/resolve/main/Smart-Lemon-Cookie-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Smart-Lemon-Cookie-7B-GGUF/resolve/main/Smart-Lemon-Cookie-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Smart-Lemon-Cookie-7B-GGUF/resolve/main/Smart-Lemon-Cookie-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Smart-Lemon-Cookie-7B-GGUF/resolve/main/Smart-Lemon-Cookie-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Smart-Lemon-Cookie-7B-GGUF/resolve/main/Smart-Lemon-Cookie-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Smart-Lemon-Cookie-7B-GGUF/resolve/main/Smart-Lemon-Cookie-7B.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Smart-Lemon-Cookie-7B-GGUF/resolve/main/Smart-Lemon-Cookie-7B.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "library_name": "transformers", "tags": ["mergekit", "merge", "mistral", "text-generation"], "base_model": "FallenMerick/Smart-Lemon-Cookie-7B", "quantized_by": "mradermacher"} | mradermacher/Smart-Lemon-Cookie-7B-GGUF | null | [
"transformers",
"gguf",
"mergekit",
"merge",
"mistral",
"text-generation",
"en",
"base_model:FallenMerick/Smart-Lemon-Cookie-7B",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:44:13+00:00 |
image-classification | 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. -->
# swin-tiny-patch4-window7-224-finetuned-student_two_classes
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6649
- Accuracy: 0.8
## 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-06
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3416 | 1.0 | 13 | 0.6767 | 0.81 |
| 0.2745 | 2.0 | 26 | 0.7166 | 0.81 |
| 0.2482 | 3.0 | 39 | 0.7644 | 0.83 |
| 0.2664 | 4.0 | 52 | 0.7142 | 0.81 |
| 0.2372 | 5.0 | 65 | 0.8010 | 0.85 |
| 0.2238 | 6.0 | 78 | 0.8655 | 0.86 |
| 0.1687 | 7.0 | 91 | 0.8156 | 0.83 |
| 0.209 | 8.0 | 104 | 0.8460 | 0.83 |
| 0.4251 | 9.0 | 117 | 0.7301 | 0.81 |
| 0.4392 | 10.0 | 130 | 0.6775 | 0.78 |
| 0.3285 | 11.0 | 143 | 0.7145 | 0.81 |
| 0.3178 | 12.0 | 156 | 0.7431 | 0.83 |
| 0.4715 | 13.0 | 169 | 0.6973 | 0.81 |
| 0.373 | 14.0 | 182 | 0.6912 | 0.81 |
| 0.3378 | 15.0 | 195 | 0.7018 | 0.82 |
| 0.3867 | 16.0 | 208 | 0.6885 | 0.81 |
| 0.3525 | 17.0 | 221 | 0.6761 | 0.81 |
| 0.4253 | 18.0 | 234 | 0.6643 | 0.8 |
| 0.325 | 19.0 | 247 | 0.6643 | 0.8 |
| 0.372 | 20.0 | 260 | 0.6649 | 0.8 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swin-tiny-patch4-window7-224", "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-student_two_classes", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8, "name": "Accuracy"}]}]}]} | system-admin/swin-tiny-patch4-window7-224-finetuned-student_two_classes | null | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:44:29+00:00 |
null | null | {"license": "mit"} | Bluebomber182/Wilbur-Robinson-StyleTTS2-Model | null | [
"license:mit",
"region:us"
] | null | 2024-04-30T16:45:27+00:00 |
|
text-generation | 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. -->
# gpt2_viet_poem_generation
This model is a fine-tuned version of [openai-community/gpt2-medium](https://huggingface.co/openai-community/gpt2-medium) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4415
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 3.1264 | 2.62 | 500 | 2.7535 |
| 2.8412 | 5.24 | 1000 | 2.5196 |
| 2.5781 | 7.85 | 1500 | 2.3428 |
| 2.396 | 10.47 | 2000 | 2.2017 |
| 2.2638 | 13.09 | 2500 | 2.0893 |
| 2.1527 | 15.71 | 3000 | 2.0012 |
| 2.0635 | 18.32 | 3500 | 1.9299 |
| 1.9925 | 20.94 | 4000 | 1.8740 |
| 1.9348 | 23.56 | 4500 | 1.8297 |
| 1.8819 | 26.18 | 5000 | 1.7932 |
| 1.8427 | 28.8 | 5500 | 1.7621 |
| 1.8072 | 31.41 | 6000 | 1.7356 |
| 1.7753 | 34.03 | 6500 | 1.7109 |
| 1.7466 | 36.65 | 7000 | 1.6905 |
| 1.7261 | 39.27 | 7500 | 1.6715 |
| 1.6994 | 41.88 | 8000 | 1.6525 |
| 1.6769 | 44.5 | 8500 | 1.6355 |
| 1.6565 | 47.12 | 9000 | 1.6205 |
| 1.6393 | 49.74 | 9500 | 1.6037 |
| 1.6179 | 52.36 | 10000 | 1.5914 |
| 1.6016 | 54.97 | 10500 | 1.5780 |
| 1.586 | 57.59 | 11000 | 1.5643 |
| 1.5675 | 60.21 | 11500 | 1.5526 |
| 1.5534 | 62.83 | 12000 | 1.5415 |
| 1.5366 | 65.45 | 12500 | 1.5308 |
| 1.5213 | 68.06 | 13000 | 1.5204 |
| 1.507 | 70.68 | 13500 | 1.5086 |
| 1.4925 | 73.3 | 14000 | 1.4997 |
| 1.4766 | 75.92 | 14500 | 1.4906 |
| 1.4628 | 78.53 | 15000 | 1.4821 |
| 1.4511 | 81.15 | 15500 | 1.4727 |
| 1.4349 | 83.77 | 16000 | 1.4666 |
| 1.4207 | 86.39 | 16500 | 1.4581 |
