Search is not available for this dataset
pipeline_tag
stringclasses 48
values | library_name
stringclasses 205
values | text
stringlengths 0
18.3M
| metadata
stringlengths 2
1.07B
| id
stringlengths 5
122
| last_modified
null | tags
listlengths 1
1.84k
| sha
null | created_at
stringlengths 25
25
|
|---|---|---|---|---|---|---|---|---|
text-generation
|
transformers
|
!!! A new version of the model has been released. I didn’t find any problems with word duplication, but I can’t promise anything. https://huggingface.co/SolidSnacke/Llama-3-Soliloquy-8B-v1.5-64k-i-GGUF
Edit: Currently this model has a problem with repeating words. That is, at some point you may experience duplication, like: passing by the table, he caught a red red red red red red...
The problem most likely, as the author of the model explained, may be the wrong EOS token, but this is not certain. The author in another repository wrote that he will soon release a new model, so we are waiting.
I don't know what to write here.
Links to the original model and script:
- openlynn/Llama-3-Soliloquy-8B: https://huggingface.co/openlynn/Llama-3-Soliloquy-8B
- FantasiaFoundry/GGUF-Quantization-Script: https://huggingface.co/FantasiaFoundry/GGUF-Quantization-Script
|
{"language": ["en"], "license": "cc-by-nc-sa-4.0", "library_name": "transformers", "tags": ["llama", "text-generation-inference"], "pipeline_tag": "text-generation"}
|
SolidSnacke/Llama-3-Soliloquy-8B-i-GGUF
| null |
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"text-generation",
"en",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:03:57+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 🤗 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": []}
|
ripaaiii/fine-tune-C1-stage1_15epoch_besar
| null |
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:04:33+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. -->
# Boya1_RMSProp_1-e5_10Epoch_swin-base-window7-224-in22k_fold4
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2264
- Accuracy: 0.6711
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2097 | 1.0 | 924 | 1.1129 | 0.6221 |
| 0.878 | 2.0 | 1848 | 1.0367 | 0.6286 |
| 0.6969 | 3.0 | 2772 | 1.0137 | 0.6546 |
| 0.7747 | 4.0 | 3696 | 0.9819 | 0.6762 |
| 0.5932 | 5.0 | 4620 | 1.0142 | 0.6749 |
| 0.362 | 6.0 | 5544 | 1.0712 | 0.6714 |
| 0.3792 | 7.0 | 6468 | 1.1118 | 0.6741 |
| 0.3714 | 8.0 | 7392 | 1.1719 | 0.6762 |
| 0.3201 | 9.0 | 8316 | 1.2057 | 0.6687 |
| 0.2778 | 10.0 | 9240 | 1.2264 | 0.6711 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swin-base-patch4-window7-224-in22k", "model-index": [{"name": "Boya1_RMSProp_1-e5_10Epoch_swin-base-window7-224-in22k_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6710918450284475, "name": "Accuracy"}]}]}]}
|
onizukal/Boya1_RMSProp_1-e5_10Epoch_swin-base-window7-224-in22k_fold4
| null |
[
"transformers",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-base-patch4-window7-224-in22k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:04:42+00:00
|
question-answering
|
transformers
|
{}
|
lanzv/ClinicalBERTPRQABCZ_9_111_CS
| null |
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:04:42+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. -->
# dpo_harmlessharmless_human_gamma0.0_beta0.1_subset-1_modelmistral7b_maxsteps5000_bz8_lr1e-06
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 15
- training_steps: 5000
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "dpo_harmlessharmless_human_gamma0.0_beta0.1_subset-1_modelmistral7b_maxsteps5000_bz8_lr1e-06", "results": []}]}
|
Holarissun/dpo_harmlessharmless_human_gamma0.0_beta0.1_subset-1_modelmistral7b_maxsteps5000_bz8_lr1e-06
| null |
[
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T12:05:08+00:00
|
text-generation
|
transformers
|
{}
|
Hakatoma/fine_tuned_science_gemma2b-it
| null |
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:06:26+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 🤗 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": []}
|
Keetawan/image_captioning_1
| null |
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:06:39+00:00
|
null | null |
{"language": ["en"], "license": "mit", "tags": ["recommendation", "collaborative filtering"], "datasets": ["user_rating_1000.csv"], "metrics": ["RMSE"]}
|
hscrown/rating_museum
| null |
[
"recommendation",
"collaborative filtering",
"en",
"dataset:user_rating_1000.csv",
"license:mit",
"region:us"
] | null |
2024-04-25T12:06:46+00:00
|
|
null | null |
LLM for using at khoj project
|
{"license": "llama2"}
|
ajinkgupta/khoj-7b
| null |
[
"license:llama2",
"region:us"
] | null |
2024-04-25T12:06:57+00:00
|
null |
diffusers
|
{}
|
RonenWeiz/encdec_debug_model_overfit_10
| null |
[
"diffusers",
"tensorboard",
"safetensors",
"diffusers:StableDiffusionInstructPix2PixPipeline",
"region:us"
] | null |
2024-04-25T12:08:29+00:00
|
|
null | null |
{"license": "openrail"}
|
Cloudxego/ChioChicano
| null |
[
"license:openrail",
"region:us"
] | null |
2024-04-25T12:08:32+00:00
|
|
null | null |
{}
|
cyberpantom/qy-zitn
| null |
[
"region:us"
] | null |
2024-04-25T12:08:44+00:00
|
|
text-generation
|
transformers
|
{}
|
Ma234/sql-classification-llama-2-7b-test
| null |
[
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:10:03+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 🤗 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": []}
|
happylayers/sc20
| null |
[
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:10:07+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 🤗 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": []}
|
m-gopichand/x-small
| null |
[
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:10:13+00:00
|
null | null |
{}
|
ACEGameAI/Yuna-Aoki_ohwx-woman
| null |
[
"region:us"
] | null |
2024-04-25T12:10:31+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. -->
# dpo_harmlessharmless_human_gamma100.0_beta0.1_subset-1_modelmistral7b_maxsteps5000_bz8_lr1e-06
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 15
- training_steps: 5000
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "dpo_harmlessharmless_human_gamma100.0_beta0.1_subset-1_modelmistral7b_maxsteps5000_bz8_lr1e-06", "results": []}]}
|
Holarissun/dpo_harmlessharmless_human_gamma100.0_beta0.1_subset-1_modelmistral7b_maxsteps5000_bz8_lr1e-06
| null |
[
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T12:11:05+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. -->
# dpo_harmlessharmless_human_gamma5.0_beta0.1_subset-1_modelmistral7b_maxsteps5000_bz8_lr1e-06
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 15
- training_steps: 5000
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "dpo_harmlessharmless_human_gamma5.0_beta0.1_subset-1_modelmistral7b_maxsteps5000_bz8_lr1e-06", "results": []}]}
|
Holarissun/dpo_harmlessharmless_human_gamma5.0_beta0.1_subset-1_modelmistral7b_maxsteps5000_bz8_lr1e-06
| null |
[
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T12:11:08+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. -->
# RM-HH-GPT2Large_helpful_gpt3_loraR64_40000_gpt2-large_shuffleTrue_extractchosenFalse
This model is a fine-tuned version of [openai-community/gpt2-large](https://huggingface.co/openai-community/gpt2-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4752
- Accuracy: 0.7446
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.41e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6556 | 0.02 | 250 | 0.6162 | 0.6465 |
| 0.5807 | 0.04 | 500 | 0.5552 | 0.6974 |
| 0.5449 | 0.06 | 750 | 0.5322 | 0.7111 |
| 0.5024 | 0.08 | 1000 | 0.5223 | 0.7145 |
| 0.5227 | 0.1 | 1250 | 0.5184 | 0.7147 |
| 0.52 | 0.13 | 1500 | 0.5156 | 0.7175 |
| 0.5127 | 0.15 | 1750 | 0.5127 | 0.7143 |
| 0.4988 | 0.17 | 2000 | 0.5081 | 0.7198 |
