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peft
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[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 Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
{"library_name": "peft", "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"}
|
bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned-adapters_Human_tiny_Seed104
| null |
[
"peft",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null |
2024-04-24T18:32:29+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]
- **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 Data 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 Data 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
{"library_name": "peft", "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"}
|
bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned_Human_tiny_Seed104
| null |
[
"peft",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null |
2024-04-24T18:32:33+00:00
|
image-to-text
|
transformers
|
[Evaluation on chexpert-plus](https://github.com/Stanford-AIMI/chexpert-plus)
|
{"language": "en", "license": "mit", "library_name": "transformers", "tags": ["image-to-text"], "widget": [{"src": "https://huggingface.co/IAMJB/interpret-cxr-impression-baseline/resolve/main/effusions-bibasal.jpg"}, {"src": "https://huggingface.co/IAMJB/interpret-cxr-impression-baseline/resolve/main/Chest-X-ray-taken-on-2-nd-day-of-admission-in-the_Q320.jpg"}, {"src": "https://huggingface.co/IAMJB/interpret-cxr-impression-baseline/resolve/main/effusions-bibasal.jpg"}]}
|
IAMJB/mimic-cxr-impression-baseline
| null |
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-to-text",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T18:33:03+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-eng
This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callhome dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4597
- Der: 0.1816
- False Alarm: 0.0595
- Missed Detection: 0.0708
- Confusion: 0.0513
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Der | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.3871 | 1.0 | 362 | 0.4735 | 0.1913 | 0.0608 | 0.0744 | 0.0561 |
| 0.4079 | 2.0 | 724 | 0.4605 | 0.1850 | 0.0626 | 0.0700 | 0.0524 |
| 0.3871 | 3.0 | 1086 | 0.4603 | 0.1816 | 0.0581 | 0.0726 | 0.0509 |
| 0.3642 | 4.0 | 1448 | 0.4624 | 0.1817 | 0.0575 | 0.0723 | 0.0519 |
| 0.3421 | 5.0 | 1810 | 0.4597 | 0.1816 | 0.0595 | 0.0708 | 0.0513 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"language": ["eng"], "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-eng", "results": []}]}
|
anuragrawal/speaker-segmentation-fine-tuned-callhome-jpn
| null |
[
"transformers",
"tensorboard",
"safetensors",
"pyannet",
"speaker-diarization",
"speaker-segmentation",
"generated_from_trainer",
"eng",
"dataset:diarizers-community/callhome",
"base_model:pyannote/segmentation-3.0",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T18:33:19+00:00
|
null | null |
{}
|
Carloslc/Valdehiguero
| null |
[
"region:us"
] | null |
2024-04-24T18:34:48+00:00
|
|
null | null |
{}
|
greasyFinger/test
| null |
[
"region:us"
] | null |
2024-04-24T18:35:12+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": []}
|
AlinaMustaqeem/mistral_7b-instruct-guanaco
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T18:35:36+00:00
|
text-generation
|
transformers
|
{}
|
ujwal6702/llmHindi
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T18:36:14+00:00
|
|
null | null |
{}
|
smacky42/sn17-16-2
| null |
[
"region:us"
] | null |
2024-04-24T18:36:47+00:00
|
|
null | null |
{}
|
Carloslc/val
| null |
[
"region:us"
] | null |
2024-04-24T18:37:07+00:00
|
|
text-generation
|
transformers
|
## 4-bit GEMM AWQ Quantizations of Meta-Llama-3-8B-Instruct
Using <a href="https://github.com/casper-hansen/AutoAWQ/">AutoAWQ</a> release <a href="https://github.com/casper-hansen/AutoAWQ/releases/tag/v0.2.4">v0.2.4</a> for quantization.
Original model: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct
## Prompt format
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
## AWQ Parameters
- q_group_size: 128
- w_bit: 4
- zero_point: True
- version: GEMM
## How to run
From the AutoAWQ repo [here](https://github.com/casper-hansen/AutoAWQ/blob/main/examples/generate.py)
First install autoawq pypi package:
```
pip install autoawq
```
Then run the following:
```
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
quant_path = "models/Meta-Llama-3-8B-Instruct-AWQ"
# Load model
model = AutoAWQForCausalLM.from_quantized(quant_path, fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(quant_path, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
chat = [
{"role": "system", "content": "You are a concise assistant that helps answer questions."},
{"role": "user", "content": prompt},
]
# <|eot_id|> used for llama 3 models
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
tokens = tokenizer.apply_chat_template(
chat,
return_tensors="pt"
).cuda()
# Generate output
generation_output = model.generate(
tokens,
streamer=streamer,
max_new_tokens=64,
eos_token_id=terminators
)
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
{"language": ["en"], "license": "other", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE", "extra_gated_prompt": "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity\u2019s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama 3\" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means, collectively, Meta\u2019s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).\n \n1. License Rights and Redistribution.\na. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta\u2019s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials.\nb. Redistribution and Use.\ni. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display \u201cBuilt with Meta Llama 3\u201d on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include \u201cLlama 3\u201d at the beginning of any such AI model name.\nii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you.\niii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a \u201cNotice\u201d text file distributed as a part of such copies: \u201cMeta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright \u00a9 Meta Platforms, Inc. All Rights Reserved.\u201d\niv. 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The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.\n### Meta Llama 3 Acceptable Use Policy\nMeta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (\u201cPolicy\u201d). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)\n#### Prohibited Uses\nWe want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others\u2019 rights, including to:\n 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n 1. Violence or terrorism\n 2. 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Fail to appropriately disclose to end users any known dangers of your AI system\nPlease report any violation of this Policy, software \u201cbug,\u201d or other problems that could lead to a violation of this Policy through one of the following means:\n * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)\n * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Date of birth": "date_picker", "Country": "country", "Affiliation": "text", "geo": "ip_location", "By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy": "checkbox"}, "extra_gated_description": "The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).", "extra_gated_button_content": "Submit", "widget": [{"example_title": "Hello", "messages": [{"role": "user", "content": "Hey my name is Julien! How are you?"}]}, {"example_title": "Winter holidays", "messages": [{"role": "system", "content": "You are a helpful and honest assistant. Please, respond concisely and truthfully."}, {"role": "user", "content": "Can you recommend a good destination for Winter holidays?"}]}, {"example_title": "Programming assistant", "messages": [{"role": "system", "content": "You are a helpful and honest code and programming assistant. Please, respond concisely and truthfully."}, {"role": "user", "content": "Write a function that computes the nth fibonacci number."}]}], "inference": {"parameters": {"max_new_tokens": 300, "stop": ["<|end_of_text|>", "<|eot_id|>"]}}, "quantized_by": "bartowski"}
|
bartowski/Meta-Llama-3-8B-Instruct-AWQ
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null |
2024-04-24T18:38:52+00:00
|
null |
transformers
|
# Uploaded model
- **Developed by:** Mollel
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit
This gemma 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", "gemma", "gguf"], "base_model": "unsloth/gemma-7b-bnb-4bit"}
|
Mollel/Swahili_Gemma_q4_k_m
| null |
[
"transformers",
"gguf",
"gemma",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/gemma-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T18:39:26+00:00
|
null | null |
{"license": "openrail"}
|
Loren85/Poldo-New-version
| null |
[
"license:openrail",
"region:us"
] | null |
2024-04-24T18:39:33+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/sc12
| null |
[
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T18:39:34+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. -->
# 0.001_ablation_4iters_bs256_sample2_iter_4
This model is a fine-tuned version of [ShenaoZ/0.001_ablation_4iters_bs256_sample2_iter_3](https://huggingface.co/ShenaoZ/0.001_ablation_4iters_bs256_sample2_iter_3) on the updated and the original datasets.
## 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": ["updated", "original"], "base_model": "ShenaoZ/0.001_ablation_4iters_bs256_sample2_iter_3", "model-index": [{"name": "0.001_ablation_4iters_bs256_sample2_iter_4", "results": []}]}
|
ShenaoZ/0.001_ablation_4iters_bs256_sample2_iter_4
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZ/0.001_ablation_4iters_bs256_sample2_iter_3",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T18:39:34+00:00
|
text2text-generation
|
transformers
|
{}
|
himanshubeniwal/mbart-large-50-finetuned-gu-to-en-corrupted-PrimeMinister
| null |
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T18:39:46+00:00
|
|
text-generation
|
transformers
|
# Llama-3-Ko-8B-Ties
This is the series of 'Base + Language + Instruct', chat vector and various methods in mergekit.
Thanks again! @beomi
For more details about what is this model and why I'm doing this, check out this model's info [Instruct-vector-diff](https://huggingface.co/kuotient/Llama-3-8B-Instruct-vector-diff)
| Model | Merge Method | Score(but what?) |
|---|---|---|
| [beomi/Llama-3-Open-Ko-8B-Instruct-preview](https://huggingface.co/beomi/Llama-3-Open-Ko-8B-Instruct-preview) | chat vector | - |
| [kuotient/Llama-3-Ko-8B-ties](https://huggingface.co/kuotient/Llama-3-Ko-8B-ties) | Ties | - |
| [kuotient/Llama-3-Ko-8B-dare-ties](https://huggingface.co/kuotient/Llama-3-Ko-8B-dare-ties) | Dare-ties | - |
| [kuotient/Llama-3-Ko-8B-TA](https://huggingface.co/kuotient/Llama-3-Ko-8B-TA) | Task Arithmetic(maybe...? not sure about this) | - |
| WIP | Model stock(I don't read this paper yet but still) | - |
```python
messages = [
{"role": "system", "content": "친절한 챗봇으로서 상대방의 요청에 최대한 자세하고 친절하게 답하자. 모든 대답은 한국어(Korean)으로 대답해줘."},
{"role": "user", "content": "피보나치 수열이 뭐야? 그리고 피보나치 수열에 대해 파이썬 코드를 짜줘볼래?"},
]
---
피보나치 수열은 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 985, 1597, 2584, 4181, 6765,...와 같은 수열로, 이 수열의 각 항은 이전 두 항의 합으로 구성됩니다. 즉, 첫 번째 항은 1, 두 번째 항은 1, 세 번째 항은 1+2=3, 네 번째 항은 2+3=5, 다섯 번째 항은 3+5=8, 여섯 번째 항은 5+8=13, 일곱 번째 항은 8+13=21, 여덟 번째 항은 13+21=34, 구체적으로는 첫 번째 항과 두 번째 항을 더한 결과입니다.