| 1.4086 | 89.01 | 17000 | 1.4479 |
| 1.3942 | 91.62 | 17500 | 1.4453 |
| 1.3834 | 94.24 | 18000 | 1.4373 |
| 1.3667 | 96.86 | 18500 | 1.4330 |
| 1.3549 | 99.48 | 19000 | 1.4237 |
| 1.3408 | 102.09 | 19500 | 1.4186 |
| 1.3286 | 104.71 | 20000 | 1.4136 |
| 1.3146 | 107.33 | 20500 | 1.4127 |
| 1.3031 | 109.95 | 21000 | 1.4067 |
| 1.2879 | 112.57 | 21500 | 1.4044 |
| 1.2753 | 115.18 | 22000 | 1.4021 |
| 1.262 | 117.8 | 22500 | 1.3945 |
| 1.2504 | 120.42 | 23000 | 1.3957 |
| 1.2362 | 123.04 | 23500 | 1.3909 |
| 1.2234 | 125.65 | 24000 | 1.3913 |
| 1.2122 | 128.27 | 24500 | 1.3932 |
| 1.1993 | 130.89 | 25000 | 1.3878 |
| 1.182 | 133.51 | 25500 | 1.3886 |
| 1.1735 | 136.13 | 26000 | 1.3880 |
| 1.1574 | 138.74 | 26500 | 1.3857 |
| 1.1441 | 141.36 | 27000 | 1.3938 |
| 1.1313 | 143.98 | 27500 | 1.3936 |
| 1.117 | 146.6 | 28000 | 1.3941 |
| 1.0998 | 149.21 | 28500 | 1.3986 |
| 1.0894 | 151.83 | 29000 | 1.4054 |
| 1.0736 | 154.45 | 29500 | 1.4068 |
| 1.0602 | 157.07 | 30000 | 1.4098 |
| 1.0432 | 159.69 | 30500 | 1.4135 |
| 1.0324 | 162.3 | 31000 | 1.4237 |
| 1.0151 | 164.92 | 31500 | 1.4188 |
| 0.999 | 167.54 | 32000 | 1.4437 |
| 0.9848 | 170.16 | 32500 | 1.4415 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "openai-community/gpt2-medium", "model-index": [{"name": "gpt2_viet_poem_generation", "results": []}]} | duydatnguyen/gpt2_viet_poem_generation | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2-medium",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T16:45:32+00:00 |
null | null |
based on [TheBloke/X-MythoChronos-13B-GGUF](https://huggingface.co/TheBloke/X-MythoChronos-13B-GGUF)
only keep q_4 format | {"license": "llama2"} | afpro/X-MythoChronos-13B-GGUF | null | [
"gguf",
"license:llama2",
"region:us"
] | null | 2024-04-30T16:46:35+00:00 |
null | null | {"license": "llama3"} | ehartford/dolphin-2.9-llama3-8b-1m | null | [
"license:llama3",
"region:us"
] | null | 2024-04-30T16:47:35+00:00 |
|
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-scam-classification-fine-tuned-elder
This model is a fine-tuned version of [BothBosu/distilbert-scam-classification-v1](https://huggingface.co/BothBosu/distilbert-scam-classification-v1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6626
- Accuracy: 0.925
- Precision: 0.8889
- Recall: 0.9412
- F1: 0.9143
## 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: 0.004
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0 | 6.6667 | 20 | 1.8102 | 0.9 | 0.8421 | 0.9412 | 0.8889 |
| 0.0 | 13.3333 | 40 | 1.7751 | 0.9 | 0.8421 | 0.9412 | 0.8889 |
| 0.0 | 20.0 | 60 | 1.7390 | 0.9 | 0.8421 | 0.9412 | 0.8889 |
| 0.0 | 26.6667 | 80 | 1.7140 | 0.925 | 0.8889 | 0.9412 | 0.9143 |
| 0.0 | 33.3333 | 100 | 1.7016 | 0.925 | 0.8889 | 0.9412 | 0.9143 |
| 0.0 | 40.0 | 120 | 1.6990 | 0.925 | 0.8889 | 0.9412 | 0.9143 |
| 0.0 | 46.6667 | 140 | 1.6819 | 0.925 | 0.8889 | 0.9412 | 0.9143 |
| 0.0 | 53.3333 | 160 | 1.6602 | 0.925 | 0.8889 | 0.9412 | 0.9143 |
| 0.0 | 60.0 | 180 | 1.6626 | 0.925 | 0.8889 | 0.9412 | 0.9143 |
| 0.0 | 66.6667 | 200 | 1.6626 | 0.925 | 0.8889 | 0.9412 | 0.9143 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "BothBosu/distilbert-scam-classification-v1", "model-index": [{"name": "distilbert-scam-classification-fine-tuned-elder", "results": []}]} | autobio/distilbert-scam-classification-fine-tuned-elder | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:BothBosu/distilbert-scam-classification-v1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:47:42+00:00 |
text-generation | transformers |
# Dolphin 2.9 Llama 3 8b 1m π¬
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: https://discord.gg/8fbBeC7ZGx
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
This version of Dolphin has a 1 million token context. I have applied `winglian/llama-3-1m-context-gradient-lora` - created by @gradientai and @winglian and sponsored by @CrusoeCloud
A bug has been found in the Dolphin 2.9 dataset in SystemConversations that causes the model to overly talk about the "SYSTEM MESSAGE". To counter this, we recommend you add a statement in the system message directing the model not to mention the system message. An example system message is "The assistant is named Dolphin. A helpful and friendly AI assistant, Dolphin avoids discussing the system message unless directly asked about it."