| 0.5129 | 0.19 | 2250 | 0.5051 | 0.7217 |
| 0.4995 | 0.21 | 2500 | 0.5028 | 0.7254 |
| 0.508 | 0.23 | 2750 | 0.5009 | 0.7264 |
| 0.4953 | 0.25 | 3000 | 0.4992 | 0.7277 |
| 0.5049 | 0.27 | 3250 | 0.4986 | 0.7275 |
| 0.5042 | 0.29 | 3500 | 0.4974 | 0.7309 |
| 0.4971 | 0.31 | 3750 | 0.4958 | 0.7286 |
| 0.4871 | 0.33 | 4000 | 0.4950 | 0.7316 |
| 0.5001 | 0.36 | 4250 | 0.4938 | 0.7277 |
| 0.507 | 0.38 | 4500 | 0.4927 | 0.7290 |
| 0.5242 | 0.4 | 4750 | 0.4889 | 0.7320 |
| 0.5212 | 0.42 | 5000 | 0.4876 | 0.7350 |
| 0.4955 | 0.44 | 5250 | 0.4864 | 0.7354 |
| 0.4809 | 0.46 | 5500 | 0.4884 | 0.7363 |
| 0.4784 | 0.48 | 5750 | 0.4865 | 0.7326 |
| 0.4933 | 0.5 | 6000 | 0.4857 | 0.7348 |
| 0.5008 | 0.52 | 6250 | 0.4827 | 0.7335 |
| 0.5045 | 0.54 | 6500 | 0.4816 | 0.7358 |
| 0.4757 | 0.57 | 6750 | 0.4843 | 0.7356 |
| 0.5055 | 0.59 | 7000 | 0.4819 | 0.7318 |
| 0.4984 | 0.61 | 7250 | 0.4800 | 0.7362 |
| 0.5073 | 0.63 | 7500 | 0.4813 | 0.7354 |
| 0.4512 | 0.65 | 7750 | 0.4836 | 0.7375 |
| 0.4841 | 0.67 | 8000 | 0.4830 | 0.7380 |
| 0.4621 | 0.69 | 8250 | 0.4854 | 0.7392 |
| 0.487 | 0.71 | 8500 | 0.4807 | 0.7388 |
| 0.474 | 0.73 | 8750 | 0.4814 | 0.7422 |
| 0.4921 | 0.75 | 9000 | 0.4826 | 0.7412 |
| 0.4687 | 0.77 | 9250 | 0.4794 | 0.7401 |
| 0.4793 | 0.8 | 9500 | 0.4780 | 0.7407 |
| 0.4659 | 0.82 | 9750 | 0.4779 | 0.7399 |
| 0.4821 | 0.84 | 10000 | 0.4774 | 0.7403 |
| 0.499 | 0.86 | 10250 | 0.4786 | 0.7416 |
| 0.5152 | 0.88 | 10500 | 0.4747 | 0.7394 |
| 0.4566 | 0.9 | 10750 | 0.4764 | 0.7405 |
| 0.45 | 0.92 | 11000 | 0.4761 | 0.7394 |
| 0.4726 | 0.94 | 11250 | 0.4794 | 0.7397 |
| 0.468 | 0.96 | 11500 | 0.4784 | 0.7390 |
| 0.4951 | 0.98 | 11750 | 0.4744 | 0.7395 |
| 0.5151 | 1.0 | 12000 | 0.4741 | 0.7382 |
| 0.4632 | 1.03 | 12250 | 0.4751 | 0.7409 |
| 0.4746 | 1.05 | 12500 | 0.4742 | 0.7422 |
| 0.4858 | 1.07 | 12750 | 0.4743 | 0.7424 |
| 0.482 | 1.09 | 13000 | 0.4751 | 0.7426 |
| 0.4527 | 1.11 | 13250 | 0.4798 | 0.7431 |
| 0.4709 | 1.13 | 13500 | 0.4773 | 0.7437 |
| 0.457 | 1.15 | 13750 | 0.4779 | 0.7426 |
| 0.4771 | 1.17 | 14000 | 0.4817 | 0.7414 |
| 0.4755 | 1.19 | 14250 | 0.4792 | 0.7439 |
| 0.4548 | 1.21 | 14500 | 0.4816 | 0.7446 |
| 0.4459 | 1.23 | 14750 | 0.4841 | 0.7424 |
| 0.4699 | 1.26 | 15000 | 0.4798 | 0.7409 |
| 0.4373 | 1.28 | 15250 | 0.4848 | 0.7392 |
| 0.4754 | 1.3 | 15500 | 0.4826 | 0.7409 |
| 0.4806 | 1.32 | 15750 | 0.4792 | 0.7414 |
| 0.4934 | 1.34 | 16000 | 0.4779 | 0.7431 |
| 0.4501 | 1.36 | 16250 | 0.4797 | 0.7426 |
| 0.4654 | 1.38 | 16500 | 0.4769 | 0.7439 |
| 0.4839 | 1.4 | 16750 | 0.4763 | 0.7446 |
| 0.4942 | 1.42 | 17000 | 0.4769 | 0.7429 |
| 0.4391 | 1.44 | 17250 | 0.4810 | 0.7426 |
| 0.4947 | 1.46 | 17500 | 0.4787 | 0.7422 |
| 0.4644 | 1.49 | 17750 | 0.4819 | 0.7422 |
| 0.4945 | 1.51 | 18000 | 0.4775 | 0.7420 |
| 0.4333 | 1.53 | 18250 | 0.4830 | 0.7418 |
| 0.4863 | 1.55 | 18500 | 0.4795 | 0.7416 |
| 0.4843 | 1.57 | 18750 | 0.4777 | 0.7414 |
| 0.4772 | 1.59 | 19000 | 0.4771 | 0.7427 |
| 0.4834 | 1.61 | 19250 | 0.4765 | 0.7422 |
| 0.4673 | 1.63 | 19500 | 0.4758 | 0.7424 |
| 0.4795 | 1.65 | 19750 | 0.4763 | 0.7424 |
| 0.4808 | 1.67 | 20000 | 0.4764 | 0.7427 |
| 0.4593 | 1.7 | 20250 | 0.4772 | 0.7427 |
| 0.4947 | 1.72 | 20500 | 0.4771 | 0.7431 |
| 0.4776 | 1.74 | 20750 | 0.4770 | 0.7444 |
| 0.4841 | 1.76 | 21000 | 0.4764 | 0.7452 |
| 0.4536 | 1.78 | 21250 | 0.4766 | 0.7441 |
| 0.4772 | 1.8 | 21500 | 0.4766 | 0.7444 |
| 0.4668 | 1.82 | 21750 | 0.4766 | 0.7439 |
| 0.4884 | 1.84 | 22000 | 0.4750 | 0.7444 |
| 0.498 | 1.86 | 22250 | 0.4745 | 0.7446 |
| 0.47 | 1.88 | 22500 | 0.4743 | 0.7443 |
| 0.4524 | 1.9 | 22750 | 0.4752 | 0.7448 |
| 0.4885 | 1.93 | 23000 | 0.4754 | 0.7441 |
| 0.4734 | 1.95 | 23250 | 0.4752 | 0.7448 |
| 0.4882 | 1.97 | 23500 | 0.4753 | 0.7446 |
| 0.4929 | 1.99 | 23750 | 0.4752 | 0.7446 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "library_name": "peft", "tags": ["trl", "reward-trainer", "generated_from_trainer"], "metrics": ["accuracy"], "base_model": "openai-community/gpt2-large", "model-index": [{"name": "RM-HH-GPT2Large_helpful_gpt3_loraR64_40000_gpt2-large_shuffleTrue_extractchosenFalse", "results": []}]}
|
Holarissun/RM-HH-GPT2Large_helpful_gpt3_loraR64_40000_gpt2-large_shuffleTrue_extractchosenFalse
| null |
[
"peft",
"safetensors",
"trl",
"reward-trainer",
"generated_from_trainer",
"base_model:openai-community/gpt2-large",
"license:mit",
"region:us"
] | null |
2024-04-25T12:13:06+00:00
|
text-generation
|
transformers
|
{}
|
realtluu/fine_tuned_science_gemma2b-it
| null |
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:13:09+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. -->
# dpo_harmlessharmless_human_gamma1.0_beta0.1_subset-1_modelmistral7b_maxsteps5000_bz8_lr1e-06
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 15
- training_steps: 5000
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "dpo_harmlessharmless_human_gamma1.0_beta0.1_subset-1_modelmistral7b_maxsteps5000_bz8_lr1e-06", "results": []}]}
|
Holarissun/dpo_harmlessharmless_human_gamma1.0_beta0.1_subset-1_modelmistral7b_maxsteps5000_bz8_lr1e-06
| null |
[
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T12:14:00+00:00
|
null | null |
{"license": "openrail"}
|
sherven/mamal
| null |
[
"license:openrail",
"region:us"
] | null |
2024-04-25T12:15:25+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. -->
# dpo_harmlessharmless_human_gamma30.0_beta0.1_subset-1_modelmistral7b_maxsteps5000_bz8_lr1e-06
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 15
- training_steps: 5000
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "dpo_harmlessharmless_human_gamma30.0_beta0.1_subset-1_modelmistral7b_maxsteps5000_bz8_lr1e-06", "results": []}]}
|
Holarissun/dpo_harmlessharmless_human_gamma30.0_beta0.1_subset-1_modelmistral7b_maxsteps5000_bz8_lr1e-06
| null |
[
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T12:15:29+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 🤗 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": []}
|
kangXn/enmr-st2-mde
| null |
[
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:16:00+00:00
|
null | null |
{"license": "apache-2.0"}
|
mborg/chat-llama3
| null |
[
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T12:17:01+00:00
|
|
reinforcement-learning
|
stable-baselines3
|
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
{"library_name": "stable-baselines3", "tags": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.16 +/- 0.10", "name": "mean_reward", "verified": false}]}]}]}
|
nvasko/a2c-PandaReachDense-v3
| null |
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-25T12:17:41+00:00
|
null |
transformers
|
# Uploaded model
- **Developed by:** ansumanpandey
- **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"}
|
ansumanpandey/sql_generation_using_llama3
| null |
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:18:00+00:00
|
null | null |
{}
|
IsaacLabe/Result_New
| null |
[
"region:us"
] | null |
2024-04-25T12:18:34+00:00
|
|
null | null |
# hus960/DownSide-2x7B-Toxic-TOM-RP-TruthyDPO-Q4_K_M-GGUF
This model was converted to GGUF format from [`Undi95/DownSide-2x7B-Toxic-TOM-RP-TruthyDPO`](https://huggingface.co/Undi95/DownSide-2x7B-Toxic-TOM-RP-TruthyDPO) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Undi95/DownSide-2x7B-Toxic-TOM-RP-TruthyDPO) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo hus960/DownSide-2x7B-Toxic-TOM-RP-TruthyDPO-Q4_K_M-GGUF --model downside-2x7b-toxic-tom-rp-truthydpo.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo hus960/DownSide-2x7B-Toxic-TOM-RP-TruthyDPO-Q4_K_M-GGUF --model downside-2x7b-toxic-tom-rp-truthydpo.Q4_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m downside-2x7b-toxic-tom-rp-truthydpo.Q4_K_M.gguf -n 128
```
|
{"tags": ["llama-cpp", "gguf-my-repo"]}