이러한 수열은 13세기 이탈리아 수학자 레오나르도 피보나치가 처음으로 발견했습니다. 피보나치 수열은 수학, 컴퓨터 과학, 경제학, 생물학 등 다양한 분야에서 응용됩니다.
다음은 피보나치 수열을 생성하는 파이썬 코드입니다.
``(포맷이 깨져서..🤗)`python
def fibonacci(n):
if n == 0: return 0
elif n == 1: return 1
else: return fibonacci(n-1) + fibonacci(n-2)
``🤗`
이 코드를 실행하면 피보나치 수열을 생성할 수 있습니다. 예를 들어, `fibonacci(5)`를 실행하면 5번째 항인 5가 출력됩니다.
이 코드는 수열의 각 항을 계산하는 함수를 정의했습니다. 함수의 이름은 `fibonacci`입니다. 함수는 두 개의 입력인 `n`을 받습니다. `n`이 0이면 0을 반환하고, `n`이 1이면 1을 반환합니다. 그렇지 않으면 이전 두 항을 더한 값을 반환합니다.
이 코드를 실행하면 다음과 같은 결과를 얻을 수 있습니다.
```python
print(fibonacci(0)) # 0
print(fibonacci(1)) # 1
print(fibonacci(2)) # 1
print(fibonacci(3))
```
```python
messages = [
{"role": "system", "content": "친절한 챗봇으로서 상대방의 요청에 최대한 자세하고 친절하게 답하자. 모든 대답은 한국어(Korean)으로 대답해줘."},
{"role": "user", "content": "한국의 제헌 헌법에 대해서 자세히 설명해주세요."},
]
---
한국의 제헌헌법은 1948년 7월 12일에 제정된 대한민국의 헌법으로, 이헌 5조에 따라 1948년 7월 17일에 공포된 헌법입니다. 이 헌법은 대한민국의 기본적인 정치질서, 정부조직, 국민의 권리와 의무, 행정기관의 조직과 권한, 재판제도, 지방자치제도, 선거제도, 헌법 개정절차 등에 대한 기본적인 사항을 규정하고 있습니다.
제헌헌법은 1948년 5월 10일에 제정된 헌법 제정위원회에서 작성된 헌법 초안을 수정, 보완하여 1948년 6월 5일에 국회에서 의결한 후, 1948년 7월 12일에 공포되었습니다. 이 헌법은 1948년 7월 17일에 시행되었습니다.
이 헌법은 민주공화국의 기본원칙으로서, 국민주권, 인권보장, 민주적 기본질서, 법치주의를 기본원칙으로 합니다. 또한, 국민의 기본권을 보장하고, 행정기관의 조직과 권한을 정하여 국가의 권력을 분산하고, 지방자치제도를 구축하여 민주적이고 공정한 정부를 구축하려는 목적을 가지고 있습니다.
이 헌법은 1987년 10월 29일에 개정된 헌법으로 대체되었습니다. 이 헌법은 1987년 10월 29일에 제정된 헌법 개정위원회에서 작성된 헌법 초안을 수정, 보완하여 1987년 12월 30일에 국회에서 의결한 후, 1988년 2월 25일에 공포되었습니다. 이 헌법은 민주화운동의 결과로 만들어진 헌법으로, 민주화의 이념을 강조하고, 국민의 기본권을 보장하고, 행정기관의 조직과 권한을 정하여 민주적이고 공정한 정부를 구축하려는 목적을 가지고 있습니다.
이 헌법은 대한민국의 헌법으로서, 현재까지도 대한민국의 헌법으로서 기능하고 있습니다.
```
```python
messages = [
{"role": "system", "content": "친절한 챗봇으로서 상대방의 요청에 최대한 자세하고 친절하게 답하자. 모든 대답은 한국어(Korean)으로 대답해줘."},
{"role": "user", "content": "세종대왕의 집현전 맥북 던짐 사건에 대해서 알고 있어?"},
]
---
세종대왕의 집현전 맥북 던짐 사건은 1443년 세종대왕이 집현전에서 의궤를 작성하는 과정에서 일어난 일화입니다. 당시 집현전의 학자들은 의궤를 작성하는 데에 어려움을 겪고 있었는데, 이에 세종대왕은 직접 집현전에 내려가서 학자들을 격려하고 의궤 작성에 필요한 자료를 요청했습니다. 하지만 학자들은 자료를 찾을 수 없어 답답해하던 중에 한 학자가 맥북에 있는 자료를 찾는 방법을 생각해냈고, 이 방법을 통해 의궤를 작성할 수 있었습니다. 이 일화는 세종대왕이 학자들에게는 지혜를 주고, 학자들은 왕에게는 의궤를 작성할 수 있는 방법을 보여주는 좋은 예가 됩니다.
이 사건은 세종대왕이 학자들에게 지식을 요구하고 학자들은 이를 해결하기 위해 노력하는 모습을 보여주는 좋은 예로, 학문과 왕의 지혜가 조화를 이룬 좋은 사례입니다. 또한, 이 사건은 우리나라의 역사에서 중요한 사건으로, 우리는 이 사건을 통해 세종대왕의 학문 지지 정책과 학자의 노력을 알 수 있습니다.
이 사건이 일어난 후, 조선 왕조는 학문을 장려하는 정책을 펼쳤고, 조선 후기에는 학자들이 왕조를 지원받아 학문을 연구할 수 있는 환경이 조성되었습니다. 이 사건은 우리나라 학문 발전에 큰 영향을 미쳤습니다.
이 사건은 세종대왕의 지혜와 학자들의 노력이 조화를 이룬 좋은 예입니다. 우리는 이 사건을 통해 세종대왕의 학문 지지 정책과 학자의 노력을 배워야 합니다.
```
```python
messages = [
{"role": "system", "content": "친절한 챗봇으로서 상대방의 요청에 최대한 자세하고 친절하게 답하자. 모든 대답은 한국어(Korean)으로 대답해줘."},
{"role": "user", "content": "나는 오늘 사과 5개를 갖고있어. 그리고 어제 나는 사과 2개를 먹었지. 내가 지금 가진 사과는 몇 개지?"},
]
---
사과 5개 중에서 2개를 먹었으니, 아직 남은 사과는 5개에서 2개를 뺀 3개입니다! 😊
```
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 [TIES](https://arxiv.org/abs/2306.01708) merge method using [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) as a base.
### Models Merged
The following models were included in the merge:
* [beomi/Llama-3-Open-Ko-8B](https://huggingface.co/beomi/Llama-3-Open-Ko-8B)
* [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: meta-llama/Meta-Llama-3-8B
# no parameters necessary for base model
- model: meta-llama/Meta-Llama-3-8B-Instruct
parameters:
density: 0.8
weight: 0.5
- model: beomi/Llama-3-Open-Ko-8B
parameters:
density: 0.8
weight: 0.5
merge_method: ties
parameters:
rescale: true
int8_mask: true
base_model: meta-llama/Meta-Llama-3-8B
dtype: bfloat16
```
사족: dare-ties 결과가 더 좋아보이는데, 아마 density 차이 때문으로 보임.
|
{"language": ["ko"], "license": "other", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["meta-llama/Meta-Llama-3-8B", "beomi/Llama-3-Open-Ko-8B", "meta-llama/Meta-Llama-3-8B-Instruct"], "license_name": "llama3"}
|
kuotient/Llama-3-Ko-8B-ties
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"ko",
"arxiv:2306.01708",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:beomi/Llama-3-Open-Ko-8B",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T18:40:05+00:00
|
null | null |
{}
|
sibylexpe/ModelsXL
| null |
[
"region:us"
] | null |
2024-04-24T18:40:43+00:00
|
|
audio-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ast-finetuned-audioset-16-16-0.442-finetuned-gtzan
This model is a fine-tuned version of [ast-finetuned-audioset-16-16-0.442](https://huggingface.co/ast-finetuned-audioset-16-16-0.442) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3315
- Accuracy: 0.93
## 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: 20
- eval_batch_size: 20
- seed: 42
- optimizer: Adam-8bits 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8802 | 1.0 | 45 | 0.5267 | 0.85 |
| 0.3183 | 2.0 | 90 | 0.5893 | 0.81 |
| 0.1094 | 3.0 | 135 | 0.4421 | 0.89 |
| 0.0259 | 4.0 | 180 | 0.4100 | 0.88 |
| 0.0291 | 5.0 | 225 | 0.3695 | 0.9 |
| 0.0409 | 6.0 | 270 | 0.3071 | 0.91 |
| 0.0152 | 7.0 | 315 | 0.3482 | 0.92 |
| 0.0003 | 8.0 | 360 | 0.3187 | 0.94 |
| 0.0003 | 9.0 | 405 | 0.3258 | 0.93 |
| 0.0004 | 10.0 | 450 | 0.3315 | 0.93 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"tags": ["generated_from_trainer"], "datasets": ["marsyas/gtzan"], "metrics": ["accuracy"], "base_model": "MIT/ast-finetuned-audioset-16-16-0.442", "model-index": [{"name": "ast-finetuned-audioset-16-16-0.442-finetuned-gtzan", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "GTZAN", "type": "marsyas/gtzan", "config": "all", "split": "train", "args": "all"}, "metrics": [{"type": "accuracy", "value": 0.93, "name": "Accuracy"}]}]}]}
|
Ostixe360/ast-finetuned-audioset-16-16-0.442-finetuned-gtzan
| null |
[
"transformers",
"tensorboard",
"safetensors",
"audio-spectrogram-transformer",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:MIT/ast-finetuned-audioset-16-16-0.442",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T18:40:54+00:00
|
question-answering
|
transformers
|
# Model Card for Model ID
This model is a fine-tuned version of Llama-3-8B-Instruct on the BatchPrompting dataset, which spans 13 diverse NLP tasks. The model has been fine-tuned to effectively perform batch prompting - answering multiple questions concatenated into a single prompt in one inference pass.