My appreciation for the sponsors of Dolphin 2.9:
- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE)
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
[<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: meta-llama/Meta-Llama-3-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false
load_in_8bit: false
load_in_4bit: false
strict: false
model_config:
datasets:
- path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Ultrachat200kunfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/SystemConversations.jsonl
type: sharegpt
conversation: chatml
chat_template: chatml
dataset_prepared_path: /workspace/datasets/dolphin-2.9/thingy
val_set_size: 0.0002
output_dir: ./out
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
gradient_accumulation_steps: 4
micro_batch_size: 3
num_epochs: 3
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
wandb_project: dolphin-2.9-mixtral-8x22b
wandb_watch:
wandb_run_id:
wandb_log_model:
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 4
save_total_limit: 2
save_steps:
evals_per_epoch: 4
eval_sample_packing: false
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
pad_token: "<|end_of_text|>"
tokens:
- "<|im_start|>"
- "<|im_end|>"
```
</details><br>
## Quants
GGUF : https://huggingface.co/QuantFactory/dolphin-2.9-llama3-8b-GGUF
GGUF with imatrix: https://huggingface.co/bartowski/dolphin-2.9-llama3-8b-GGUF
Exllamav2: https://huggingface.co/bartowski/dolphin-2.9-llama3-8b-exl2
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- total_eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 7
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.146 | 0.0005 | 1 | 1.1064 |
| 0.6962 | 0.2501 | 555 | 0.6636 |
| 0.6857 | 0.5001 | 1110 | 0.6503 |
| 0.6592 | 0.7502 | 1665 | 0.6419 |
| 0.6465 | 1.0002 | 2220 | 0.6317 |
| 0.5295 | 1.2395 | 2775 | 0.6408 |
| 0.5302 | 1.4895 | 3330 | 0.6351 |
| 0.5188 | 1.7396 | 3885 | 0.6227 |
| 0.521 | 1.9896 | 4440 | 0.6168 |
| 0.3968 | 2.2289 | 4995 | 0.6646 |
| 0.3776 | 2.4789 | 5550 | 0.6619 |
| 0.3983 | 2.7290 | 6105 | 0.6602 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1 | {"license": "other", "tags": ["generated_from_trainer", "axolotl"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "out", "results": []}]} | cognitivecomputations/dolphin-2.9-llama3-8b-1m | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"conversational",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:abacusai/SystemChat-1.1",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T16:48:10+00:00 |
text-generation | transformers | # [MaziyarPanahi/LlaMAndement-7b-GGUF](https://huggingface.co/MaziyarPanahi/LlaMAndement-7b-GGUF)
- Model creator: [AgentPublic](https://huggingface.co/AgentPublic)
- Original model: [AgentPublic/LlaMAndement-7b](https://huggingface.co/AgentPublic/LlaMAndement-7b)
## Description
[MaziyarPanahi/LlaMAndement-7b-GGUF](https://huggingface.co/MaziyarPanahi/LlaMAndement-7b-GGUF) contains GGUF format model files for [AgentPublic/LlaMAndement-7b](https://huggingface.co/AgentPublic/LlaMAndement-7b).
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
## Special thanks
π Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. | {"tags": ["quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "llama", "llama-2", "text-generation"], "model_name": "LlaMAndement-7b-GGUF", "base_model": "AgentPublic/LlaMAndement-7b", "inference": false, "model_creator": "AgentPublic", "pipeline_tag": "text-generation", "quantized_by": "MaziyarPanahi"} | MaziyarPanahi/LlaMAndement-7b-GGUF | null | [
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"text-generation",
"llama",
"llama-2",
"base_model:AgentPublic/LlaMAndement-7b",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T16:48:19+00:00 |
audio-classification | 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. -->
# distilhubert-finetuned-speech_commands
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the Speech_command_RK dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1895
- Accuracy: 0.9976
## 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: 264
- eval_batch_size: 264
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1911 | 1.0 | 25 | 1.9352 | 0.8859 |
| 1.0366 | 2.0 | 50 | 0.8334 | 0.9915 |
| 0.4879 | 3.0 | 75 | 0.3774 | 0.9939 |
| 0.297 | 4.0 | 100 | 0.2254 | 0.9951 |
| 0.2422 | 5.0 | 125 | 0.1895 | 0.9976 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["speech-commands"], "metrics": ["accuracy"], "base_model": "ntu-spml/distilhubert", "model-index": [{"name": "distilhubert-finetuned-speech_commands", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "Speech_command_RK", "type": "speech-commands"}, "metrics": [{"type": "accuracy", "value": 0.9975728155339806, "name": "Accuracy"}]}]}]} | imrajeshkr/distilhubert-finetuned-speech_commands | null | [
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:speech-commands",
"base_model:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:49:01+00:00 |
text-generation | transformers |
# 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. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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] | {"library_name": "transformers", "tags": []} | nem012/gemma2b-1e-5r1 | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T16:50:18+00:00 |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-cartpole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "173.20 +/- 48.69", "name": "mean_reward", "verified": false}]}]}]} | Zan135/Reinforce-cartpole-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2024-04-30T16:50:20+00:00 |
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. -->
# TempReason-L1
This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the EllieS/TempReason-L1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2934
## 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: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.2862 | 1.0 | 25000 | 0.2934 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.39.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer"], "datasets": ["EllieS/TempReason-L1"], "base_model": "alignment-handbook/zephyr-7b-sft-full", "model-index": [{"name": "TempReason-L1", "results": []}]} | EllieS/TempReason-L1 | null | [
"peft",