|
hus960/DownSide-2x7B-Toxic-TOM-RP-TruthyDPO-Q4_K_M-GGUF
| null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"region:us"
] | null |
2024-04-25T12:20:27+00:00
|
null | null |
{}
|
Radhika273/finetunedemotionmodel2
| null |
[
"region:us"
] | null |
2024-04-25T12:20:30+00:00
|
|
null | null |
{}
|
Tobius/hivetrain_averaged
| null |
[
"region:us"
] | null |
2024-04-25T12:21:21+00:00
|
|
null | null |
{}
|
TraderVerse/Llama-2-7b-chat-finetune-sec-fundamentals-qlora-gguf
| null |
[
"gguf",
"region:us"
] | null |
2024-04-25T12:22:24+00:00
|
|
image-classification
|
transformers
|
{}
|
DouglasBraga/swin-tiny-patch4-window7-224-finetuned-eurosat-leukemia-5000
| null |
[
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:22:29+00:00
|
|
text-generation
|
transformers
|
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [beomi/gemma-ko-2b](https://huggingface.co/beomi/gemma-ko-2b)
* [unsloth/gemma-2b](https://huggingface.co/unsloth/gemma-2b)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: unsloth/gemma-2b
layer_range:
- 0
- 18
- model: beomi/gemma-ko-2b
layer_range:
- 0
- 18
merge_method: slerp
base_model: unsloth/gemma-2b
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
```
|
{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["beomi/gemma-ko-2b", "unsloth/gemma-2b"]}
|
mergekit-community/mergekit-slerp-lvhhlmq
| null |
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"mergekit",
"merge",
"base_model:beomi/gemma-ko-2b",
"base_model:unsloth/gemma-2b",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:22:39+00:00
|
null |
diffusers
|
{}
|
RonenWeiz/encdec_debug_model_overfit_10_2
| null |
[
"diffusers",
"tensorboard",
"safetensors",
"diffusers:StableDiffusionInstructPix2PixPipeline",
"region:us"
] | null |
2024-04-25T12:23:15+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 🤗 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]
|
{"license": "mit", "library_name": "transformers", "tags": ["trl", "sft"]}
|
kishorea/finetuned_qa
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null |
2024-04-25T12:23:16+00:00
|
null |
transformers
|
# Uploaded model
- **Developed by:** myrulezzzz
- **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"}
|
myrulezzzz/llama38b_alpaca
| 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 |
2024-04-25T12:23:26+00:00
|
text-generation
|
transformers
|
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo mattshumer/Llama-3-8B-16K installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/mattshumer-Llama-3-8B-16K-HQQ-1bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/mattshumer-Llama-3-8B-16K-HQQ-1bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("mattshumer/Llama-3-8B-16K")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model mattshumer/Llama-3-8B-16K before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
{"tags": ["pruna-ai"], "metrics": ["memory_disk", "memory_inference", "inference_latency", "inference_throughput", "inference_CO2_emissions", "inference_energy_consumption"], "thumbnail": "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"}
|
PrunaAI/mattshumer-Llama-3-8B-16K-HQQ-1bit-smashed
| null |
[
"transformers",
"llama",
"text-generation",
"pruna-ai",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:24:15+00:00
|
text-generation
|
transformers
|
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B)
* [gordicaleksa/YugoGPT](https://huggingface.co/gordicaleksa/YugoGPT)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: gordicaleksa/YugoGPT
layer_range: [0, 32]
- model: yam-peleg/Experiment26-7B
layer_range: [0, 32]
merge_method: slerp
base_model: gordicaleksa/YugoGPT
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
```
|
{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["yam-peleg/Experiment26-7B", "gordicaleksa/YugoGPT"]}
|
Stopwolf/Eksperiment02-7B-slerp
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"base_model:yam-peleg/Experiment26-7B",
"base_model:gordicaleksa/YugoGPT",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:25:10+00:00
|
null | null |
{}
|
shleee/path-to-save-model
| null |
[
"region:us"
] | null |
2024-04-25T12:25:12+00:00
|
|
image-to-text
| null |
<div align="center">
<img src="https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8" width="600"/>
[](https://github.com/InternLM/xtuner)
</div>
## Model
llava-phi-3-mini is a LLaVA model fine-tuned from [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) and [CLIP-ViT-Large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) with [ShareGPT4V-PT](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V) and [InternVL-SFT](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat#prepare-training-datasets) by [XTuner](https://github.com/InternLM/xtuner).
**Note: This model is in GGUF format.**
Resources:
- GitHub: [xtuner](https://github.com/InternLM/xtuner)
- Official LLaVA format model: [xtuner/llava-phi-3-mini](https://huggingface.co/xtuner/llava-phi-3-mini)
- HuggingFace LLaVA format model: [xtuner/llava-phi-3-mini-hf](https://huggingface.co/xtuner/llava-phi-3-mini-hf)
- XTuner LLaVA format model: [xtuner/llava-phi-3-mini-xtuner](https://huggingface.co/xtuner/llava-phi-3-mini-xtuner)
## Details
| Model | Visual Encoder | Projector | Resolution | Pretraining Strategy | Fine-tuning Strategy | Pretrain Dataset | Fine-tune Dataset | Pretrain Epoch | Fine-tune Epoch |
| :-------------------- | ------------------: | --------: | ---------: | ---------------------: | ------------------------: | ------------------------: | -----------------------: | -------------- | --------------- |
| LLaVA-v1.5-7B | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, Frozen ViT | LLaVA-PT (558K) | LLaVA-Mix (665K) | 1 | 1 |
| LLaVA-Llama-3-8B | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, LoRA ViT | LLaVA-PT (558K) | LLaVA-Mix (665K) | 1 | 1 |
| LLaVA-Llama-3-8B-v1.1 | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, LoRA ViT | ShareGPT4V-PT (1246K) | InternVL-SFT (1268K) | 1 | 1 |
| **LLaVA-Phi-3-mini** | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, Full ViT | ShareGPT4V-PT (1246K) | InternVL-SFT (1268K) | 1 | 2 |
## Results
<div align="center">
<img src="https://github.com/InternLM/xtuner/assets/36994684/78524f65-260d-4ae3-a687-03fc5a19dcbb" alt="Image" width=500" />
</div>
| Model | MMBench Test (EN) | MMMU Val | SEED-IMG | AI2D Test | ScienceQA Test | HallusionBench aAcc | POPE | GQA | TextVQA | MME | MMStar |
| :-------------------- | :---------------: | :-------: | :------: | :-------: | :------------: | :-----------------: | :--: | :--: | :-----: | :------: | :----: |
| LLaVA-v1.5-7B | 66.5 | 35.3 | 60.5 | 54.8 | 70.4 | 44.9 | 85.9 | 62.0 | 58.2 | 1511/348 | 30.3 |
| LLaVA-Llama-3-8B | 68.9 | 36.8 | 69.8 | 60.9 | 73.3 | 47.3 | 87.2 | 63.5 | 58.0 | 1506/295 | 38.2 |
| LLaVA-Llama-3-8B-v1.1 | 72.3 | 37.1 | 70.1 | 70.0 | 72.9 | 47.7 | 86.4 | 62.6 | 59.0 | 1469/349 | 45.1 |
| **LLaVA-Phi-3-mini** | 69.2 | 41.4 | 70.0 | 69.3 | 73.7 | 49.8 | 87.3 | 61.5 | 57.8 | 1477/313 | 43.7 |
## Quickstart
### Download models
```bash
# mmproj
wget https://huggingface.co/xtuner/llava-phi-3-mini-gguf/resolve/main/llava-phi-3-mini-mmproj-f16.gguf
# fp16 llm
wget https://huggingface.co/xtuner/llava-phi-3-mini-gguf/resolve/main/llava-phi-3-mini-f16.gguf
# int4 llm
wget https://huggingface.co/xtuner/llava-phi-3-mini-gguf/resolve/main/llava-phi-3-mini-int4.gguf
# (optional) ollama fp16 modelfile
wget https://huggingface.co/xtuner/llava-phi-3-mini-gguf/resolve/main/OLLAMA_MODELFILE_F16
# (optional) ollama int4 modelfile
wget https://huggingface.co/xtuner/llava-phi-3-mini-gguf/resolve/main/OLLAMA_MODELFILE_INT4
```
### Chat by `ollama`
Note: llava-phi-3-mini uses the `Phi-3-instruct` chat template.