## Model Details
This model is a fine-tuned version of Llama-3-8B-Instruct on the BatchPrompting dataset, which spans 13 diverse NLP tasks. The model has been fine-tuned to effectively perform batch prompting - answering multiple questions concatenated into a single prompt in one inference pass.
### Model Description
<!-- Provide a longer summary of what this model is. TODO-->
- **Developed by:** Alex Chandler, Sebastian Joseph
- **Model type:** Large Language Model (Llama-3 variant
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model [optional]:** Llama-3-8B-Instruct
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** Forthcoming
- **Paper:** Forthcoming
- **Demo:** Forthcoming
## Uses
### How to Use
Use with transformers
See the snippet below for usage with Transformers:
```python
import transformers
import torch
model_id = "achandlr/Llama-3-8B-Instruct-BatchPromptQA"
# Load the model pipeline
pipeline = transformers.pipeline("text-generation", model=model_id)
# Generate text using the pipeline
generated_text = pipeline("Hey how are you doing today?")
print(generated_text)
```
### Direct Use
The model can be used for efficient question-answering on a variety of NLP tasks by concatenating multiple questions into a single prompt. It demonstrates strong generalization to unseen tasks and maintains performance with larger batch sizes compared to the non-fine-tuned model.
### Out-of-Scope Use
The model should not be used for tasks that may cause harm or for generating factually incorrect or biased content. Caution should be exercised if using the model for high-stakes decision making.
## Bias, Risks, and Limitations
The model may exhibit biases present in its pretraining data or the BatchPrompting dataset. It has not been extensively tested for fairness or potential misuse. Performance may degrade on out-of-distribution examples or tasks very dissimilar to the training data.
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the model's potential limitations and biases. The model's outputs should be carefully monitored, especially when used for sensitive applications. More testing is needed to fully characterize its capabilities and shortcomings.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
The model was fine-tuned on our BatchPrompting dataset consisting of 13 NLP tasks:
- **GLUE Benchmark Tasks**: A collection of datasets used for evaluating the performance of models on a variety of natural language understanding tasks.
- **Mathematical Reasoning Datasets**:
- **GSM8K**: Focuses on numerical and logical reasoning challenges.
- **GSM8K-Hard**: Contains more complex problems from the GSM8K dataset.
- **CommonsenseQA**: Tests the model's commonsense reasoning ability through multiple-choice question answering.
- **RACE Reading Comprehension Dataset**: Consists of passages and questions designed to assess reading comprehension, derived from English exams.
### Training Procedure
The model was fine-tuned using the LoRA method.
#### Training Hyperparameters
- **Training regime:** Forthcoming <!--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. -->
Forthcoming
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
Testing Data, Factors & Metrics
Evaluation was performed on tasks that were excluded from the training run. Key metrics included accuracy and BatchPrompt error rate (failure to answer a question or conform to the specified format).
A table of our results is forthcoming.
### Testing Data, Factors & Metrics
Forthcoming
#### Testing Data
Forthcoming
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Metrics
Forthcoming
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
Forthcoming
[More Information Needed]
#### Summary
Forthcoming
## Model Examination [optional]
Forthcoming
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
<!-- 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).
## Environmental Impact
- **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]
-->
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation
Forthcoming
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
Forthcoming
**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]
|
{"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["batch prompting", "batch", "BatchPrompt", "BatchPrompting", "GLUE", "Llama", "fine-tuned", "Llama3", "Llama-3-8B-Instruct"], "datasets": ["achandlr/BatchPrompting"], "metrics": ["accuracy"], "pipeline_tag": "question-answering"}
|
achandlr/Llama-3-8B-Instruct-BatchPromptQA
| null |
[
"transformers",
"safetensors",
"batch prompting",
"batch",
"BatchPrompt",
"BatchPrompting",
"GLUE",
"Llama",
"fine-tuned",
"Llama3",
"Llama-3-8B-Instruct",
"question-answering",
"en",
"dataset:achandlr/BatchPrompting",
"arxiv:1910.09700",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T18:41:11+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": []}
|
SamaahKhan/flan-before-fine-tuning
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T18:41:18+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. -->
# mohsenfayyaz/Meta-Llama-3-8B-Instruct_esnli_5000_Lora_lr2e-6_1ep
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.4651
- eval_runtime: 2.8947
- eval_samples_per_second: 69.092
- eval_steps_per_second: 8.637
- epoch: 0.9984
- step: 78
## 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-06
- train_batch_size: 2
- eval_batch_size: 8
- seed: 0
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- PEFT 0.9.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.19.1
|
{"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "mohsenfayyaz/Meta-Llama-3-8B-Instruct_esnli_5000_Lora_lr2e-6_1ep", "results": []}]}
|
mohsenfayyaz/Meta-Llama-3-8B-Instruct_esnli_5000_Lora_lr2e-6_1ep
| null |
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null |
2024-04-24T18:41:41+00:00
|
null |
transformers
|
# Uploaded model
- **Developed by:** FeinFein
- **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"}
|
FeinFein/llama3_police
| 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 |
2024-04-24T18:42:38+00:00
|
null |
transformers
|
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/TroyDoesAI/Mermaid-Llama-3-4B-Pruned
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-4B-Pruned-GGUF/resolve/main/Mermaid-Llama-3-4B-Pruned.Q2_K.gguf) | Q2_K | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-4B-Pruned-GGUF/resolve/main/Mermaid-Llama-3-4B-Pruned.IQ3_XS.gguf) | IQ3_XS | 2.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-4B-Pruned-GGUF/resolve/main/Mermaid-Llama-3-4B-Pruned.Q3_K_S.gguf) | Q3_K_S | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-4B-Pruned-GGUF/resolve/main/Mermaid-Llama-3-4B-Pruned.IQ3_S.gguf) | IQ3_S | 2.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-4B-Pruned-GGUF/resolve/main/Mermaid-Llama-3-4B-Pruned.IQ3_M.gguf) | IQ3_M | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-4B-Pruned-GGUF/resolve/main/Mermaid-Llama-3-4B-Pruned.Q3_K_M.gguf) | Q3_K_M | 2.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-4B-Pruned-GGUF/resolve/main/Mermaid-Llama-3-4B-Pruned.Q3_K_L.gguf) | Q3_K_L | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-4B-Pruned-GGUF/resolve/main/Mermaid-Llama-3-4B-Pruned.IQ4_XS.gguf) | IQ4_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-4B-Pruned-GGUF/resolve/main/Mermaid-Llama-3-4B-Pruned.Q4_K_S.gguf) | Q4_K_S | 2.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-4B-Pruned-GGUF/resolve/main/Mermaid-Llama-3-4B-Pruned.Q4_K_M.gguf) | Q4_K_M | 2.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-4B-Pruned-GGUF/resolve/main/Mermaid-Llama-3-4B-Pruned.Q5_K_S.gguf) | Q5_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-4B-Pruned-GGUF/resolve/main/Mermaid-Llama-3-4B-Pruned.Q5_K_M.gguf) | Q5_K_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-4B-Pruned-GGUF/resolve/main/Mermaid-Llama-3-4B-Pruned.Q6_K.gguf) | Q6_K | 3.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-4B-Pruned-GGUF/resolve/main/Mermaid-Llama-3-4B-Pruned.Q8_0.gguf) | Q8_0 | 4.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-4B-Pruned-GGUF/resolve/main/Mermaid-Llama-3-4B-Pruned.f16.gguf) | f16 | 9.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
{"language": ["en"], "license": "cc-by-4.0", "library_name": "transformers", "base_model": "TroyDoesAI/Mermaid-Llama-3-4B-Pruned", "quantized_by": "mradermacher"}
|
mradermacher/Mermaid-Llama-3-4B-Pruned-GGUF
| null |
[
"transformers",
"gguf",
"en",
"base_model:TroyDoesAI/Mermaid-Llama-3-4B-Pruned",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T18:43:26+00:00
|
graph-ml
| null |
# PreMode
This is the repository for our manuscript "PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context" posted on bioRxiv: https://www.biorxiv.org/content/10.1101/2024.02.20.581321v3
# Data
Unzip the files with this script:
```
bash unzip.files.sh
```
Unfortunately we are not allowed to share the HGMD data, so in the `data.files/pretrain/training.*` files we removed all the pathogenic variants from HGMD (49218 in total). This might affect the plots of `analysis/figs/fig.sup.12.pdf` and `analysis/figs/fig.sup.13.pdf` if you re-run the R codes in `analysis/` folder.
We shared the trained weights of our models trained using HGMD instead.
# Install Packages
Please install the necessary packages using
```
mamba env create -f PreMode.yaml
mamba env create -f r4-base.yaml
```
You can check the installation by running
```
conda activate PreMode
python train.py --conf scripts/TEST.yaml --mode train
```
If no error occurs, it means successful installation.
# New Experiment
## Start from scratch and use our G/LoF datasets
1. Please prepare a folder under `scripts/` and create a file named `pretrain.seed.0.yaml` inside the folder, check `scripts/PreMode/pretrain.seed.0.yaml` for example.
2. Run pretrain in pathogenicity task:
```
python train.py --conf scripts/NEW_FOLDER/pretrain.seed.0.yaml
```
3. Prepare transfer learning config files:
```
bash scripts/DMS.prepare.yaml.sh scripts/NEW_FOLDER/
```
4. Run transfer learning:
```
bash scripts/DMS.5fold.run.sh scripts/NEW_FOLDER TASK_NAME GPU_ID
```
If you have multiple tasks, just separate each task by comma in the TASK_NAME, like "task_1,task_2,task_3".
5. (Optional) To reuse the transfer learning tasks in our paper using 8 GPU cards, just do
```
bash transfer.all.sh scripts/NEW_FOLDER
```
If you only have one GPU card, then do
```
bash transfer.all.in.one.card.sh scripts/NEW_FOLDER
```
6. Save inference results:
```
bash scripts/DMS.5fold.inference.sh scripts/NEW_FOLDER analysis/NEW_FOLDER TASK_NAME GPU_ID
```
7. You'll get a folder `analysis/NEW_FOLDER/TASK_NAME` with 5 `.csv` files, each file has 4 columns `logits.FOLD.[0-3]`. Each column represent the G/LoF prediction at one cross-validation (closer to 0 means more likely GoF, closer to 1 means more likely LoF). We suggest averaging the predictions at 4 columns.