"tensorboard",
"safetensors",
"mistral",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"dataset:EllieS/TempReason-L1",
"base_model:alignment-handbook/zephyr-7b-sft-full",
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T16:50:46+00:00 |
text2text-generation | transformers | {} | alexandro767/my_t5_base_for_hw2_title | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T16:51:07+00:00 |
|
null | null | {"license": "apache-2.0"} | moiseserg/fine-tuned-llama2 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T16:53:02+00:00 |
|
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. -->
# tulu2-13b-cost-UI-UF-5e-7
This model is a fine-tuned version of [allenai/tulu-2-13b](https://huggingface.co/allenai/tulu-2-13b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6919
- Rewards/chosen: 0.0201
- Rewards/rejected: 0.0163
- Rewards/accuracies: 0.5695
- Rewards/margins: 0.0038
- Rewards/margins Max: 0.0555
- Rewards/margins Min: -0.0516
- Rewards/margins Std: 0.0355
- Logps/rejected: -313.9302
- Logps/chosen: -326.6726
- Logits/rejected: -0.8648
- Logits/chosen: -1.0116
## 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-07
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Rewards/margins Max | Rewards/margins Min | Rewards/margins Std | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:-------------------:|:-------------------:|:-------------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.661 | 1.0 | 1770 | 0.6919 | 0.0201 | 0.0163 | 0.5695 | 0.0038 | 0.0555 | -0.0516 | 0.0355 | -313.9302 | -326.6726 | -0.8648 | -1.0116 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.39.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "allenai/tulu-2-13b", "model-index": [{"name": "tulu2-13b-cost-UI-UF-5e-7", "results": []}]} | just1nseo/tulu2-13b-cost-UI-UF-5e-7 | null | [
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:allenai/tulu-2-13b",
"region:us"
] | null | 2024-04-30T16:53:04+00:00 |
image-classification | 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. -->
# swin-tiny-patch4-window7-224-finetuned-student_six_classes-finetuned-student_six_classes
This model is a fine-tuned version of [NiharGupte/swin-tiny-patch4-window7-224-finetuned-student_six_classes](https://huggingface.co/NiharGupte/swin-tiny-patch4-window7-224-finetuned-student_six_classes) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4176
- Accuracy: 0.83
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log | 0.9231 | 3 | 0.4943 | 0.78 |
| No log | 1.8462 | 6 | 0.4716 | 0.78 |
| No log | 2.7692 | 9 | 0.4725 | 0.81 |
| 0.3732 | 4.0 | 13 | 0.4678 | 0.78 |
| 0.3732 | 4.9231 | 16 | 0.4779 | 0.78 |
| 0.3732 | 5.8462 | 19 | 0.4564 | 0.79 |
| 0.3459 | 6.7692 | 22 | 0.4556 | 0.82 |
| 0.3459 | 8.0 | 26 | 0.4757 | 0.77 |
| 0.3459 | 8.9231 | 29 | 0.4773 | 0.77 |
| 0.3273 | 9.8462 | 32 | 0.4661 | 0.77 |
| 0.3273 | 10.7692 | 35 | 0.4518 | 0.79 |
| 0.3273 | 12.0 | 39 | 0.4405 | 0.81 |
| 0.2974 | 12.9231 | 42 | 0.4359 | 0.82 |
| 0.2974 | 13.8462 | 45 | 0.4298 | 0.82 |
| 0.2974 | 14.7692 | 48 | 0.4242 | 0.84 |
| 0.2874 | 16.0 | 52 | 0.4199 | 0.84 |
| 0.2874 | 16.9231 | 55 | 0.4185 | 0.83 |
| 0.2874 | 17.8462 | 58 | 0.4179 | 0.83 |
| 0.2737 | 18.4615 | 60 | 0.4176 | 0.83 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "NiharGupte/swin-tiny-patch4-window7-224-finetuned-student_six_classes", "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-student_six_classes-finetuned-student_six_classes", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.83, "name": "Accuracy"}]}]}]} | system-admin/swin-tiny-patch4-window7-224-finetuned-student_six_classes-finetuned-student_six_classes | null | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:NiharGupte/swin-tiny-patch4-window7-224-finetuned-student_six_classes",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:53:09+00:00 |
null | null | {"license": "unknown"} | hautc/x2 | null | [
"license:unknown",
"region:us"
] | null | 2024-04-30T16:53:31+00:00 |
|
text-classification | transformers | {"license": "unlicense"} | mboachie/roberta-large-mnli-appropriate-questions | null | [
"transformers",
"onnx",
"safetensors",
"roberta",
"text-classification",
"license:unlicense",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:54:09+00:00 |
|
null | null | {"license": "unknown"} | mjfan1999/BrantleyGilbert13-14 | null | [
"license:unknown",
"region:us"
] | null | 2024-04-30T16:55:30+00:00 |
|
feature-extraction | transformers |
# 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. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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] | {"library_name": "transformers", "tags": []} | saadliaquat19/Llama-3-Patient-Doctor | null | [
"transformers",
"safetensors",
"llama",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-30T16:56:07+00:00 |
null | null | {"license": "openrail"} | DreemurrPooper/Mario | null | [
"license:openrail",
"region:us"
] | null | 2024-04-30T16:56:34+00:00 |
|
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. -->
# Zaligner-v1
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 5
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "HuggingFaceH4/zephyr-7b-beta", "model-index": [{"name": "Zaligner-v1", "results": []}]} | Ksgk-fy/Zaligner-v1 | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"license:mit",
"region:us"
] | null | 2024-04-30T16:56:40+00:00 |
null | null | {} | gatoch/april30-instruct-pix2pix-model | null | [
"region:us"
] | null | 2024-04-30T16:57:05+00:00 |
|
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. -->
# tulu2-13b-cost-UI-UF-HHRLHF-5e-7
This model is a fine-tuned version of [allenai/tulu-2-13b](https://huggingface.co/allenai/tulu-2-13b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6892
- Rewards/chosen: 0.0042
- Rewards/rejected: -0.0043
- Rewards/accuracies: 0.5820
- Rewards/margins: 0.0085
- Rewards/margins Max: 0.0641
- Rewards/margins Min: -0.0449
- Rewards/margins Std: 0.0365
- Logps/rejected: -315.0963
- Logps/chosen: -326.8977