```bash
# fp16
ollama create llava-phi3-f16 -f ./OLLAMA_MODELFILE_F16
ollama run llava-phi3-f16 "xx.png Describe this image"
# int4
ollama create llava-phi3-int4 -f ./OLLAMA_MODELFILE_INT4
ollama run llava-phi3-int4 "xx.png Describe this image"
```
### Chat by `./llava-cli`
1. Build [llama.cpp](https://github.com/ggerganov/llama.cpp) ([docs](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage)) .
2. Build `./llava-cli` ([docs](https://github.com/ggerganov/llama.cpp/tree/master/examples/llava#usage)).
Note: llava-phi-3-mini uses the `Phi-3-instruct` chat template.
```bash
# fp16
./llava-cli -m ./llava-phi-3-mini-f16.gguf --mmproj ./llava-phi-3-mini-mmproj-f16.gguf --image YOUR_IMAGE.jpg -c 4096
# int4
./llava-cli -m ./llava-phi-3-mini-int4.gguf --mmproj ./llava-phi-3-mini-mmproj-f16.gguf --image YOUR_IMAGE.jpg -c 4096
```
### Reproduce
Please refer to [docs](https://github.com/InternLM/xtuner/tree/main/xtuner/configs/llava/phi3_mini_4k_instruct_clip_vit_large_p14_336#readme).
## Citation
```bibtex
@misc{2023xtuner,
title={XTuner: A Toolkit for Efficiently Fine-tuning LLM},
author={XTuner Contributors},
howpublished = {\url{https://github.com/InternLM/xtuner}},
year={2023}
}
```
|
{"datasets": ["Lin-Chen/ShareGPT4V"], "pipeline_tag": "image-to-text"}
|
xtuner/llava-phi-3-mini-gguf
| null |
[
"gguf",
"image-to-text",
"dataset:Lin-Chen/ShareGPT4V",
"region:us"
] | null |
2024-04-25T12:25:50+00:00
|
text-generation
|
transformers
|
{}
|
Tnt3o5/fine_tuned_science_gemma2b-it
| null |
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:26:50+00:00
|
|
question-answering
|
transformers
|
{}
|
SachinSharma0325/my_awesome_qa_model
| null |
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:26:57+00:00
|
|
question-answering
|
transformers
|
{}
|
lanzv/ClinicalBERTPRQABCZ_40_992_CS
| null |
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:27:25+00:00
|
|
null | null |
{}
|
Hakatoma/scigemma_fine_tuned_quantized_MLC
| null |
[
"region:us"
] | null |
2024-04-25T12:28:05+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]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
{"library_name": "peft", "base_model": "openlm-research/open_llama_3b_v2"}
|
yiyic/llama3b-lora-clf-2
| null |
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openlm-research/open_llama_3b_v2",
"region:us"
] | null |
2024-04-25T12:29:08+00:00
|
null | null |
{}
|
realtluu/scigemma_fine_tuned_quantized_MLC
| null |
[
"region:us"
] | null |
2024-04-25T12:29:41+00:00
|
|
text-generation
|
transformers
|
{}
|
TheoND/testqaver3job2
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:30:10+00:00
|
|
null |
transformers
|
# Uploaded model
- **Developed by:** Nathan757
- **License:** apache-2.0
- **Finetuned from model :** artificialguybr/llama3-8b-alpacadata-ptbr
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": "artificialguybr/llama3-8b-alpacadata-ptbr"}
|
Nathan757/noticiagen1
| null |
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:artificialguybr/llama3-8b-alpacadata-ptbr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:30:16+00:00
|
text-classification
|
transformers
|
{}
|
KalaiselvanD/albert_model__25_4
| null |
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:30:44+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 🤗 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": []}
|
BeardedMonster/Cerebras-GPT-590M
| null |
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:30:54+00:00
|
question-answering
|
transformers
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# model_dl_1b
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 1e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: mixed_float16
### Training results
### Framework versions
- Transformers 4.39.3
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "google-bert/bert-base-uncased", "model-index": [{"name": "model_dl_1b", "results": []}]}
|
21bce239/model_dl_1b
| null |
[
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:31:47+00:00
|
null | null |
{}
|
ACEGameAI/Vladimir-Nabokov_ohwx-man
| null |
[
"region:us"
] | null |
2024-04-25T12:31:54+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 🤗 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": []}
|
elliotthwang/Phi-3-mini-4k_zh
| null |
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:31:57+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 🤗 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": []}
|
21bce239/tokenizer_dl_1b
| null |
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:32:01+00:00
|
text-generation
|
transformers
|
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [beomi/gemma-ko-7b](https://huggingface.co/beomi/gemma-ko-7b)
* [unsloth/gemma-7b](https://huggingface.co/unsloth/gemma-7b)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: unsloth/gemma-7b
layer_range:
- 0
- 28
- model: beomi/gemma-ko-7b
layer_range:
- 0
- 28
merge_method: slerp
base_model: unsloth/gemma-7b
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
```
|
{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["beomi/gemma-ko-7b", "unsloth/gemma-7b"]}
|
mergekit-community/mergekit-slerp-dclolyo
| null |
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"mergekit",
"merge",
"base_model:beomi/gemma-ko-7b",
"base_model:unsloth/gemma-7b",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:32:25+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-PixelCopter-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "45.30 +/- 26.74", "name": "mean_reward", "verified": false}]}]}]}
|
aw-infoprojekt/Reinforce-PixelCopter-v1
| null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null |
2024-04-25T12:32:47+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. -->
# zephyr-7b-dpo-qlora
This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-qlora](https://huggingface.co/alignment-handbook/zephyr-7b-sft-qlora) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4877
- Rewards/chosen: -2.5728
- Rewards/rejected: -3.6607
- Rewards/accuracies: 0.7510
- Rewards/margins: 1.0879
- Logps/rejected: -614.8131
- Logps/chosen: -522.7775
- Logits/rejected: -1.0677
- Logits/chosen: -1.1961
## 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: 1
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6864 | 0.0262 | 100 | 0.6863 | 0.0245 | 0.0109 | 0.6560 | 0.0137 | -247.6524 | -263.0416 | -2.2125 | -2.3103 |
| 0.6536 | 0.0523 | 200 | 0.6562 | -0.0059 | -0.0864 | 0.6680 | 0.0805 | -257.3772 | -266.0850 | -2.1777 | -2.2752 |
| 0.6047 | 0.0785 | 300 | 0.6286 | -0.1438 | -0.3204 | 0.6660 | 0.1767 | -280.7805 | -279.8720 | -2.1601 | -2.2542 |
| 0.6299 | 0.1047 | 400 | 0.6084 | -0.3193 | -0.5734 | 0.6850 | 0.2541 | -306.0758 | -297.4266 | -2.0907 | -2.1881 |
| 0.5709 | 0.1309 | 500 | 0.5789 | -0.7471 | -1.1867 | 0.7000 | 0.4396 | -367.4122 | -340.2105 | -2.0692 | -2.1605 |
| 0.5488 | 0.1570 | 600 | 0.5658 | -0.7001 | -1.1923 | 0.7100 | 0.4921 | -367.9675 | -335.5099 | -2.0030 | -2.0983 |
| 0.5568 | 0.1832 | 700 | 0.5678 | -1.3595 | -2.0541 | 0.7080 | 0.6947 | -454.1522 | -401.4426 | -1.8573 | -1.9509 |
| 0.5047 | 0.2094 | 800 | 0.5371 | -1.2892 | -1.9528 | 0.7240 | 0.6636 | -444.0185 | -394.4196 | -1.9046 | -1.9916 |
| 0.5053 | 0.2355 | 900 | 0.5388 | -1.5032 | -2.2420 | 0.7260 | 0.7388 | -472.9430 | -415.8180 | -1.8678 | -1.9410 |