## Only transfer learning, user defined mode-of-action datasets
1. Prepare a `.csv` file for training and inference, there are two accepted formats:
+ Format 1 (only for missense variants):
| uniprotID | aaChg | score | ENST |
| :-: | :-: | :-: | :-: |
| P15056 | p.V600E | 1 | ENST00000646891 |
| P15056 | p.G446V | -1 | ENST00000646891 |
+ `uniprotID`: the uniprot ID of the protein.
+ `aaChg`: the amino acid change induced by missense variant.
+ `score`: 1 for GoF, -1 for LoF. For inference it is not required. For DMS, this could be experimental readouts. If you have multiplexed assays, you can change it to `score.1, score.2, score.3, ..., score.N`.
+ `ENST` (optional): the ensemble transcript ID that matched the uniprotID.
+ Format 2 (can be missense variant or multiple variants):
| uniprotID | ref | alt | pos.orig | score | ENST | wt.orig | sequence.len.orig
| :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
| P15056 | V | E | 600 | 1 | ENST00000646891 | ... | 766 |
| P15056 | G | V | 446 | -1 | ENST00000646891 | ... | 766 |
| P15056 | G;V | V;F | 446;471 | -1 | ENST00000646891 | ... | 766 |
+ `uniprotID`: the uniprot ID of the protein.
+ `ref`: the reference amino acid, if multiple variants, separated by ";".
+ `alt`: the alternative, if multiple variants, separated by ";" in the same order of "ref".
+ `pos.orig`: the amino acid change position, if multiple variants, separated by ";" in the same order of "ref".
+ `score`: same as above.
+ `ENST` (optional): same as above.
+ `wt.orig`: the wild type protein sequence, in the uniprot format.
+ `sequence.len.orig`: the wild type protein sequence length.
+ If you prepared your input in Format 1, please run
```
bash parse.input.table/parse.input.table.sh YOUR_FILE TRANSFORMED_FILE
```
to transform it to Format 2, note it will drop some lines if your aaChg doesn't match the corresponding alphafold sequence.
2. Prepare a config file for training the model and inference.
```
bash scripts/prepare.new.task.yaml.sh PRETRAIN_MODEL_NAME YOUR_TASK_NAME YOUR_TRAINING_FILE YOUR_INFERENCE_FILE TASK_TYPE MODE_OF_ACTION_N
```
+ `PRETRAIN_MODEL_NAME` could be one of the following:
+ `scripts/PreMode`: Default PreMode
+ `scripts/PreMode.ptm`: PreMode + ptm as input
+ `scripts/PreMode.noStructure`: PreMode without structure input
+ `scripts/PreMode.noESM`: PreMode, replaced ESM2 input with one-hot encodings of 20 AAs.
+ `scripts/PreMode.noMSA`: PreMode without MSA input
+ `scripts/ESM.SLP`: ESM embedding + Single Layer Perceptron
+ `YOUR_TASK_NAME` can be anything on your preference
+ `YOUR_TRAINING_FILE` is the training file you prepared in step 1.
+ `YOUR_INFERENCE_FILE` is the inference file you prepared in step 1.
+ `TASK_TYPE` could be `DMS` or `GLOF`.
+ `MODE_OF_ACTION_N` The number of dimensions of mode-of-action. For `GLOF` this is usually 1. For multiplexed `DMS` dataset, this could be the number of biochemical properties measured. Note that if it is larger than 1, then you have to make sure the `score` column in step 1 is replaced to `score.1, score.2, ..., score.N` correspondingly.
3. Run your config file
```
conda activate PreMode
bash scripts/run.new.task.sh PRETRAIN_MODEL_NAME YOUR_TASK_NAME OUTPUT_FOLDER GPU_ID
```
This should take ~30min on a NVIDIA A40 GPU depending on your data set size.
4. You'll get a file in the `OUTPUT_FOLDER` named as `YOUR_TASK_NAME.inference.result.csv`.
+ If your `TASK_TYPE` is `GLOF`, then the column `logits` will be the inference results. Closer to 0 means GoF, closer to 1 means LoF.
+ If your `TASK_TYPE` is `DMS` and `MODE_OF_ACTION_N` is 1, then the column `logits` will be the inference results. If your `MODE_OF_ACTION_N` is larger than 1, then you will get multiple columns of `logits.*`, each represent a predicted DMS measurement.
# Models & Figures in our manuscript
## Pretrained Models
Here is the list of models in our manuscript:
`scripts/PreMode/` PreMode, it takes 250 GB RAM and 4 A40 Nvidia GPUs to run, will finish in ~50h.
`scripts/ESM.SLR/` Baseline Model, ESM2 (650M) + Single Layer Perceptron
`scripts/PreMode.large.window/` PreMode, window size set to 1251 AA.
`scripts/PreMode.noESM/` PreMode, replace the ESM2 embeddings to one hot encodings of 20 AA.
`scripts/PreMode.noMSA/` PreMode, remove the MSA input.
`scripts/PreMode.noPretrain/` PreMode, but didn't pretrain on ClinVar/HGMD.
`scripts/PreMode.noStructure/` PreMode, remove the AF2 predicted structure input.
`scripts/PreMode.ptm/` PreMode, add the onehot encoding of post transcriptional modification sites as input.
`scripts/PreMode.mean.var/` PreMode, it will output both predicted value (mean) and confidence (var), used in adaptive learning tasks.
## Predicted mode-of-action
| gene | file |
| :-: | :-: |
| BRAF | `analysis/5genes.all.mut/PreMode/P15056.logits.csv` |
| RET | `analysis/5genes.all.mut/PreMode/P07949.logits.csv` |
| TP53 | `analysis/5genes.all.mut/PreMode/P04637.logits.csv` |
| KCNJ11 | `analysis/5genes.all.mut/PreMode/Q14654.logits.csv` |
| CACNA1A | `analysis/5genes.all.mut/PreMode/O00555.logits.csv` |
| SCN5A | `analysis/5genes.all.mut/PreMode/Q14524.logits.csv` |
| SCN2A | `analysis/5genes.all.mut/PreMode/Q99250.logits.csv` |
| ABCC8 | `analysis/5genes.all.mut/PreMode/Q09428.logits.csv` |
| PTEN | `analysis/5genes.all.mut/PreMode/P60484.logits.csv` |
For each file, column `logits.0` is predicted pathogenicity. column `logits.1` is predicted LoF probability, `logits.2` is predicted GoF probability.
For PTEN, column `logits.1` is predicted stability, 0 is loss, 1 is neutral, `logits.2` is predicted enzyme activity, 0 is loss, 1 is neutral
## Figures
Please go to `analysis/` folder and run the corresponding R scripts.
|
{"language": ["en"], "tags": ["biology"], "pipeline_tag": "graph-ml"}
|
gzhong/PreMode
| null |
[
"biology",
"graph-ml",
"en",
"region:us"
] | null |
2024-04-24T18:43:56+00:00
|
null | null |
{}
|
Henibergs/llama-2-7b-miniguanaco-gguf
| null |
[
"region:us"
] | null |
2024-04-24T18:45:53+00:00
|
|
text2text-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": []}
|
boringblobking/medmcqa1
| null |
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T18:46:24+00:00
|
null | null |
GGUF-IQ-Imatrix quants for [jeiku/Average_Normie_l3_v1_8B](https://huggingface.co/jeiku/Average_Normie_l3_v1_8B).
> [!WARNING]
> Compatible SillyTavern presets [here (simple)](https://huggingface.co/Lewdiculous/Model-Requests/tree/main/data/presets/cope-llama-3-0.1) or [here (Virt's)](https://huggingface.co/Virt-io/SillyTavern-Presets). <br>
> Use the latest version of KoboldCpp. **Use the provided presets.** <br>
> This is all still highly experimental, let the authors know how it performs for you, feedback is more important than ever now.
> [!NOTE]
> For **8GB VRAM** GPUs, I recommend the **Q4_K_M-imat** quant for up to 12288 context sizes.
**Original model information:**
# Average Normie v1
A model by an average normie for the average normie.
This model is a stock merge of the following models:
https://huggingface.co/cgato/L3-TheSpice-8b-v0.1.3
https://huggingface.co/Sao10K/L3-Solana-8B-v1
https://huggingface.co/ResplendentAI/Kei_Llama3_8B
The final merge then had the following LoRA applied over it:
https://huggingface.co/ResplendentAI/Theory_of_Mind_Llama3
This should be an intelligent and adept roleplaying model.