- Logits/rejected: -0.8867
- Logits/chosen: -1.0136
## 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-07
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Rewards/margins Max | Rewards/margins Min | Rewards/margins Std | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:-------------------:|:-------------------:|:-------------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6522 | 1.0 | 2632 | 0.6892 | 0.0042 | -0.0043 | 0.5820 | 0.0085 | 0.0641 | -0.0449 | 0.0365 | -315.0963 | -326.8977 | -0.8867 | -1.0136 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.39.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "allenai/tulu-2-13b", "model-index": [{"name": "tulu2-13b-cost-UI-UF-HHRLHF-5e-7", "results": []}]} | just1nseo/tulu2-13b-cost-UI-UF-HHRLHF-5e-7 | null | [
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:allenai/tulu-2-13b",
"region:us"
] | null | 2024-04-30T16:58:15+00:00 |
null | diffusers | {"license": "mit"} | nathanReitinger/MNIST-diffusion-threeHundredImages | null | [
"diffusers",
"safetensors",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2024-04-30T16:59:01+00:00 |
|
feature-extraction | transformers |
# 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. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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] | {"library_name": "transformers", "tags": []} | saadliaquat19/Llama-3-Patient-Doctor-2 | null | [
"transformers",
"safetensors",
"llama",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-30T16:59:13+00:00 |
text-generation | 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", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]} | nanxiz/autotrain-qg4sd-zaqzt | null | [
"transformers",
"tensorboard",
"safetensors",
"phi3",
"text-generation",
"autotrain",
"text-generation-inference",
"peft",
"conversational",
"custom_code",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:59:45+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** Cognitus-Stuti
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | Cognitus-Stuti/llama3-8b-orpo-unsloth_v2gguf | null | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T17:01:27+00:00 |
null | null | {} | fiouf/micr | null | [
"region:us"
] | null | 2024-04-30T17:02:40+00:00 |
|
null | transformers |
# 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. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | saadliaquat19/Llama-3-Patient-Doctor-3 | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T17:02:59+00:00 |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-base-imdb-text-classification
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "flan-t5-base-imdb-text-classification", "results": []}]} | amitku123/flan-t5-base-imdb-text-classification | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T17:03:33+00:00 |
video-classification | 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-numbers-augmented
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: 0.1494
- Accuracy: 0.9559
- F1: 0.9562
- Precision: 0.9568
- Recall: 0.9565
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 2816
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.8968 | 0.25 | 704 | 0.8689 | 0.6878 | 0.6885 | 0.7423 | 0.6886 |
| 0.5002 | 1.25 | 1408 | 0.4374 | 0.8542 | 0.8531 | 0.8718 | 0.8535 |
| 0.3627 | 2.25 | 2112 | 0.1109 | 0.9623 | 0.9618 | 0.9647 | 0.9614 |
| 0.0289 | 3.25 | 2816 | 0.0374 | 0.9880 | 0.9880 | 0.9881 | 0.9880 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
| {"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "base_model": "MCG-NJU/videomae-base", "model-index": [{"name": "videomae-base-finetuned-numbers-augmented", "results": []}]} | latif98/videomae-base-finetuned-numbers-augmented | null | [
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T17:03:40+00:00 |
null | null | {} | jelenatest/test2 | null | [
"region:us"
] | null | 2024-04-30T17:03:49+00:00 |
|
null | null | # Abstract Art
Based on Frea Bucklerβs artwork from her Instagram account (purposefully undertrained to be abstract and not infringe on the artistβs own work)
Author: Derrick Schultz | {"license": "mit", "tags": ["StyleGAN2-ada", "StyleGAN2", "GAN", "Autolume"]} | Autolume/FreaGAN | null | [
"StyleGAN2-ada",
"StyleGAN2",
"GAN",
"Autolume",
"license:mit",
"region:us"
] | null | 2024-04-30T17:03:57+00:00 |
text-generation | transformers | {} | TechxGenus/starcoder2-15b-instruct-v0.1-AWQ | null | [
"transformers",
"safetensors",
"starcoder2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-30T17:05:23+00:00 |
|
text-generation | transformers | {} | TechxGenus/starcoder2-15b-instruct-v0.1-GPTQ | null | [
"transformers",
"safetensors",
"starcoder2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-30T17:06:03+00:00 |
|
token-classification | transformers | {} | vishwa27/halueval_syn_all_fb_model_v3_FAC | null | [
"transformers",
"pytorch",
"longformer",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T17:06:42+00:00 |
|
null | transformers | {} | Qdrant/clip-ViT-B-32-vision | null | [
"transformers",
"onnx",
"clip_vision_model",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T17:07:51+00:00 |
|
token-classification | transformers | {} | vishwa27/halueval_syn_all_fb_model_v3_UNSUP | null | [
"transformers",
"pytorch",
"longformer",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T17:07:54+00:00 |
|
null | transformers | {} | Qdrant/clip-ViT-B-32-text | null | [
"transformers",
"onnx",
"clip_text_model",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T17:07:54+00:00 |
|
text-generation | transformers |
# JSL-MedLlama-3-8B-v2.0
[<img src="https://repository-images.githubusercontent.com/104670986/2e728700-ace4-11ea-9cfc-f3e060b25ddf">](http://www.johnsnowlabs.com)
This model is developed by [John Snow Labs](https://www.johnsnowlabs.com/).
This model is available under a [CC-BY-NC-ND](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en) license and must also conform to this [Acceptable Use Policy](https://huggingface.co/johnsnowlabs). If you need to license this model for commercial use, please contact us at [email protected].