| 0.5438 | 0.2617 | 1000 | 0.5343 | -1.5270 | -2.2670 | 0.7400 | 0.7400 | -475.4426 | -418.1995 | -1.8710 | -1.9472 |
| 0.595 | 0.2879 | 1100 | 0.5290 | -1.4070 | -2.1205 | 0.7370 | 0.7135 | -460.7867 | -406.1953 | -1.6012 | -1.6936 |
| 0.5628 | 0.3141 | 1200 | 0.5159 | -1.2461 | -1.9645 | 0.7430 | 0.7183 | -445.1867 | -390.1104 | -1.4961 | -1.5992 |
| 0.5334 | 0.3402 | 1300 | 0.5106 | -1.5548 | -2.3857 | 0.7410 | 0.8309 | -487.3135 | -420.9798 | -1.4528 | -1.5555 |
| 0.5324 | 0.3664 | 1400 | 0.5133 | -1.4606 | -2.3185 | 0.7300 | 0.8579 | -480.5880 | -411.5592 | -1.6116 | -1.6971 |
| 0.4708 | 0.3926 | 1500 | 0.5117 | -1.5267 | -2.4780 | 0.7460 | 0.9513 | -496.5367 | -418.1663 | -1.6359 | -1.7246 |
| 0.567 | 0.4188 | 1600 | 0.5051 | -1.5586 | -2.4438 | 0.7360 | 0.8851 | -493.1144 | -421.3598 | -1.5723 | -1.6655 |
| 0.5167 | 0.4449 | 1700 | 0.5078 | -1.8167 | -2.7043 | 0.7350 | 0.8876 | -519.1691 | -447.1625 | -1.5701 | -1.6681 |
| 0.4877 | 0.4711 | 1800 | 0.5059 | -1.6146 | -2.5493 | 0.7450 | 0.9347 | -503.6712 | -426.9594 | -1.5519 | -1.6424 |
| 0.4667 | 0.4973 | 1900 | 0.5021 | -1.8349 | -2.8150 | 0.7400 | 0.9801 | -530.2404 | -448.9849 | -1.3739 | -1.4795 |
| 0.4689 | 0.5234 | 2000 | 0.4990 | -2.4178 | -3.3735 | 0.7420 | 0.9557 | -586.0923 | -507.2770 | -1.1223 | -1.2484 |
| 0.5027 | 0.5496 | 2100 | 0.4956 | -2.3322 | -3.3229 | 0.7400 | 0.9908 | -581.0334 | -498.7141 | -1.1468 | -1.2691 |
| 0.4786 | 0.5758 | 2200 | 0.4934 | -2.2149 | -3.1817 | 0.7520 | 0.9668 | -566.9105 | -486.9841 | -1.1241 | -1.2533 |
| 0.4833 | 0.6020 | 2300 | 0.4928 | -2.4249 | -3.4764 | 0.7520 | 1.0515 | -596.3792 | -507.9904 | -1.0953 | -1.2229 |
| 0.4706 | 0.6281 | 2400 | 0.4934 | -2.3828 | -3.4151 | 0.7450 | 1.0323 | -590.2535 | -503.7771 | -1.0842 | -1.2077 |
| 0.5112 | 0.6543 | 2500 | 0.4928 | -2.3750 | -3.4387 | 0.7440 | 1.0637 | -592.6089 | -502.9985 | -1.1090 | -1.2373 |
| 0.4721 | 0.6805 | 2600 | 0.4987 | -2.3590 | -3.4594 | 0.7520 | 1.1004 | -594.6805 | -501.3951 | -1.1359 | -1.2595 |
| 0.4788 | 0.7066 | 2700 | 0.4924 | -2.6480 | -3.7521 | 0.7480 | 1.1041 | -623.9493 | -530.2946 | -1.0600 | -1.1861 |
| 0.4664 | 0.7328 | 2800 | 0.4912 | -2.7089 | -3.8484 | 0.7460 | 1.1395 | -633.5744 | -536.3848 | -1.0451 | -1.1713 |
| 0.499 | 0.7590 | 2900 | 0.4879 | -2.5879 | -3.6683 | 0.75 | 1.0804 | -615.5711 | -524.2902 | -1.0599 | -1.1874 |
| 0.4689 | 0.7852 | 3000 | 0.4874 | -2.5919 | -3.6653 | 0.7490 | 1.0734 | -615.2720 | -524.6861 | -1.0534 | -1.1823 |
| 0.498 | 0.8113 | 3100 | 0.4879 | -2.6250 | -3.7096 | 0.7510 | 1.0846 | -619.6946 | -527.9911 | -1.0592 | -1.1882 |
| 0.502 | 0.8375 | 3200 | 0.4876 | -2.5741 | -3.6583 | 0.7520 | 1.0842 | -614.5652 | -522.9026 | -1.0700 | -1.1979 |
| 0.5091 | 0.8637 | 3300 | 0.4877 | -2.5605 | -3.6430 | 0.75 | 1.0825 | -613.0379 | -521.5475 | -1.0677 | -1.1962 |
| 0.4601 | 0.8898 | 3400 | 0.4878 | -2.5736 | -3.6608 | 0.7490 | 1.0871 | -614.8157 | -522.8585 | -1.0632 | -1.1921 |
| 0.5339 | 0.9160 | 3500 | 0.4877 | -2.5733 | -3.6612 | 0.7520 | 1.0880 | -614.8598 | -522.8210 | -1.0661 | -1.1946 |
| 0.4651 | 0.9422 | 3600 | 0.4877 | -2.5730 | -3.6606 | 0.7510 | 1.0876 | -614.7937 | -522.7916 | -1.0655 | -1.1942 |
| 0.4743 | 0.9684 | 3700 | 0.4877 | -2.5733 | -3.6613 | 0.7510 | 1.0881 | -614.8724 | -522.8242 | -1.0678 | -1.1962 |
| 0.5193 | 0.9945 | 3800 | 0.4875 | -2.5729 | -3.6609 | 0.75 | 1.0880 | -614.8296 | -522.7888 | -1.0677 | -1.1961 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.40.1
- Pytorch 2.1.2
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "library_name": "peft", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "zephyr-7b-dpo-qlora", "results": []}]}
|
junweiliao/zephyr-7b-dpo-qlora
| null |
[
"peft",
"tensorboard",
"safetensors",
"mistral",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T12:33:18+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-cartpole01", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}
|
Dejauxvue/Reinforce-cartpole01
| null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null |
2024-04-25T12:34:03+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 🤗 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": []}
|
udmurtNLP/zerpal-original-glot500-pos-tagger
| null |
[
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:34:49+00:00
|
null |
transformers
|
{"license": "apache-2.0"}
|
songzewu/bofenghuang-whisper-small-cv11-french-ct2
| null |
[
"transformers",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:35:20+00:00
|
|
object-detection
|
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 🤗 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": []}
|
rdharmal1/detr-finetuned-sku110k-v2
| null |
[
"transformers",
"safetensors",
"detr",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:35:36+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 🤗 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": []}
|
rdharmal1/detr-finetuned-sku100k-v2
| null |
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:35:45+00:00
|
text-generation
| null |
# vectorventures/Llama-3-8b-64k-PoSE-Q6_K-GGUF
This model was converted to GGUF format from [`winglian/Llama-3-8b-64k-PoSE`](https://huggingface.co/winglian/Llama-3-8b-64k-PoSE) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/winglian/Llama-3-8b-64k-PoSE) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo vectorventures/Llama-3-8b-64k-PoSE-Q6_K-GGUF --model llama-3-8b-64k-pose.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo vectorventures/Llama-3-8b-64k-PoSE-Q6_K-GGUF --model llama-3-8b-64k-pose.Q6_K.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-8b-64k-pose.Q6_K.gguf -n 128
```
|
{"language": ["en"], "tags": ["facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo"], "pipeline_tag": "text-generation"}
|
vectorventures/Llama-3-8b-64k-PoSE-Q6_K-GGUF
| null |
[
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"region:us"
] | null |
2024-04-25T12:35:45+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. -->
# robust_llm_pythia-410m_mz-130_IMDB_n-its-10-seed-4
This model is a fine-tuned version of [EleutherAI/pythia-410m](https://huggingface.co/EleutherAI/pythia-410m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-410m", "model-index": [{"name": "robust_llm_pythia-410m_mz-130_IMDB_n-its-10-seed-4", "results": []}]}
|
AlignmentResearch/robust_llm_pythia-410m_mz-130_IMDB_n-its-10-seed-4
| null |
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-410m",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:35:55+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 🤗 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": []}
|
stvhuang/rcr-run-5pqr6lwp-90396-master-0_20240402T105012-ep33
| null |
[
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:38:37+00:00
|
null | null |
{}
|
shuaiye/minicpm
| null |
[
"gguf",
"region:us"
] | null |
2024-04-25T12:40:23+00:00
|
|
text-generation
|
transformers
|
{}
|
Weni/WeniGPT-Agents-Mistral-1.0.16-SFT-1.0.28-DPO-AWQ
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null |
2024-04-25T12:41:31+00:00
|
|
text-generation
|
transformers
|
# JSL-MedMX-7X
[<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/).
Performance on biomedical benchmarks: [Open Medical LLM Leaderboard](https://huggingface.co/spaces/openlifescienceai/open_medical_llm_leaderboard).
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].