|
{"language": ["en"], "tags": ["roleplay", "llama3", "sillytavern"]}
|
Lewdiculous/Average_Normie_l3_v1_8B-GGUF-IQ-Imatrix
| null |
[
"gguf",
"roleplay",
"llama3",
"sillytavern",
"en",
"region:us"
] | null |
2024-04-24T18:46:33+00:00
|
text2text-generation
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_opus_books_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1804
- Bleu: 0.225
- Gen Len: 18.1268
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 3.6472 | 1.0 | 1617 | 3.2646 | 0.1867 | 18.1246 |
| 3.5198 | 2.0 | 3234 | 3.1804 | 0.225 | 18.1268 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "base_model": "t5-small", "model-index": [{"name": "my_awesome_opus_books_model", "results": []}]}
|
liamvbetts/my_awesome_opus_books_model
| null |
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T18:47:13+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. -->
# mohsenfayyaz/Meta-Llama-3-8B-Instruct_esnli_5000_Lora_lr2e-6_2ep
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.2777
- eval_runtime: 2.8582
- eval_samples_per_second: 69.975
- eval_steps_per_second: 8.747
- epoch: 1.9968
- step: 156
## 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-06
- train_batch_size: 2
- eval_batch_size: 8
- seed: 0
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- PEFT 0.9.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.19.1
|
{"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "mohsenfayyaz/Meta-Llama-3-8B-Instruct_esnli_5000_Lora_lr2e-6_2ep", "results": []}]}
|
mohsenfayyaz/Meta-Llama-3-8B-Instruct_esnli_5000_Lora_lr2e-6_2ep
| null |
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null |
2024-04-24T18:47:29+00:00
|
image-to-text
|
transformers
|
[Evaluation on chexpert-plus](https://github.com/Stanford-AIMI/chexpert-plus)
|
{"language": "en", "license": "mit", "library_name": "transformers", "tags": ["image-to-text"], "widget": [{"src": "https://huggingface.co/IAMJB/interpret-cxr-impression-baseline/resolve/main/effusions-bibasal.jpg"}, {"src": "https://huggingface.co/IAMJB/interpret-cxr-impression-baseline/resolve/main/Chest-X-ray-taken-on-2-nd-day-of-admission-in-the_Q320.jpg"}, {"src": "https://huggingface.co/IAMJB/interpret-cxr-impression-baseline/resolve/main/effusions-bibasal.jpg"}]}
|
IAMJB/mimic-cxr-findings-baseline
| null |
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-to-text",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T18:47:58+00:00
|
reinforcement-learning
|
stable-baselines3
|
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "246.90 +/- 19.00", "name": "mean_reward", "verified": false}]}]}]}
|
yosthin06/ppo-LunarLander-v2-yosthin
| null |
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-24T18:48:11+00:00
|
null | null |
{}
|
Dua020/Whisper
| null |
[
"region:us"
] | null |
2024-04-24T18:48:30+00:00
|
|
text-generation
|
transformers
|
{}
|
LucileFavero/llama_s3_1
| null |
[
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T18:48:36+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. -->
# llava_clip_llama3_8b_finetune_8192
This model is a fine-tuned version of [MFuyu/llava_clip_llama3_8b_pretrain_8192](https://huggingface.co/MFuyu/llava_clip_llama3_8b_pretrain_8192) 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.39.2
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"tags": ["generated_from_trainer"], "base_model": "MFuyu/llava_clip_llama3_8b_pretrain_8192", "model-index": [{"name": "llava_clip_llama3_8b_finetune_8192", "results": []}]}
|
MFuyu/llava_clip_llama3_8b_finetune_8192
| null |
[
"transformers",
"safetensors",
"llava",
"pretraining",
"generated_from_trainer",
"base_model:MFuyu/llava_clip_llama3_8b_pretrain_8192",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T18:49:12+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. -->
# 0.001_ablation_4iters_bs256_nodpo_sample2_iter_3
This model is a fine-tuned version of [ShenaoZ/0.001_ablation_4iters_bs256_nodpo_sample2_iter_2](https://huggingface.co/ShenaoZ/0.001_ablation_4iters_bs256_nodpo_sample2_iter_2) on the updated and the original datasets.
## 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": ["updated", "original"], "base_model": "ShenaoZ/0.001_ablation_4iters_bs256_nodpo_sample2_iter_2", "model-index": [{"name": "0.001_ablation_4iters_bs256_nodpo_sample2_iter_3", "results": []}]}
|
ShenaoZ/0.001_ablation_4iters_bs256_nodpo_sample2_iter_3
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZ/0.001_ablation_4iters_bs256_nodpo_sample2_iter_2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T18:50:04+00:00
|
text-generation
|
transformers
|
{}
|
CMU-AIR2/math-deepseek-FULL-ArithHard-low-lr-100k
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T18:51:37+00:00
|
|
null | null |
{"license": "unknown"}
|
wearedogs/wearedogs
| null |
[
"license:unknown",
"region:us"
] | null |
2024-04-24T18:52:11+00:00
|
|
feature-extraction
|
transformers
|
{"license": "mit"}
|
avsolatorio/GIST-all-MiniLM-L6-v2-onnx
| null |
[
"transformers",
"onnx",
"bert",
"feature-extraction",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T18:52:36+00:00
|
|
null |
transformers
|
# Uploaded model
- **Developed by:** alberthtan
- **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"}
|
alberthtan/lora_model_1065_samples
| 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 |
2024-04-24T18:52:48+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. -->
# mohsenfayyaz/Meta-Llama-3-8B-Instruct_esnli_5000_Lora_lr2e-6_3ep
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.1635
- eval_runtime: 2.8733
- eval_samples_per_second: 69.606
- eval_steps_per_second: 8.701
- epoch: 2.9952
- step: 234
## 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-06
- train_batch_size: 2
- eval_batch_size: 8
- seed: 0
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- PEFT 0.9.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.19.1
|
{"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "mohsenfayyaz/Meta-Llama-3-8B-Instruct_esnli_5000_Lora_lr2e-6_3ep", "results": []}]}
|
mohsenfayyaz/Meta-Llama-3-8B-Instruct_esnli_5000_Lora_lr2e-6_3ep
| null |
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null |
2024-04-24T18:53:16+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. -->
# 0.001_ablation_5iters_bs256_nodpo_iter_5
This model is a fine-tuned version of [ShenaoZ/0.001_ablation_5iters_bs256_nodpo_iter_4](https://huggingface.co/ShenaoZ/0.001_ablation_5iters_bs256_nodpo_iter_4) on the updated and the original datasets.
## 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": ["updated", "original"], "base_model": "ShenaoZ/0.001_ablation_5iters_bs256_nodpo_iter_4", "model-index": [{"name": "0.001_ablation_5iters_bs256_nodpo_iter_5", "results": []}]}
|
ShenaoZ/0.001_ablation_5iters_bs256_nodpo_iter_5
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZ/0.001_ablation_5iters_bs256_nodpo_iter_4",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T18:54:06+00:00
|
null | null |
{}
|
sushane123/fine-tuned-indicbart_2
| null |
[
"region:us"
] | null |
2024-04-24T18:54:37+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": []}
|
yl2342/Llama-3-8Bing-ORPO
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T18:54:50+00:00
|
text-generation
|
transformers
|
# Uploaded model
- **Developed by:** baconnier
- **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", "trl", "orpo"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}
|
baconnier/finance_orpo_llama3_8B_51K
| null |
[
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"orpo",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T18:55:30+00:00
|
null | null |
{}
|
Sam137/deepseek-local-coder
| null |
[
"region:us"
] | null |
2024-04-24T18:56:13+00:00
|
|
null | null |
{"license": "openrail"}
|
CaioConteudos/PapyHoo
| null |
[
"license:openrail",
"region:us"
] | null |
2024-04-24T18:56:43+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. -->
# mohsenfayyaz/Meta-Llama-3-8B-Instruct_esnli_5000_Lora_lr2e-6_4ep
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.1567
- eval_runtime: 2.8629
- eval_samples_per_second: 69.859
- eval_steps_per_second: 8.732
- epoch: 3.9936
- step: 312
## 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-06
- train_batch_size: 2
- eval_batch_size: 8
- seed: 0
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- PEFT 0.9.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.19.1
|
{"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "mohsenfayyaz/Meta-Llama-3-8B-Instruct_esnli_5000_Lora_lr2e-6_4ep", "results": []}]}
|
mohsenfayyaz/Meta-Llama-3-8B-Instruct_esnli_5000_Lora_lr2e-6_4ep
| null |
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null |
2024-04-24T18:58:59+00:00
|
null | null |
{}
|
mob2711/phi-3-4k-instruct-domain-vi-sft
| null |
[
"tensorboard",
"safetensors",
"region:us"
] | null |
2024-04-24T18:59:21+00:00
|
|
text2text-generation
|
transformers
|
{}
|
tilakM/t5-base-pos2neg
| null |
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T19:00:19+00:00
|
|
null |
transformers
|
## Installation from source
```bash
git clone https://github.com/foundation-model-stack/fms-extras
cd fms-extras
pip install -e .
```
## Description
This model is intended to be used as an accelerator for [llama3 8b (instruct)](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) and takes inspiration
from the Medusa speculative decoding architecture. This accelerator modifies the MLP into a multi-stage MLP, where each stage predicts
a single token in the draft based on both a state vector and sampled token
from the prior stage (the base model can be considered stage 0).
The state vector from the base model provides contextual information to the accelerator,
while conditioning on prior sampled tokens allows it to produce higher-quality draft n-grams.
Note: The underlying MLP speculator is a generic architecture that can be trained with any generative model to accelerate inference.
Training is light-weight and can be completed in only a few days depending on base model size and speed.
## Repository Links
1. [Paged Attention KV-Cache / Speculator](https://github.com/foundation-model-stack/fms-extras)
2. [Production Server with speculative decoding](https://github.com/IBM/text-generation-inference.git)
3. [Speculator training](https://github.com/foundation-model-stack/fms-fsdp/pull/35)
## Samples
_Note: For all samples, your environment must have access to cuda_
### Production Server Sample
*To try this out running in a production-like environment, please use the pre-built docker image:*
#### Setup
```bash
HF_HUB_CACHE=/hf_hub_cache
chmod a+w $HF_HUB_CACHE
HF_HUB_TOKEN="your huggingface hub token"
TGIS_IMAGE=quay.io/wxpe/text-gen-server:main.ee927a4
docker pull $TGIS_IMAGE
# optionally download llama3-8b-instruct if the weights do not already exist
docker run --rm \
-v $HF_HUB_CACHE:/models \
-e HF_HUB_CACHE=/models \
-e TRANSFORMERS_CACHE=/models \
$TGIS_IMAGE \
text-generation-server download-weights \
meta-llama/Meta-Llama-3-8B-Instruct \
--token $HF_HUB_TOKEN
# optionally download the speculator model if the weights do not already exist
docker run --rm \
-v $HF_HUB_CACHE:/models \
-e HF_HUB_CACHE=/models \
-e TRANSFORMERS_CACHE=/models \
$TGIS_IMAGE \
text-generation-server download-weights \
ibm-fms/llama3-8b-accelerator \
--token $HF_HUB_TOKEN
# note: if the weights were downloaded separately (not with the above commands), please place them in the HF_HUB_CACHE directory and refer to them with /models/<model_name>
docker run -d --rm --gpus all \
--name my-tgis-server \
-p 8033:8033 \
-v $HF_HUB_CACHE:/models \
-e HF_HUB_CACHE=/models \
-e TRANSFORMERS_CACHE=/models \
-e MODEL_NAME=meta-llama/Meta-Llama-3-8B-Instruct \
-e SPECULATOR_NAME=ibm-fms/llama3-8b-accelerator \
-e FLASH_ATTENTION=true \
-e PAGED_ATTENTION=true \
-e DTYPE=float16 \
$TGIS_IMAGE
# check logs and wait for "gRPC server started on port 8033" and "HTTP server started on port 3000"
docker logs my-tgis-server -f
# get the client sample (Note: The first prompt will take longer as there is a warmup time)
conda create -n tgis-client-env python=3.11
conda activate tgis-client-env
git clone --branch main --single-branch https://github.com/IBM/text-generation-inference.git
cd text-generation-inference/integration_tests
make gen-client
pip install . --no-cache-dir
```
#### Run Sample
```bash
python sample_client.py
```
_Note: first prompt may be slower as there is a slight warmup time_
### Minimal Sample
#### Install
```bash
git clone https://github.com/foundation-model-stack/fms-extras
(cd fms-extras && pip install -e .)