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "johnsnowlabs/JSL-MedLlama-3-8B-v2.0"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
## π Evaluation
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|-------------------------------|-------|------|-----:|--------|-----:|---|-----:|
|stem |N/A |none | 0|acc |0.6466|Β± |0.0056|
| | |none | 0|acc_norm|0.6124|Β± |0.0066|
| - medmcqa |Yaml |none | 0|acc |0.6118|Β± |0.0075|
| | |none | 0|acc_norm|0.6118|Β± |0.0075|
| - medqa_4options |Yaml |none | 0|acc |0.6143|Β± |0.0136|
| | |none | 0|acc_norm|0.6143|Β± |0.0136|
| - anatomy (mmlu) | 0|none | 0|acc |0.7185|Β± |0.0389|
| - clinical_knowledge (mmlu) | 0|none | 0|acc |0.7811|Β± |0.0254|
| - college_biology (mmlu) | 0|none | 0|acc |0.8264|Β± |0.0317|
| - college_medicine (mmlu) | 0|none | 0|acc |0.7110|Β± |0.0346|
| - medical_genetics (mmlu) | 0|none | 0|acc |0.8300|Β± |0.0378|
| - professional_medicine (mmlu)| 0|none | 0|acc |0.7868|Β± |0.0249|
| - pubmedqa | 1|none | 0|acc |0.7420|Β± |0.0196|
|Groups|Version|Filter|n-shot| Metric |Value | |Stderr|
|------|-------|------|-----:|--------|-----:|---|-----:|
|stem |N/A |none | 0|acc |0.6466|Β± |0.0056|
| | |none | 0|acc_norm|0.6124|Β± |0.0066| | {"license": "cc-by-nc-nd-4.0", "tags": ["llama-3-8b", "sft", "medical"], "base_model": ["meta-llama/Meta-Llama-3-8B"]} | johnsnowlabs/JSL-MedLlama-3-8B-v2.0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-3-8b",
"sft",
"medical",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:cc-by-nc-nd-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T17:08:30+00:00 |
text-generation | transformers |
# PULI LlumiX 32K instruct (6.74B billion parameter)
Intruct finetuned version of NYTK/PULI-LlumiX-32K.
## Training platform
[Lightning AI Studio](https://lightning.ai/studios) L4 GPU
## Hyper parameters
- Epoch: 3
- LoRA rank (r): 16
- LoRA alpha: 16
- Lr: 2e-4
- Lr scheduler: cosine
- Optimizer: adamw_8bit
- Weight decay: 0.01
## Dataset
boapps/alpaca-cleaned-gemini-hun-ratings
Only instructions with rating >= 9 were used.
In total ~25k instructions were selected where the length of prompts were less than equal to 512 characters.
## Prompt template: ChatML
```
<|im_start|>system
Az alΓ‘bbiakban egy feladatot leΓrΓ³ utasΓtΓ‘s talΓ‘lhatΓ³. ΓrjΓ‘l olyan vΓ‘laszt, amely megfelelΕen teljesΓti a kΓ©rΓ©st.<|im_end|>
<|im_start|>user
Ki a legerΕsebb szuperhΕs?<|im_end|>
<|im_start|>assistant
A legerΕsebb szuperhΕs a Marvel univerzumΓ‘ban Hulk.<|im_end|>
```
## Base model
- Trained with OpenChatKit [github](https://github.com/togethercomputer/OpenChatKit)
- The [LLaMA-2-7B-32K](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K) model were continuously pretrained on Hungarian dataset
- The model has been extended to a context length of 32K with position interpolation
- Checkpoint: 100 000 steps
## Dataset for continued pretraining
- Hungarian: 7.9 billion words, documents (763K) that exceed 5000 words in length
- English: Long Context QA (2 billion words), BookSum (78 million words)
## Limitations
- max_seq_length = 32 768
- float16
- vocab size: 32 000 | {"language": ["hu", "en"], "license": "llama2", "tags": ["puli", "text-generation-inference", "transformers", "unsloth", "llama", "trl", "finetuned"], "datasets": ["boapps/alpaca-cleaned-gemini-hun-ratings"], "base_model": "NYTK/PULI-LlumiX-32K", "pipeline_tag": "text-generation"} | ariel-ml/PULI-LlumiX-instruct-32K-16bit | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"puli",
"text-generation-inference",
"unsloth",
"trl",
"finetuned",
"conversational",
"custom_code",
"hu",
"en",
"dataset:boapps/alpaca-cleaned-gemini-hun-ratings",
"base_model:NYTK/PULI-LlumiX-32K",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T17:09:07+00:00 |
text-generation | transformers |
# 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. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | OwOOwO/finalupdate1 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T17:10:52+00:00 |
null | null | {} | fiouf/lo | null | [
"region:us"
] | null | 2024-04-30T17:11:26+00:00 |
|
null | null | Number of experts present in the library: 8
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| e0 | phi-2 | sordonia/flan-10k-flat/None | skilled_lora |
| e1 | phi-2 | sordonia/flan-10k-flat/None | skilled_lora |
| e2 | phi-2 | sordonia/flan-10k-flat/None | skilled_lora |
| e3 | phi-2 | sordonia/flan-10k-flat/None | skilled_lora |
| e4 | phi-2 | sordonia/flan-10k-flat/None | skilled_lora |
| e5 | phi-2 | sordonia/flan-10k-flat/None | skilled_lora |
| e6 | phi-2 | sordonia/flan-10k-flat/None | skilled_lora |
| e7 | phi-2 | sordonia/flan-10k-flat/None | skilled_lora |
Last updated on: 2024-04-30T17:11:45.000Z
| {} | pclucas14/phi2_poly_S1_1ep | null | [
"region:us"
] | null | 2024-04-30T17:11:45+00:00 |
null | transformers |
# PULI LlumiX 32K instruct LoRA adapter (6.74B billion parameter)
Intruct finetuned version of NYTK/PULI-LlumiX-32K.
## Training platform
[Lightning AI Studio](https://lightning.ai/studios) L4 GPU
## Hyper parameters
- Epoch: 3
- LoRA rank (r): 16
- LoRA alpha: 16
- Lr: 2e-4
- Lr scheduler: cosine
- Optimizer: adamw_8bit
- Weight decay: 0.01
## Dataset
boapps/alpaca-cleaned-gemini-hun-ratings
Only instructions with rating >= 9 were used.
In total ~25k instructions were selected where the length of prompts were less than equal to 512 characters.