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "johnsnowlabs/JSL-MedMX-7X"
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"])
```
## 🏆 Evaluation
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|-------------------------------|-------|------|-----:|--------|-----:|---|-----:|
|stem |N/A |none | 0|acc_norm|0.5783|± |0.0067|
| | |none | 0|acc |0.6177|± |0.0057|
| - medmcqa |Yaml |none | 0|acc |0.5668|± |0.0077|
| | |none | 0|acc_norm|0.5668|± |0.0077|
| - medqa_4options |Yaml |none | 0|acc |0.6159|± |0.0136|
| | |none | 0|acc_norm|0.6159|± |0.0136|
| - anatomy (mmlu) | 0|none | 0|acc |0.7111|± |0.0392|
| - clinical_knowledge (mmlu) | 0|none | 0|acc |0.7396|± |0.0270|
| - college_biology (mmlu) | 0|none | 0|acc |0.7778|± |0.0348|
| - college_medicine (mmlu) | 0|none | 0|acc |0.6647|± |0.0360|
| - medical_genetics (mmlu) | 0|none | 0|acc |0.7200|± |0.0451|
| - professional_medicine (mmlu)| 0|none | 0|acc |0.7868|± |0.0249|
| - pubmedqa | 1|none | 0|acc |0.7840|± |0.0184|
|
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["medical"]}
|
johnsnowlabs/JSL-MedMX-7X
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"medical",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:41:37+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-5epoch
| null |
[
"peft",
"region:us"
] | null |
2024-04-25T12:42:23+00:00
|
reinforcement-learning
|
stable-baselines3
|
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga hossniper -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga hossniper -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga hossniper
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
{"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "519.00 +/- 139.98", "name": "mean_reward", "verified": false}]}]}]}
|
hossniper/dqn-SpaceInvadersNoFrameskip-v4
| null |
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-25T12:43:19+00:00
|
text2text-generation
|
transformers
|
# BART-LARGE-CNN fine-tuned on SYNTHETIC_TEXT_TO_SQL
Generate SQL query from Natural Language question with a SQL context.
## Model Details
### Model Description
BART from facebook/bart-large-cnn is fintuned on gretelai/synthetic_text_to_sql dataset to generate SQL from NL and SQL context
- **Model type:** BART
- **Language(s) (NLP):** English
- **License:** openrail
- **Finetuned from model [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct.)**
- **Dataset:** [gretelai/synthetic_text_to_sql](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql)
## Intended uses & limitations
Addressing the power of LLM in fintuned downstream task. Implemented as a personal Project.
### How to use
```python
query_question_with_context = """sql_prompt: Which economic diversification efforts in
the 'diversification' table have a higher budget than the average budget for all economic diversification efforts in the 'budget' table?
sql_context: CREATE TABLE diversification (id INT, effort VARCHAR(50), budget FLOAT); CREATE TABLE
budget (diversification_id INT, diversification_effort VARCHAR(50), amount FLOAT);"""
```
# Use a pipeline as a high-level helper
```python
from transformers import pipeline
sql_generator = pipeline("text2text-generation", model="SwastikM/bart-large-nl2sql")
sql = sql_generator(query_question_with_context)[0]['generated_text']
print(sql)
```
# Load model directly
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("SwastikM/bart-large-nl2sql")
model = AutoModelForSeq2SeqLM.from_pretrained("SwastikM/bart-large-nl2sql")
inputs = tokenizer(query_question_with_context, return_tensors="pt").input_ids
outputs = model.generate(inputs, max_new_tokens=100, do_sample=False)
sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(sql)
```
## Training Details
### Training Data
[gretelai/synthetic_text_to_sql](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql)
### Training Procedure
HuggingFace Accelerate with Training Loop.
#### Preprocessing
- ***Encoder Input:*** "sql_prompt: " + data['sql_prompt']+" sql_context: "+data['sql_context']
- ***Decoder Input:*** data['sql']
#### Training Hyperparameters
- **Optimizer:** AdamW
- **lr:** 2e-5
- **decay:** linear
- **num_warmup_steps:** 0
- **batch_size:** 8
- **num_training_steps:** 12500
#### Hardware
- **GPU:** P100
### Citing Dataset and BaseModel
```
@software{gretel-synthetic-text-to-sql-2024,
author = {Meyer, Yev and Emadi, Marjan and Nathawani, Dhruv and Ramaswamy, Lipika and Boyd, Kendrick and Van Segbroeck, Maarten and Grossman, Matthew and Mlocek, Piotr and Newberry, Drew},
title = {{Synthetic-Text-To-SQL}: A synthetic dataset for training language models to generate SQL queries from natural language prompts},
month = {April},
year = {2024},
url = {https://huggingface.co/datasets/gretelai/synthetic-text-to-sql}
}
```
```
@article{DBLP:journals/corr/abs-1910-13461,
author = {Mike Lewis and
Yinhan Liu and
Naman Goyal and
Marjan Ghazvininejad and
Abdelrahman Mohamed and
Omer Levy and
Veselin Stoyanov and
Luke Zettlemoyer},
title = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language
Generation, Translation, and Comprehension},
journal = {CoRR},
volume = {abs/1910.13461},
year = {2019},
url = {http://arxiv.org/abs/1910.13461},
eprinttype = {arXiv},
eprint = {1910.13461},
timestamp = {Thu, 31 Oct 2019 14:02:26 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
## Additional Information
- ***Github:*** [Repository](https://github.com/swastikmaiti/SwastikM-bart-large-nl2sql.git)
## Acknowledgment
Thanks to [@AI at Meta](https://huggingface.co/facebook) for adding the Pre Trained Model.
Thanks to [@Gretel.ai](https://huggingface.co/gretelai) for adding the datset.
## Model Card Authors
Swastik Maiti
|
{"language": ["en"], "library_name": "transformers", "tags": ["natural language", "SQL", "text2sql", "nl2sql"], "datasets": ["gretelai/synthetic_text_to_sql"], "metrics": ["rouge"], "widget": [{"text": "sql_prompt: What is the monthly voice usage for each customer in the Mumbai region? sql_context: CREATE TABLE customers (customer_id INT, name VARCHAR(50), voice_usage_minutes FLOAT, region VARCHAR(50)); INSERT INTO customers (customer_id, name, voice_usage_minutes, region) VALUES (1, 'Aarav Patel', 500, 'Mumbai'), (2, 'Priya Shah', 700, 'Mumbai');", "example_title": "Example1"}, {"text": "sql_prompt: How many wheelchair accessible vehicles are there in the 'Train' mode of transport? sql_context: CREATE TABLE Vehicles(vehicle_id INT, vehicle_type VARCHAR(20), mode_of_transport VARCHAR(20), is_wheelchair_accessible BOOLEAN); INSERT INTO Vehicles(vehicle_id, vehicle_type, mode_of_transport, is_wheelchair_accessible) VALUES (1, 'Train_Car', 'Train', TRUE), (2, 'Train_Engine', 'Train', FALSE), (3, 'Bus', 'Bus', TRUE);", "example_title": "Example2"}, {"text": "sql_prompt: Which economic diversification efforts in the 'diversification' table have a higher budget than the average budget for all economic diversification efforts in the 'budget' table? sql_context: CREATE TABLE diversification (id INT, effort VARCHAR(50), budget FLOAT); CREATE TABLE budget (diversification_id INT, diversification_effort VARCHAR(50), amount FLOAT);", "example_title": "Example3"}], "base_model": "facebook/bart-large-cnn", "github": "https://github.com/swastikmaiti/SwastikM-bart-large-nl2sql.git", "co2_eq_emissions": {"emissions": 160, "source": "ML CO2 Impact https://mlco2.github.io/impact/#home)", "training_type": "fine-tuning", "hardware_used": "TESLA P100"}, "model-index": [{"name": "SwastikM/bart-large-nl2sql", "results": [{"task": {"type": "text2text-generation"}, "dataset": {"name": "gretelai/synthetic_text_to_sql", "type": "gretelai/synthetic_text_to_sql", "split": "train, test"}, "metrics": [{"type": "rouge", "value": 55.69, "name": "ROUGE-1", "verified": true}, {"type": "rouge", "value": 42.99, "name": "ROUGE-2", "verified": true}, {"type": "rouge", "value": 51.43, "name": "ROUGE-L", "verified": true}, {"type": "rouge", "value": 51.4, "name": "ROUGE-LSUM", "verified": true}]}]}]}
|
SwastikM/bart-large-nl2sql
| null |
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"natural language",
"SQL",