pip install transformers==4.35.0 sentencepiece numpy
```
#### Run Sample
##### batch_size=1 (compile + cudagraphs)
```bash
MODEL_PATH=/path/to/llama3/hf/Meta-Llama-3-8B-Instruct
python fms-extras/scripts/paged_speculative_inference.py \
--variant=llama3.8b \
--model_path=$MODEL_PATH \
--model_source=hf \
--tokenizer=$MODEL_PATH \
--speculator_path=ibm-fms/llama3-8b-accelerator \
--speculator_source=hf \
--speculator_variant=3_2b \
--top_k_tokens_per_head=4,3,2,2 \
--compile \
--compile_mode=reduce-overhead
```
##### batch_size=1 (compile)
```bash
MODEL_PATH=/path/to/llama3/hf/Meta-Llama-3-8B-Instruct
python fms-extras/scripts/paged_speculative_inference.py \
--variant=llama3.8b \
--model_path=$MODEL_PATH \
--model_source=hf \
--tokenizer=$MODEL_PATH \
--speculator_path=ibm-fms/llama3-8b-accelerator \
--speculator_source=hf \
--speculator_variant=3_2b \
--top_k_tokens_per_head=4,3,2,2 \
--compile
```
##### batch_size=4 (compile)
```bash
MODEL_PATH=/path/to/llama3/hf/Meta-Llama-3-8B-Instruct
python fms-extras/scripts/paged_speculative_inference.py \
--variant=llama3.8b \
--model_path=$MODEL_PATH \
--model_source=hf \
--tokenizer=$MODEL_PATH \
--speculator_path=ibm-fms/llama3-8b-accelerator \
--speculator_source=hf \
--speculator_variant=3_2b \
--top_k_tokens_per_head=4,3,2,2 \
--batch_input \
--compile
```
|
{"license": "llama3"}
|
ibm-fms/llama3-8b-accelerator
| null |
[
"transformers",
"safetensors",
"mlp_speculator",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T19:03:59+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. -->
# mohsenfayyaz/Meta-Llama-3-8B-Instruct_esnli_5000_Lora_lr2e-6_5ep
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.1583
- eval_runtime: 2.8616
- eval_samples_per_second: 69.89
- eval_steps_per_second: 8.736
- epoch: 4.992
- step: 390
## 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-06
- train_batch_size: 2
- eval_batch_size: 8
- seed: 0
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- PEFT 0.9.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.19.1
|
{"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "mohsenfayyaz/Meta-Llama-3-8B-Instruct_esnli_5000_Lora_lr2e-6_5ep", "results": []}]}
|
mohsenfayyaz/Meta-Llama-3-8B-Instruct_esnli_5000_Lora_lr2e-6_5ep
| null |
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null |
2024-04-24T19:04:40+00:00
|
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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": []}
|
SamaahKhan/TinyLlama-before-fine-tuning
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T19:05:04+00:00
|
null | null |
# Experiment27pasticheOgnoexperiment27multi_verse_model-7B
Experiment27pasticheOgnoexperiment27multi_verse_model-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
## 🧩 Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
- model: automerger/Experiment27Pastiche-7B
- model: automerger/Ognoexperiment27Multi_verse_model-7B
merge_method: model_stock
base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/Experiment27pasticheOgnoexperiment27multi_verse_model-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
{"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"]}
|
automerger/Experiment27pasticheOgnoexperiment27multi_verse_model-7B
| null |
[
"merge",
"mergekit",
"lazymergekit",
"automerger",
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T19:05:38+00:00
|
null | null |
{"license": "mit"}
|
daisysxm76/Skin_Tone_Prediction_model
| null |
[
"license:mit",
"region:us"
] | null |
2024-04-24T19:05:42+00:00
|
|
null | null |
{}
|
giux78/llama3-usenet
| null |
[
"safetensors",
"region:us"
] | null |
2024-04-24T19:06:08+00:00
|
|
null |
transformers
|
### Model Description
This was model was fine-tuned from TinyLlama-1.1B-Chat-v0.6.
Using Instruction Tuning and Parameter Efficient Tuning on a smaller model,
this fine-tuned model is intended to improve dialogue policy.
The instruction dataset was generated from the MultiWOZ dataset.
|
{"library_name": "transformers", "tags": []}
|
SamaahKhan/TinyLlama-after-fine-tuning
| null |
[
"transformers",
"safetensors",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T19:07:23+00:00
|
null | null |
{}
|
sohamslc5/llama-2
| null |
[
"region:us"
] | null |
2024-04-24T19:07:26+00:00
|
|
automatic-speech-recognition
|
transformers
|
{}
|
shull/whisper-medium-finetuned
| null |
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T19:08:25+00:00
|
|
text-generation
|
transformers
|
## Llamacpp imatrix Quantizations of wavecoder-ultra-1.1-6.7b
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2717">b2717</a> for quantization.
Original model: https://huggingface.co/microsoft/wavecoder-ultra-6.7b
All quants made using imatrix option with dataset provided by Kalomaze [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384)
## Prompt format
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction: {prompt}
### Response:
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [wavecoder-ultra-1.1-6.7b-Q8_0.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-Q8_0.gguf) | Q8_0 | 7.16GB | Extremely high quality, generally unneeded but max available quant. |
| [wavecoder-ultra-1.1-6.7b-Q6_K.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-Q6_K.gguf) | Q6_K | 5.52GB | Very high quality, near perfect, *recommended*. |
| [wavecoder-ultra-1.1-6.7b-Q5_K_M.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-Q5_K_M.gguf) | Q5_K_M | 4.78GB | High quality, *recommended*. |
| [wavecoder-ultra-1.1-6.7b-Q5_K_S.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-Q5_K_S.gguf) | Q5_K_S | 4.65GB | High quality, *recommended*. |
| [wavecoder-ultra-1.1-6.7b-Q4_K_M.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-Q4_K_M.gguf) | Q4_K_M | 4.08GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [wavecoder-ultra-1.1-6.7b-Q4_K_S.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-Q4_K_S.gguf) | Q4_K_S | 3.85GB | Slightly lower quality with more space savings, *recommended*. |
| [wavecoder-ultra-1.1-6.7b-IQ4_NL.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-IQ4_NL.gguf) | IQ4_NL | 3.82GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. |
| [wavecoder-ultra-1.1-6.7b-IQ4_XS.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-IQ4_XS.gguf) | IQ4_XS | 3.61GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [wavecoder-ultra-1.1-6.7b-Q3_K_L.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-Q3_K_L.gguf) | Q3_K_L | 3.59GB | Lower quality but usable, good for low RAM availability. |
| [wavecoder-ultra-1.1-6.7b-Q3_K_M.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-Q3_K_M.gguf) | Q3_K_M | 3.29GB | Even lower quality. |
| [wavecoder-ultra-1.1-6.7b-IQ3_M.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-IQ3_M.gguf) | IQ3_M | 3.11GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [wavecoder-ultra-1.1-6.7b-IQ3_S.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-IQ3_S.gguf) | IQ3_S | 2.94GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| [wavecoder-ultra-1.1-6.7b-Q3_K_S.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-Q3_K_S.gguf) | Q3_K_S | 2.94GB | Low quality, not recommended. |
| [wavecoder-ultra-1.1-6.7b-IQ3_XS.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-IQ3_XS.gguf) | IQ3_XS | 2.79GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [wavecoder-ultra-1.1-6.7b-IQ3_XXS.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-IQ3_XXS.gguf) | IQ3_XXS | 2.58GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [wavecoder-ultra-1.1-6.7b-Q2_K.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-Q2_K.gguf) | Q2_K | 2.53GB | Very low quality but surprisingly usable. |
| [wavecoder-ultra-1.1-6.7b-IQ2_M.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-IQ2_M.gguf) | IQ2_M | 2.36GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [wavecoder-ultra-1.1-6.7b-IQ2_S.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-IQ2_S.gguf) | IQ2_S | 2.19GB | Very low quality, uses SOTA techniques to be usable. |
| [wavecoder-ultra-1.1-6.7b-IQ2_XS.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-IQ2_XS.gguf) | IQ2_XS | 2.03GB | Very low quality, uses SOTA techniques to be usable. |
| [wavecoder-ultra-1.1-6.7b-IQ2_XXS.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-IQ2_XXS.gguf) | IQ2_XXS | 1.85GB | Lower quality, uses SOTA techniques to be usable. |
| [wavecoder-ultra-1.1-6.7b-IQ1_M.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-IQ1_M.gguf) | IQ1_M | 1.65GB | Extremely low quality, *not* recommended. |
| [wavecoder-ultra-1.1-6.7b-IQ1_S.gguf](https://huggingface.co/bartowski/wavecoder-ultra-1.1-6.7b-GGUF/blob/main/wavecoder-ultra-1.1-6.7b-IQ1_S.gguf) | IQ1_S | 1.52GB | Extremely low quality, *not* recommended. |
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
{"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["code"], "datasets": ["humaneval"], "metrics": ["code_eval"], "license_link": "https://huggingface.co/microsoft/wavecoder-ultra-6.7b/blob/main/LICENSE", "pipeline_tag": "text-generation", "quantized_by": "bartowski"}
|
bartowski/wavecoder-ultra-1.1-6.7b-GGUF
| null |
[
"transformers",
"gguf",
"code",
"text-generation",
"en",
"dataset:humaneval",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T19:08:33+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": []}
|
pedrogarcias/whisper-small-v2-jax
| null |
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T19:09:40+00:00