## Prompt template: ChatML
```
<|im_start|>system
Az alΓ‘bbiakban egy feladatot leΓrΓ³ utasΓtΓ‘s talΓ‘lhatΓ³. ΓrjΓ‘l olyan vΓ‘laszt, amely megfelelΕen teljesΓti a kΓ©rΓ©st.<|im_end|>
<|im_start|>user
Ki a legerΕsebb szuperhΕs?<|im_end|>
<|im_start|>assistant
A legerΕsebb szuperhΕs a Marvel univerzumΓ‘ban Hulk.<|im_end|>
```
## Base model
- Trained with OpenChatKit [github](https://github.com/togethercomputer/OpenChatKit)
- The [LLaMA-2-7B-32K](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K) model were continuously pretrained on Hungarian dataset
- The model has been extended to a context length of 32K with position interpolation
- Checkpoint: 100 000 steps
## Dataset for continued pretraining
- Hungarian: 7.9 billion words, documents (763K) that exceed 5000 words in length
- English: Long Context QA (2 billion words), BookSum (78 million words)
## Limitations
- max_seq_length = 32 768
- float16
- vocab size: 32 000 | {"language": ["hu", "en"], "license": "llama2", "tags": ["puli", "text-generation-inference", "transformers", "unsloth", "llama", "trl", "finetuned", "lora"], "datasets": ["boapps/alpaca-cleaned-gemini-hun-ratings"], "base_model": "NYTK/PULI-LlumiX-32K"} | ariel-ml/PULI-LlumiX-instruct-32K-lora | null | [
"transformers",
"safetensors",
"puli",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"finetuned",
"lora",
"hu",
"en",
"dataset:boapps/alpaca-cleaned-gemini-hun-ratings",
"base_model:NYTK/PULI-LlumiX-32K",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T17:11:53+00:00 |
null | null | {} | lmh2011/m2m100_418M-en-vi | null | [
"region:us"
] | null | 2024-04-30T17:12:37+00:00 |
|
text-to-image | 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 - aimickey/alishemenu-sdv1-4-lora-v1
<Gallery />
## Model description
These are aimickey/alishemenu-sdv1-4-lora-v1 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 menu images in the style of TOK to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](aimickey/alishemenu-sdv1-4-lora-v1/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", "text-to-image", "diffusers-training", "diffusers", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "menu images in the style of TOK", "widget": []} | aimickey/alishemenu-sdv1-4-lora-v1 | null | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null | 2024-04-30T17:16:21+00:00 |
text-generation | transformers |
# Model Card for Model ID
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## Model Details
### Model Description
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This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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## 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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | abc88767/model21 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T17:16:25+00:00 |
null | transformers |
# 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. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- 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]
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[More Information Needed]
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[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]
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<!-- 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 Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Fawazzx/Saul-instruct | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T17:17:07+00:00 |
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. -->
# outputs
This model is a fine-tuned version of [Fawazzx/Saul-Instruct-v1_sharded](https://huggingface.co/Fawazzx/Saul-Instruct-v1_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: 2
- eval_batch_size: 8
- seed: 3407
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 120
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "Fawazzx/Saul-Instruct-v1_sharded", "model-index": [{"name": "outputs", "results": []}]} | Fawazzx/outputs | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:Fawazzx/Saul-Instruct-v1_sharded",
"region:us"
] | null | 2024-04-30T17:17:09+00:00 |
null | null | {} | asakti47/DS301_HW6 | null | [
"region:us"
] | null | 2024-04-30T17:17:33+00:00 |
|
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.9.0 | {"license": "apache-2.0", "library_name": "peft", "base_model": "google/gemma-2b-it"} | azarafrooz/phi-gemma-nlaf-v0 | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b-it",
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T17:18:06+00:00 |
null | null | {"license": "mit"} | Pranjalya/stable-diffusion-models | null | [
"region:us"
] | null | 2024-04-30T17:18:21+00:00 |
|
text2text-generation | transformers | {"license": "unlicense"} | mboachie/asset_title_gen | null | [
"transformers",
"pytorch",
"onnx",
"bart",
"text2text-generation",
"license:unlicense",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T17:18:49+00:00 |
|
text-generation | transformers |
# llama3-slerp-med
llama3-slerp-med is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [aaditya/Llama3-OpenBioLLM-8B](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B)
* [johnsnowlabs/JSL-MedLlama-3-8B-v1.0](https://huggingface.co/johnsnowlabs/JSL-MedLlama-3-8B-v1.0)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: aaditya/Llama3-OpenBioLLM-8B
layer_range: [0, 32]
- model: johnsnowlabs/JSL-MedLlama-3-8B-v1.0
layer_range: [0, 32]
merge_method: slerp
base_model: aaditya/Llama3-OpenBioLLM-8B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "shanchen/llama3-slerp-med"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "llama3", "tags": ["merge", "mergekit", "lazymergekit", "aaditya/Llama3-OpenBioLLM-8B", "johnsnowlabs/JSL-MedLlama-3-8B-v1.0"], "base_model": ["aaditya/Llama3-OpenBioLLM-8B", "johnsnowlabs/JSL-MedLlama-3-8B-v1.0"]} | shanchen/llama3-slerp-med | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"aaditya/Llama3-OpenBioLLM-8B",
"johnsnowlabs/JSL-MedLlama-3-8B-v1.0",
"base_model:aaditya/Llama3-OpenBioLLM-8B",
"base_model:johnsnowlabs/JSL-MedLlama-3-8B-v1.0",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T17:19:27+00:00 |
video-classification | 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-kinetics-finetuned-ucf101-subset
This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1277
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2722 | 0.25 | 75 | 0.1311 | 1.0 |
| 0.0765 | 1.25 | 150 | 0.0185 | 1.0 |
| 0.0072 | 2.25 | 225 | 0.0096 | 1.0 |
| 0.0059 | 3.25 | 300 | 0.0094 | 1.0 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "MCG-NJU/videomae-base-finetuned-kinetics", "model-index": [{"name": "videomae-base-finetuned-kinetics-finetuned-ucf101-subset", "results": []}]} | Abdullah1/videomae-base-finetuned-kinetics-finetuned-ucf101-subset | null | [
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base-finetuned-kinetics",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T17:20:32+00:00 |
null | null | {"license": "mit"} | FinCatch/Llama-3-8b-FB-100-SFI-177k-qaAndqaConv | null | [
"safetensors",
"license:mit",
"region:us"
] | null | 2024-04-30T17:22:24+00:00 |
|
null | null | {"license": "openrail"} | Danikdsa/Chaehyun | null | [
"license:openrail",
"region:us"
] | null | 2024-04-30T17:22:26+00:00 |
|
image-classification | 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. -->
# platzi-vit-model-damian-r
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0334