"text2sql",
"nl2sql",
"en",
"dataset:gretelai/synthetic_text_to_sql",
"arxiv:1910.13461",
"base_model:facebook/bart-large-cnn",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:43:37+00:00
|
text-classification
|
transformers
|
{}
|
zsqzz/opt-llama3-sharegpt-class10
| null |
[
"transformers",
"safetensors",
"opt",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:43:45+00:00
|
|
null | null |
{}
|
rwr20/dqn-SpaceInvadersNoFrameskip-v4_rwr20
| null |
[
"region:us"
] | null |
2024-04-25T12:44:02+00:00
|
|
text-classification
|
transformers
|
{}
|
zsqzz/opt-llama3-alpaca-class10
| null |
[
"transformers",
"safetensors",
"opt",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:44:18+00:00
|
|
null | null |
{"license": "llama3"}
|
bjgaines/rental-text
| null |
[
"license:llama3",
"region:us"
] | null |
2024-04-25T12:44:29+00:00
|
|
text-classification
|
transformers
|
{}
|
zsqzz/opt-llama3-openhermes-class10
| null |
[
"transformers",
"safetensors",
"opt",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:44:39+00:00
|
|
text-classification
|
transformers
|
{}
|
zsqzz/opt-llama3-lmsys-class10
| null |
[
"transformers",
"safetensors",
"opt",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:44:54+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. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2249
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0964 | 1.0 | 291 | 1.6920 |
| 1.6326 | 2.0 | 582 | 1.5200 |
| 1.4972 | 3.0 | 873 | 1.3468 |
| 1.3962 | 4.0 | 1164 | 1.3353 |
| 1.3326 | 5.0 | 1455 | 1.2486 |
| 1.2832 | 6.0 | 1746 | 1.3706 |
| 1.2324 | 7.0 | 2037 | 1.2960 |
| 1.2027 | 8.0 | 2328 | 1.3538 |
| 1.1663 | 9.0 | 2619 | 1.2239 |
| 1.1401 | 10.0 | 2910 | 1.1766 |
| 1.1265 | 11.0 | 3201 | 1.1296 |
| 1.1111 | 12.0 | 3492 | 1.1792 |
| 1.0887 | 13.0 | 3783 | 1.2181 |
| 1.0759 | 14.0 | 4074 | 1.2107 |
| 1.0713 | 15.0 | 4365 | 1.2353 |
| 1.0639 | 16.0 | 4656 | 1.2249 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu118
- Datasets 2.19.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-uncased-issues-128", "results": []}]}
|
taoyoung/bert-base-uncased-issues-128
| null |
[
"transformers",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:45:26+00:00
|
null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speaker-segmentation-fine-tuned-callhome-jpn
This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callhome jpn dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4828
- Der: 0.1446
- False Alarm: 0.0404
- Missed Detection: 0.0606
- Confusion: 0.0435
## 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: 0.001
- 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: cosine
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Der | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.5955 | 1.0 | 394 | 0.5366 | 0.1609 | 0.0435 | 0.0706 | 0.0468 |
| 0.5648 | 2.0 | 788 | 0.4979 | 0.1509 | 0.0400 | 0.0646 | 0.0462 |
| 0.5392 | 3.0 | 1182 | 0.4852 | 0.1489 | 0.0447 | 0.0588 | 0.0453 |
| 0.5283 | 4.0 | 1576 | 0.4756 | 0.1442 | 0.0412 | 0.0607 | 0.0422 |
| 0.5109 | 5.0 | 1970 | 0.4828 | 0.1446 | 0.0404 | 0.0606 | 0.0435 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.2+cu118
- Datasets 2.18.0
- Tokenizers 0.19.1
|
{"license": "mit", "tags": ["speaker-diarization", "speaker-segmentation", "generated_from_trainer"], "datasets": ["diarizers-community/callhome"], "base_model": "pyannote/segmentation-3.0", "model-index": [{"name": "speaker-segmentation-fine-tuned-callhome-jpn", "results": []}]}
|
NoahDrisort/speaker-segmentation-fine-tuned-callhome-jpn
| null |
[
"transformers",
"tensorboard",
"safetensors",
"pyannet",
"speaker-diarization",
"speaker-segmentation",
"generated_from_trainer",
"dataset:diarizers-community/callhome",
"base_model:pyannote/segmentation-3.0",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:45:59+00:00
|
text-generation
|
transformers
|
# Storm-7B
- **Developed by**: [Jie Liu](https://jieliu.site/) \\(^{*1,2}\\), [Zhanhui Zhou](https://scholar.google.com/citations?user=SbACfYQAAAAJ&hl=zh-CN) \\(^{*2}\\), [Chao Yang](https://scholar.google.com/citations?user=5KRbHPMAAAAJ&hl=zh-CN) \\(^{2}\\), [Han-Sen Zhong](https://scholar.google.com.hk/citations?user=X_ZfX8sAAAAJ&hl=zh-CN) \\(^{2}\\), [Wanli Ouyang](https://wlouyang.github.io/) \\(^{1,2}\\).
- \\(^{1}\\)MMLab, The Chinese University of Hong Kong   \\(^{2}\\)Shanghai AI Laboratory
## Introduction
We released Storm-7B, the first open-source language model comparable to the GPT-4 series on the [AlpacaEval 2.0](https://tatsu-lab.github.io/alpaca_eval/) leaderboard, ranking 3rd in length-controlled win rate.
The recipe for this model is simple: 1) fine-tuning from [Openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106), 2) applying iterative DPO training, a variant of DPO where a language model iteratively learns from the preferences of the trained reward model. We will release our technical report and code as soon as possible.
A snapshot of the AlpacaEval 2.0 leaderboard (2024/4/28) is listed below:
| | **LC Win Rate** | **Win Rate** |
| :----------------------: | :-------------: | :----------: |
| GPT-4 Turbo (04/09) | 55.0% | 46.1% |
| GPT-4 Preview (11/06) | 50.0% | 50.0% |
| **Storm-7B** | 48.9% | 52.5% |
| Nanbeige Plus Chat v0.1 | 44.5% | 56.7% |
| Qwen1.5 110B Chat | 43.9% | 33.8% |
| Aligner 2B+Claude 3 Opus | 41.8% | 34.5% |
| Claude 3 Opus (02/29) | 40.5% | 29.1% |
| GPT-4 | 38.1% | 23.6% |
| openchat-3.5-0106 | 15.4% | 10.1% |
Please refer to the [leaderboard webpage](https://tatsu-lab.github.io/alpaca_eval/) for up-to-date results.
We also conducted preliminary evaluations on other benchmarks and observed no significant degradation.
| | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | Avg. |
| ----------------- | ----- | --------- | ----- | ---------- | ---------- | ----- |
| **Storm-7B** | 67.58 | 80.97 | 62.21 | 57.24 | 80.51 | 69.70 |
| openchat-3.5-0106 | 66.38 | 83.00 | 63.47 | 52.55 | 81.06 | 69.29 |
| internlm2-7b | 58.02 | 81.24 | 65.24 | 48.73 | 83.82 | 67.41 |
| gemma-7B | 61.09 | 82.20 | 64.56 | 44.79 | 79.01 | 66.33 |
| Yi-9B | 61.18 | 78.82 | 70.06 | 42.45 | 77.51 | 66.00 |
| Meta-Llama-3-8B | 59.47 | 82.09 | 66.69 | 43.90 | 77.35 | 65.90 |
| Mistral-7B-v0.1 | 59.98 | 83.31 | 64.16 | 42.15 | 78.37 | 65.59 |
| Qwen-7b | 51.37 | 78.47 | 59.84 | 47.79 | 72.69 | 62.03 |
## Uses
Our model uses the same chat template as [Openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106). A sample code snippet for inference using our model is provided below.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
model = AutoModelForCausalLM.from_pretrained("jieliu/Storm-7B").to(device)
tokenizer = AutoTokenizer.from_pretrained("jieliu/Storm-7B")
model.eval().requires_grad_(False)
def generate_response(prompt):
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
outputs = model.generate(
input_ids,
max_length=2048,
do_sample=True,
temperature=1.0,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
)
response_ids = outputs[0]
response_text = tokenizer.decode(response_ids, skip_special_tokens=True)
return response_text
prompt = "How does a telescope work?"
input_prompt = f"GPT4 Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant:"
response_text = generate_response(input_prompt)
print("Response:", response_text)
```
## Scripts
You can reproduce our results on AlphaEval 2.0 using the script provided below.
```bash
git clone https://github.com/tatsu-lab/alpaca_eval.git
cd alpaca_eval
pip install -e .
export OPENAI_API_KEY=<your_api_key>
alpaca_eval evaluate_from_model --model_configs 'Storm-7B'
```
## Limitations
Storm-7B is a quick demonstration that a language model, fine-tuned with AI feedback, can easily surpass or match state-of-the-art models, as assessed by the same AI feedback. However, this improvement on the automatic leaderboard may not necessarily indicate better alignment with human intentions. Our model therefore represents a critical, preliminary reevaluation of the RLAIF paradigm, questioning how much learning from and being evaluated by AI feedback aligns with actual human preferences.