|
null | null |
{}
|
samuel-thudi/distilbert-base-uncased-finetuned-emotion
| null |
[
"region:us"
] | null |
2024-04-24T19:10:50+00:00
|
|
token-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. -->
# privacy-masknig
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3686
- Overall Precision: 0.2885
- Overall Recall: 0.2143
- Overall F1: 0.2459
- Overall Accuracy: 0.8688
- Bod F1: 0.2375
- Building F1: 0.2871
- Cardissuer F1: 0.0
- City F1: 0.2540
- Country F1: 0.3055
- Date F1: 0.2341
- Driverlicense F1: 0.2233
- Email F1: 0.2654
- Geocoord F1: 0.1603
- Givenname1 F1: 0.2161
- Givenname2 F1: 0.1507
- Idcard F1: 0.2472
- Ip F1: 0.1851
- Lastname1 F1: 0.2296
- Lastname2 F1: 0.1305
- Lastname3 F1: 0.1245
- Pass F1: 0.1980
- Passport F1: 0.2792
- Postcode F1: 0.2794
- Secaddress F1: 0.2486
- Sex F1: 0.2933
- Socialnumber F1: 0.2258
- State F1: 0.2921
- Street F1: 0.2177
- Tel F1: 0.2409
- Time F1: 0.2893
- Title F1: 0.2814
- Username F1: 0.2368
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Bod F1 | Building F1 | Cardissuer F1 | City F1 | Country F1 | Date F1 | Driverlicense F1 | Email F1 | Geocoord F1 | Givenname1 F1 | Givenname2 F1 | Idcard F1 | Ip F1 | Lastname1 F1 | Lastname2 F1 | Lastname3 F1 | Pass F1 | Passport F1 | Postcode F1 | Secaddress F1 | Sex F1 | Socialnumber F1 | State F1 | Street F1 | Tel F1 | Time F1 | Title F1 | Username F1 |
|:-------------:|:-----:|:------:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:-----------:|:-------------:|:-------:|:----------:|:-------:|:----------------:|:--------:|:-----------:|:-------------:|:-------------:|:---------:|:------:|:------------:|:------------:|:------------:|:-------:|:-----------:|:-----------:|:-------------:|:------:|:---------------:|:--------:|:---------:|:------:|:-------:|:--------:|:-----------:|
| 0.4774 | 1.0 | 62187 | 0.4611 | 0.1764 | 0.1017 | 0.1291 | 0.8380 | 0.1353 | 0.1842 | 0.0 | 0.1255 | 0.2337 | 0.1185 | 0.0936 | 0.1261 | 0.0500 | 0.0893 | 0.0506 | 0.1041 | 0.1122 | 0.1241 | 0.0463 | 0.0020 | 0.0486 | 0.1080 | 0.1726 | 0.1540 | 0.2044 | 0.0886 | 0.1588 | 0.1239 | 0.1406 | 0.1667 | 0.1583 | 0.1386 |
| 0.4205 | 2.0 | 124374 | 0.4272 | 0.2372 | 0.1567 | 0.1887 | 0.8542 | 0.1831 | 0.2706 | 0.0 | 0.1923 | 0.2819 | 0.1821 | 0.1521 | 0.1863 | 0.1197 | 0.0997 | 0.0662 | 0.1473 | 0.1512 | 0.1443 | 0.0955 | 0.0527 | 0.1678 | 0.1997 | 0.2469 | 0.2066 | 0.2641 | 0.1827 | 0.2266 | 0.1602 | 0.1879 | 0.2372 | 0.2202 | 0.2069 |
| 0.3367 | 3.0 | 186561 | 0.3686 | 0.2885 | 0.2143 | 0.2459 | 0.8688 | 0.2375 | 0.2871 | 0.0 | 0.2540 | 0.3055 | 0.2341 | 0.2233 | 0.2654 | 0.1603 | 0.2161 | 0.1507 | 0.2472 | 0.1851 | 0.2296 | 0.1305 | 0.1245 | 0.1980 | 0.2792 | 0.2794 | 0.2486 | 0.2933 | 0.2258 | 0.2921 | 0.2177 | 0.2409 | 0.2893 | 0.2814 | 0.2368 |
| 0.301 | 4.0 | 248748 | 0.3734 | 0.3073 | 0.2484 | 0.2747 | 0.8737 | 0.2565 | 0.3272 | 0.1429 | 0.2634 | 0.3355 | 0.2707 | 0.2591 | 0.3032 | 0.2153 | 0.2458 | 0.1847 | 0.2757 | 0.2252 | 0.2594 | 0.1680 | 0.1551 | 0.2410 | 0.3080 | 0.2945 | 0.2488 | 0.3139 | 0.2522 | 0.3007 | 0.2447 | 0.2584 | 0.3107 | 0.2933 | 0.2880 |
| 0.2451 | 5.0 | 310935 | 0.3895 | 0.3091 | 0.2664 | 0.2862 | 0.8744 | 0.2720 | 0.3313 | 0.0 | 0.2773 | 0.3470 | 0.2803 | 0.2732 | 0.3109 | 0.2202 | 0.2554 | 0.1945 | 0.2899 | 0.2382 | 0.2539 | 0.1800 | 0.1651 | 0.2514 | 0.3156 | 0.2982 | 0.2720 | 0.3364 | 0.2695 | 0.3196 | 0.2561 | 0.2732 | 0.3169 | 0.3054 | 0.3020 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-multilingual-cased", "model-index": [{"name": "privacy-masknig", "results": []}]}
|
taro-pudding/privacy-masknig
| null |
[
"transformers",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"base_model:distilbert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T19:11:19+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. -->
# mohsenfayyaz/Meta-Llama-3-8B-Instruct_medical_bios_5000_Lora_lr2e-6_1ep
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.7501
- eval_runtime: 4.8828
- eval_samples_per_second: 40.96
- eval_steps_per_second: 5.12
- epoch: 0.9984
- step: 78
## 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-06
- train_batch_size: 2
- eval_batch_size: 8
- seed: 0
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- PEFT 0.9.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.19.1
|
{"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "mohsenfayyaz/Meta-Llama-3-8B-Instruct_medical_bios_5000_Lora_lr2e-6_1ep", "results": []}]}
|
mohsenfayyaz/Meta-Llama-3-8B-Instruct_medical_bios_5000_Lora_lr2e-6_1ep
| null |
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null |
2024-04-24T19:11:24+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": []}
|
Weblet/phi-1.5.2
| null |
[
"transformers",
"safetensors",
"phi",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T19:11:27+00:00
|
object-detection
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detr
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.6795
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.9973 | 0.3195 | 100 | 3.0595 |
| 2.5938 | 0.6390 | 200 | 5.8527 |
| 2.0334 | 0.9585 | 300 | 5.6795 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/detr-resnet-50", "model-index": [{"name": "detr", "results": []}]}
|
JuudasMooses/detr
| null |
[
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T19:11:59+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": []}
|
rPucs/gemma-2b-relextract-nospecialtoken-webnlg
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T19:13:00+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 0.10.0
|
{"library_name": "peft", "base_model": "microsoft/Phi-3-mini-4k-instruct"}
|
nk555/phi-3-mini_lora
| null |
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"region:us"
] | null |
2024-04-24T19:13:31+00:00
|
text-generation
|
transformers
|
ProGen2-small finetuned on 7 protein families.
Bidirectional model trained on both N -> C and C -> N directions of protein sequences, specified by tokens "1" and "2" respectively.
See my [github repo](https://github.com/hugohrban/ProGen2-finetuning/tree/main) for more information.
Example usage:
```python
from transformers import AutoModelForCausalLM
from tokenizers import Tokenizer
import torch
import torch.nn.functional as F
# load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("hugohrban/progen2-small-mix7-bidi", trust_remote_code=True)
tokenizer = Tokenizer.from_pretrained("hugohrban/progen2-small-mix7-bidi")
tokenizer.no_padding()
# prepare input
prompt = "<|pf00125|>2FDDDVSAVKSTGVSK"
input_ids = torch.tensor(tokenizer.encode(prompt).ids).to(model.device)
# forward pass
logits = model(input_ids).logits
# print output probabilities
next_token_logits = logits[-1, :]
next_token_probs = F.softmax(next_token_logits, dim=-1)
for i in range(tokenizer.get_vocab_size(with_added_tokens=False)):
print(f"{tokenizer.id_to_token(i)}: {100 * next_token_probs[i].item():.2f} %")
```
|
{"license": "bsd-3-clause", "library_name": "transformers", "tags": ["protein"]}
|
hugohrban/progen2-small-mix7-bidi
| null |
[
"transformers",
"safetensors",
"progen",
"text-generation",
"protein",
"custom_code",
"license:bsd-3-clause",
"autotrain_compatible",
"region:us"
] | null |
2024-04-24T19:15:44+00:00
|
null | null |
{}
|
alikanakar/bert-base-turkish-cased-06-A-B
| null |
[
"region:us"
] | null |
2024-04-24T19:17:30+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. -->
# mohsenfayyaz/Meta-Llama-3-8B-Instruct_medical_bios_5000_Lora_lr2e-6_2ep
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.6141
- eval_runtime: 3.6441
- eval_samples_per_second: 54.883
- eval_steps_per_second: 6.86
- epoch: 1.9968
- step: 156
## 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-06
- train_batch_size: 2
- eval_batch_size: 8
- seed: 0
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- PEFT 0.9.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.19.1
|
{"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "mohsenfayyaz/Meta-Llama-3-8B-Instruct_medical_bios_5000_Lora_lr2e-6_2ep", "results": []}]}
|
mohsenfayyaz/Meta-Llama-3-8B-Instruct_medical_bios_5000_Lora_lr2e-6_2ep
| null |
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null |
2024-04-24T19:17:48+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. -->
# Llama-3-8B-sft-lora-ultrachat
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6619
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.6009 | 0.9655 | 14 | 1.6619 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "Llama-3-8B-sft-lora-ultrachat", "results": []}]}
|
APaul1/Llama-3-8B-sft-lora-ultrachat
| null |
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null |
2024-04-24T19:17:51+00:00
|
null | null |
{}
|
Anna15/Sn25
| null |
[
"region:us"
] | null |
2024-04-24T19:17:58+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": []}
|
Rimyy/MistralGSMDataV1
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T19:18:34+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": []}
|
CMU-AIR2/math-deepseek-FULL-ArithHardC11-FTMWP-FULL
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T19:20:32+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. -->
# 0.001_ablation_3iters_bs256_nodpo_iter_2
This model is a fine-tuned version of [ShenaoZ/0.001_ablation_3iters_bs256_nodpo_iter_1](https://huggingface.co/ShenaoZ/0.001_ablation_3iters_bs256_nodpo_iter_1) on the updated and the original datasets.