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0057 | 3.85 | 500 | 0.0334 | 0.9850 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "platzi-vit-model-damian-r", "results": []}]} | platzi/platzi-vit-model-damian-r | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T17:22:38+00:00 |
null | null | {} | Amrit05/bert_swag_dataset_finetuned | null | [
"region:us"
] | null | 2024-04-30T17:24:11+00:00 |
|
automatic-speech-recognition | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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<!-- 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]
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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. -->
[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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- **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
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[More Information Needed]
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[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **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] | {"library_name": "transformers", "tags": []} | ivillar/whisperfinetune | null | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T17:25:15+00:00 |
null | peft | ## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
| {"library_name": "peft"} | lekhapinninti/llama-2-7b-enhanced-10epoch-samplingdata | null | [
"peft",
"region:us"
] | null | 2024-04-30T17:25:42+00:00 |
text-generation | transformers |
# 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. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- 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] | {"library_name": "transformers", "tags": []} | Thirawarit/openthaigpt-1.0.0-7b-chat-ft-by-Scamper | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T17:27:00+00:00 |
text-generation | transformers | {} | spielhoelle/llama-3-8B-instruct-request-response-emails | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T17:28:13+00:00 |
|
text-generation | transformers |
# 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. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### 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
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[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. -->
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[More Information Needed]
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[More Information Needed]
## 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 Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | nessa01macias/gemma-2b_sustainability-qa | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T17:28:25+00:00 |
null | null | {} | TenzinGayche/tibetan_tokenizer_v1 | null | [
"region:us"
] | null | 2024-04-30T17:29:53+00:00 |
|
text-generation | transformers | {} | sanchit-gandhi/distil-mistral-1.5B-Instruct-v0.2-cosmo-200k | null | [
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T17:30:06+00:00 |
|
text-classification | 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. -->
# NDD-mantisbt_test-content
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0915
- Accuracy: 0.9609
- F1: 0.9627
- Precision: 0.9667
- Recall: 0.9609
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1248 | 1.0 | 672 | 0.1214 | 0.9355 | 0.9413 | 0.9555 | 0.9355 |
| 0.0658 | 2.0 | 1344 | 0.0915 | 0.9609 | 0.9627 | 0.9667 | 0.9609 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "NDD-mantisbt_test-content", "results": []}]} | lgk03/NDD-mantisbt_test-content | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T17:30:53+00:00 |
null | null | {} | hyunn88/asabyaisings | null | [
"region:us"
] | null | 2024-04-30T17:31:30+00:00 |
|
null | null | {} | Xrunner/hive-n | null | [
"region:us"
] | null | 2024-04-30T17:31:43+00:00 |
|
null | null | {} | randomuser5689/test | null | [
"region:us"
] | null | 2024-04-30T17:31:44+00:00 |
|
null | null | {} | de0nis2011/vladik | null | [
"region:us"
] | null | 2024-04-30T17:32:02+00:00 |
|
text-classification | transformers |
# 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. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
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<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Out-of-Scope Use
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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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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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | HC-85/distilbert-arxiv-multilabel-b16-test | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T17:32:28+00:00 |
null | null | {} | wwhlazio/TrueFakeNews | null | [
"region:us"
] | null | 2024-04-30T17:32:41+00:00 |
|
null | null | {"license": "mit"} | pytc/mito | null | [
"license:mit",
"region:us"
] | null | 2024-04-30T17:32:46+00:00 |
|
text-generation | transformers |
# VILA Model Card
## Model details
**Model type:**
VILA is a visual language model (VLM) pretrained with interleaved image-text data at scale, enabling multi-image VLM. VILA is deployable on the edge, including Jetson Orin and laptop by AWQ 4bit quantization through TinyChat framework. We find: (1) image-text pairs are not enough, interleaved image-text is essential; (2) unfreezing LLM during interleaved image-text pre-training enables in-context learning; (3)re-blending text-only instruction data is crucial to boost both VLM and text-only performance. VILA unveils appealing capabilities, including: multi-image reasoning, in-context learning, visual chain-of-thought, and better world knowledge.
**Model date:**
VILA1.5-3b-s2 was trained in May 2024.
**Paper or resources for more information:**
https://github.com/Efficient-Large-Model/VILA
```
@misc{lin2023vila,
title={VILA: On Pre-training for Visual Language Models},
author={Ji Lin and Hongxu Yin and Wei Ping and Yao Lu and Pavlo Molchanov and Andrew Tao and Huizi Mao and Jan Kautz and Mohammad Shoeybi and Song Han},
year={2023},
eprint={2312.07533},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## License
- The code is released under the Apache 2.0 license as found in the [LICENSE](./LICENSE) file.
- The pretrained weights are released under the [CC-BY-NC-SA-4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en).
- The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms:
- [Model License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA
- [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI
- [Dataset Licenses](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/LICENSE) for each one used during training.
**Where to send questions or comments about the model:**
https://github.com/Efficient-Large-Model/VILA/issues
## Intended use
**Primary intended uses:**
The primary use of VILA 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
See [Dataset Preparation](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/README.md) for more details.
## Evaluation dataset
A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs. | {"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["VILA", "VLM"], "pipeline_tag": "text-generation"} | Efficient-Large-Model/VILA1.5-3b-s2 | null | [
"transformers",
"safetensors",
"llava_llama",
"VILA",
"VLM",
"text-generation",
"arxiv:2312.07533",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T17:33:13+00:00 |
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