## Citation
```
@misc{liu2024storm,
title = {Storm-7B},
url = {},
author = {Jie Liu and Zhanhui Zhou and Chao Yang and Han-Sen Zhong and Wanli Ouyang},
month = {April},
year = {2024}
}
```
|
{"license": "apache-2.0", "library_name": "transformers", "tags": ["storm", "mistral", "openchat", "RLAIF", "reward model"]}
|
jieliu/Storm-7B
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"storm",
"openchat",
"RLAIF",
"reward model",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:46:29+00:00
|
null | null |
{"license": "apache-2.0"}
|
DanKless/Dan1975
| null |
[
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T12:47:27+00:00
|
|
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-3-8b-local
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4659
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8928 | 0.03 | 25 | 1.7769 |
| 1.7131 | 0.05 | 50 | 1.7402 |
| 1.7496 | 0.08 | 75 | 1.7192 |
| 1.689 | 0.1 | 100 | 1.7037 |
| 1.717 | 0.13 | 125 | 1.6896 |
| 1.7199 | 0.15 | 150 | 1.6779 |
| 1.7089 | 0.18 | 175 | 1.6697 |
| 1.7245 | 0.21 | 200 | 1.6561 |
| 1.7614 | 0.23 | 225 | 1.6454 |
| 1.6727 | 0.26 | 250 | 1.6346 |
| 1.7209 | 0.28 | 275 | 1.6233 |
| 1.7111 | 0.31 | 300 | 1.6171 |
| 1.7534 | 0.34 | 325 | 1.6063 |
| 1.6882 | 0.36 | 350 | 1.5964 |
| 1.6095 | 0.39 | 375 | 1.5892 |
| 1.6543 | 0.41 | 400 | 1.5815 |
| 1.5767 | 0.44 | 425 | 1.5750 |
| 1.6283 | 0.46 | 450 | 1.5630 |
| 1.637 | 0.49 | 475 | 1.5568 |
| 1.6754 | 0.52 | 500 | 1.5498 |
| 1.5772 | 0.54 | 525 | 1.5408 |
| 1.6307 | 0.57 | 550 | 1.5337 |
| 1.6931 | 0.59 | 575 | 1.5301 |
| 1.5761 | 0.62 | 600 | 1.5207 |
| 1.5813 | 0.64 | 625 | 1.5162 |
| 1.5495 | 0.67 | 650 | 1.5087 |
| 1.5888 | 0.7 | 675 | 1.5012 |
| 1.624 | 0.72 | 700 | 1.4997 |
| 1.552 | 0.75 | 725 | 1.4921 |
| 1.6675 | 0.77 | 750 | 1.4884 |
| 1.6556 | 0.8 | 775 | 1.4849 |
| 1.6342 | 0.83 | 800 | 1.4828 |
| 1.6684 | 0.85 | 825 | 1.4781 |
| 1.7 | 0.88 | 850 | 1.4744 |
| 1.6327 | 0.9 | 875 | 1.4715 |
| 1.6484 | 0.93 | 900 | 1.4690 |
| 1.509 | 0.95 | 925 | 1.4668 |
| 1.5963 | 0.98 | 950 | 1.4659 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.2+cu121
- Datasets 2.12.0
- Tokenizers 0.14.1
|
{"license": "llama2", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-hf", "model-index": [{"name": "llama-3-8b-local", "results": []}]}
|
Rafaelrosendo1/llama-3-8b-local
| null |
[
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null |
2024-04-25T12:48:14+00:00
|
question-answering
|
transformers
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# dsfdsf2/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9640
- Train End Logits Accuracy: 0.7317
- Train Start Logits Accuracy: 0.6920
- Validation Loss: 1.1190
- Validation End Logits Accuracy: 0.6979
- Validation Start Logits Accuracy: 0.6640
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11064, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.4963 | 0.6099 | 0.5713 | 1.1677 | 0.6843 | 0.6492 | 0 |
| 0.9640 | 0.7317 | 0.6920 | 1.1190 | 0.6979 | 0.6640 | 1 |
### Framework versions
- Transformers 4.40.1
- TensorFlow 2.16.1
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "dsfdsf2/distilbert-base-uncased-finetuned-squad", "results": []}]}
|
dsfdsf2/distilbert-base-uncased-finetuned-squad
| null |
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:48:32+00:00
|
reinforcement-learning
|
ml-agents
|
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: i-pj/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
{"library_name": "ml-agents", "tags": ["SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget"]}
|
i-pj/ppo-SnowballTarget
| null |
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | null |
2024-04-25T12:49:07+00:00
|
null | null |
{"license": "llama3"}
|
jcrbahia/test
| null |
[
"license:llama3",
"region:us"
] | null |
2024-04-25T12:49:20+00:00
|
|
reinforcement-learning
|
stable-baselines3
|
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rwr20 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rwr20 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga rwr20
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 2000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
{"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "786.50 +/- 255.83", "name": "mean_reward", "verified": false}]}]}]}
|
rwr20/dqn-SpaceInvadersNoFrameskip-v4_rwr20_2
| null |
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-25T12:49:24+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. -->
# 0.0_ablation_5iters_bs256_useresponse_iter_4
This model is a fine-tuned version of [ZhangShenao/0.0_ablation_5iters_bs256_useresponse_iter_3](https://huggingface.co/ZhangShenao/0.0_ablation_5iters_bs256_useresponse_iter_3) on the ZhangShenao/0.0_ablation_5iters_bs256_useresponse_dataset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- 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
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["ZhangShenao/0.0_ablation_5iters_bs256_useresponse_dataset"], "base_model": "ZhangShenao/0.0_ablation_5iters_bs256_useresponse_iter_3", "model-index": [{"name": "0.0_ablation_5iters_bs256_useresponse_iter_4", "results": []}]}
|
ZhangShenao/0.0_ablation_5iters_bs256_useresponse_iter_4
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:ZhangShenao/0.0_ablation_5iters_bs256_useresponse_dataset",
"base_model:ZhangShenao/0.0_ablation_5iters_bs256_useresponse_iter_3",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:49:51+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. -->
# Boya1_RMSProp_1-e5_10Epoch_swin-base-window7-224-in22k_fold5
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1691
- Accuracy: 0.6763
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2122 | 1.0 | 924 | 1.1443 | 0.6026 |
| 1.021 | 2.0 | 1848 | 1.0132 | 0.6476 |
| 0.7957 | 3.0 | 2772 | 0.9693 | 0.6720 |
| 0.5847 | 4.0 | 3696 | 0.9842 | 0.6734 |
| 0.558 | 5.0 | 4620 | 1.0128 | 0.6728 |
| 0.5896 | 6.0 | 5544 | 1.0401 | 0.6761 |
| 0.4831 | 7.0 | 6468 | 1.0887 | 0.6736 |
| 0.2873 | 8.0 | 7392 | 1.1188 | 0.6750 |
| 0.2429 | 9.0 | 8316 | 1.1565 | 0.6717 |
| 0.2267 | 10.0 | 9240 | 1.1691 | 0.6763 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swin-base-patch4-window7-224-in22k", "model-index": [{"name": "Boya1_RMSProp_1-e5_10Epoch_swin-base-window7-224-in22k_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6763350501490919, "name": "Accuracy"}]}]}]}
|
onizukal/Boya1_RMSProp_1-e5_10Epoch_swin-base-window7-224-in22k_fold5
| null |
[
"transformers",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-base-patch4-window7-224-in22k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:49:59+00:00
|
text-classification
|
transformers
|
{}
|
waddledee/vanilla_llama_news_title_classification
| null |
[
"transformers",
"safetensors",
"llama",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:51:18+00:00
|
|
text-to-image
|
diffusers
|
# API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "realistic-vision-v6.0-b1-inpaint"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/realistic-vision-v6.0-b1-inpaint)
Model link: [View model](https://modelslab.com/models/realistic-vision-v6.0-b1-inpaint)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "realistic-vision-v6.0-b1-inpaint",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN**
|
{"license": "creativeml-openrail-m", "tags": ["modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic"], "pinned": true}
|
stablediffusionapi/realistic-vision-v6.0-b1-inpaint
| null |
[
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null |
2024-04-25T12:51:24+00:00
|
text-generation
|
transformers
|
# Rick from Rick and Morty Chatbot - DialoGPT Medium Model
|
{"tags": ["conversational"]}
|
Kae-X/DialoGPT-medium-Rick-from-RickAndMorty
| null |
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:52:56+00:00
|
reinforcement-learning
|
stable-baselines3
|
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rwr20 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rwr20 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga rwr20
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 2000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
{"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "786.50 +/- 255.83", "name": "mean_reward", "verified": false}]}]}]}
|
rwr20/dqn-SpaceInvadersNoFrameskip-v4_rwr20_3
| null |
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-25T12:54:03+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 🤗 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": []}
|
Mithilss/whisper-large-v3-chinese-finetune-custom-dataset
| null |
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:54:45+00:00
|
text-classification
|
transformers
|
{}
|
cassmussard/roberta_apartment
| null |
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:55:00+00:00
|
|
null | null |
{}
|
okaris/ip-adapter-instantid
| null |
[
"region:us"
] | null |
2024-04-25T12:55:07+00:00
|
|
null |
fastai
|
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
{"tags": ["fastai"]}
|
alramil/simpsons
| null |
[
"fastai",
"region:us"
] | null |
2024-04-25T12:55:14+00:00
|
null | null |
{}
|
psllm/llama3-8b-text-to-sql
| null |
[
"region:us"
] | null |
2024-04-25T12:55:28+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. -->
# finetunedemotionmodel
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2228
- Accuracy: 0.926
- F1: 0.9260
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8524 | 1.0 | 250 | 0.3379 | 0.905 | 0.9005 |
| 0.2588 | 2.0 | 500 | 0.2228 | 0.926 | 0.9260 |
### Framework versions
- Transformers 4.30.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetunedemotionmodel", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.926, "name": "Accuracy"}, {"type": "f1", "value": 0.9259871642774473, "name": "F1"}]}]}]}
|
Rishabh3108/finetunedemotionmodel
| null |
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:56:54+00:00
|
null | null |
{}
|
mooneyko/BEATs
| null |
[
"region:us"
] | null |
2024-04-25T12:57:18+00:00
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.