## 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": ["updated", "original"], "base_model": "ShenaoZ/0.001_ablation_3iters_bs256_nodpo_iter_1", "model-index": [{"name": "0.001_ablation_3iters_bs256_nodpo_iter_2", "results": []}]}
|
ShenaoZ/0.001_ablation_3iters_bs256_nodpo_iter_2
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZ/0.001_ablation_3iters_bs256_nodpo_iter_1",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T19:22:10+00:00
|
null |
peft
|
# Low-rank decomposition of [vicgalle/Roleplay-Llama-3-8B](https://huggingface.co/vicgalle/Roleplay-Llama-3-8B) using [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) as base
Created using [LoRD](https://github.com/thomasgauthier/LoRD)
Join my AI Discord: [rwitz](https://discord.gg/qbqjBEfkGw)
|
{"library_name": "peft", "base_model": "NousResearch/Meta-Llama-3-8B-Instruct"}
|
rwitz/Roleplay-Llama-3-8B-lora
| null |
[
"peft",
"safetensors",
"base_model:NousResearch/Meta-Llama-3-8B-Instruct",
"region:us"
] | null |
2024-04-24T19:22:39+00:00
|
text-generation
|
transformers
|
# Uploaded model
- **Developed by:** dbands
- **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", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}
|
dbands/llama-3-8b-php-instruct
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T19:22:43+00:00
|
null | null |
{"license": "openrail"}
|
Danikdsa/Yuri_SNSD
| null |
[
"license:openrail",
"region:us"
] | null |
2024-04-24T19:23:14+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 0.10.0
|
{"library_name": "peft", "base_model": "mistralai/Mistral-7B-Instruct-v0.2"}
|
Mouad918/sf_mistral_7b_instruct_adapter
| null |
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"region:us"
] | null |
2024-04-24T19:23:35+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. -->
# mohsenfayyaz/Meta-Llama-3-8B-Instruct_medical_bios_5000_Lora_lr2e-6_3ep
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.5388
- eval_runtime: 3.6252
- eval_samples_per_second: 55.169
- eval_steps_per_second: 6.896
- epoch: 2.9952
- step: 234
## 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-06
- train_batch_size: 2
- eval_batch_size: 8
- seed: 0
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- PEFT 0.9.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.19.1
|
{"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "mohsenfayyaz/Meta-Llama-3-8B-Instruct_medical_bios_5000_Lora_lr2e-6_3ep", "results": []}]}
|
mohsenfayyaz/Meta-Llama-3-8B-Instruct_medical_bios_5000_Lora_lr2e-6_3ep
| null |
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null |
2024-04-24T19:24:14+00:00
|
null | null |
{}
|
melitacruces/llama-2-7b-miniplatypus
| null |
[
"region:us"
] | null |
2024-04-24T19:24:32+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]
- **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 Data 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 Data 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
{"library_name": "peft", "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"}
|
bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned_Aleatoric_tiny_0.4_Seed102
| null |
[
"peft",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null |
2024-04-24T19:25:08+00:00
|
audio-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6691
- Accuracy: 0.86
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0761 | 1.0 | 113 | 1.9856 | 0.5 |
| 1.3376 | 2.0 | 226 | 1.3481 | 0.65 |
| 1.0645 | 3.0 | 339 | 1.0655 | 0.68 |
| 0.6495 | 4.0 | 452 | 0.8836 | 0.73 |
| 0.4802 | 5.0 | 565 | 0.7388 | 0.79 |
| 0.3875 | 6.0 | 678 | 0.6475 | 0.74 |
| 0.2788 | 7.0 | 791 | 0.5626 | 0.84 |
| 0.0623 | 8.0 | 904 | 0.6053 | 0.86 |
| 0.0848 | 9.0 | 1017 | 0.5784 | 0.85 |
| 0.033 | 10.0 | 1130 | 0.6307 | 0.86 |
| 0.0152 | 11.0 | 1243 | 0.6946 | 0.82 |
| 0.0098 | 12.0 | 1356 | 0.6419 | 0.87 |
| 0.0083 | 13.0 | 1469 | 0.6583 | 0.87 |
| 0.0081 | 14.0 | 1582 | 0.6584 | 0.87 |
| 0.0072 | 15.0 | 1695 | 0.6691 | 0.86 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["marsyas/gtzan"], "metrics": ["accuracy"], "base_model": "ntu-spml/distilhubert", "model-index": [{"name": "distilhubert-finetuned-gtzan", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "GTZAN", "type": "marsyas/gtzan", "config": "all", "split": "train", "args": "all"}, "metrics": [{"type": "accuracy", "value": 0.86, "name": "Accuracy"}]}]}]}
|
oyemade/distilhubert-finetuned-gtzan
| null |
[
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T19:25:31+00:00
|
null | null |
{}
|
ulusanarif/whisper_demo
| null |
[
"region:us"
] | null |
2024-04-24T19:26:04+00:00
|
|
null | null |
{"license": "gpl"}
|
Srinivasan2404/Llama-2-7b-hf
| null |
[
"license:gpl",
"region:us"
] | null |
2024-04-24T19:27:03+00:00
|
|
null | null |
{}
|
alikanakar/bert-base-turkish-cased-06-0-finetuned-squad
| null |
[
"region:us"
] | null |
2024-04-24T19:27:41+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": []}
|
LisiyLexa/mistral_7b_promptist_try2
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T19:27:43+00:00
|
text-generation
|
transformers
|
{}
|
Mouad918/sf_mistral_7b_instruct_merged
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T19:27:53+00:00
|
|
text-generation
|
transformers
|
Quantizations of https://huggingface.co/NexaAIDev/Octopus-v2
# From original readme
## Example Use Cases
You can run the model on a GPU using the following code.
```python
from transformers import AutoTokenizer, GemmaForCausalLM
import torch
import time
def inference(input_text):
start_time = time.time()
input_ids = tokenizer(input_text, return_tensors="pt").to(model.device)
input_length = input_ids["input_ids"].shape[1]
outputs = model.generate(
input_ids=input_ids["input_ids"],
max_length=1024,
do_sample=False)
generated_sequence = outputs[:, input_length:].tolist()
res = tokenizer.decode(generated_sequence[0])
end_time = time.time()
return {"output": res, "latency": end_time - start_time}
model_id = "NexaAIDev/Octopus-v2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = GemmaForCausalLM.from_pretrained(
model_id, torch_dtype=torch.bfloat16, device_map="auto"
)
input_text = "Take a selfie for me with front camera"
nexa_query = f"Below is the query from the users, please call the correct function and generate the parameters to call the function.\n\nQuery: {input_text} \n\nResponse:"
start_time = time.time()
print("nexa model result:\n", inference(nexa_query))
print("latency:", time.time() - start_time," s")
```
|
{"language": ["en"], "license": "other", "tags": ["transformers", "gguf", "imatrix", "NexaAIDev", "Octopus-v2"], "inference": false, "pipeline_tag": "text-generation"}
|
duyntnet/Octopus-v2-imatrix-GGUF
| null |
[
"transformers",
"gguf",
"imatrix",
"NexaAIDev",
"Octopus-v2",
"text-generation",
"en",
"license:other",
"region:us"
] | null |
2024-04-24T19:28:44+00:00
|
null | null |
{}
|
itay-nakash/model_4536eca16e
| null |
[
"safetensors",
"region:us"
] | null |
2024-04-24T19:29:36+00:00
|
|
text2text-generation
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bt5-small-bangla_lemma
This model is a fine-tuned version of [csebuetnlp/banglat5_small](https://huggingface.co/csebuetnlp/banglat5_small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8512
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.23 | 100 | 11.2822 |
| No log | 0.45 | 200 | 10.6983 |
| No log | 0.68 | 300 | 10.1553 |
| No log | 0.91 | 400 | 9.6258 |
| 10.0435 | 1.13 | 500 | 9.0264 |
| 10.0435 | 1.36 | 600 | 7.9661 |
| 10.0435 | 1.59 | 700 | 2.8449 |
| 10.0435 | 1.81 | 800 | 1.8512 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0
|
{"tags": ["generated_from_trainer"], "base_model": "csebuetnlp/banglat5_small", "model-index": [{"name": "bt5-small-bangla_lemma", "results": []}]}
|
arbitropy/bt5-small-bangla_lemma
| null |
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:csebuetnlp/banglat5_small",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T19:30:28+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. -->
# mohsenfayyaz/Meta-Llama-3-8B-Instruct_medical_bios_5000_Lora_lr2e-6_4ep
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.5358
- eval_runtime: 3.6275
- eval_samples_per_second: 55.135
- eval_steps_per_second: 6.892
- epoch: 3.9936
- step: 312
## 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-06
- train_batch_size: 2
- eval_batch_size: 8
- seed: 0
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- PEFT 0.9.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.19.1
|
{"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "mohsenfayyaz/Meta-Llama-3-8B-Instruct_medical_bios_5000_Lora_lr2e-6_4ep", "results": []}]}
|
mohsenfayyaz/Meta-Llama-3-8B-Instruct_medical_bios_5000_Lora_lr2e-6_4ep
| null |
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null |
2024-04-24T19:30:36+00:00
|
null | null |
{"license": "apache-2.0"}
|
tilakM/t5-base-fine_tuned
| null |
[
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T19:31:57+00:00
|
|
null | null |
{}
|
ckoozzzu/25_1
| null |
[
"region:us"
] | null |
2024-04-24T19:33:50+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": []}
|
sadpineapple/llama2-7b-chat-adapter
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T19:34:36+00:00
|
null | null |
{"license": "mit"}
|
leandro-driguez/swap-faces
| null |
[
"onnx",
"license:mit",
"region:us"
] | null |
2024-04-24T19:37:07+00:00
|
|
null |
peft
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mohsenfayyaz/Meta-Llama-3-8B-Instruct_medical_bios_5000_Lora_lr2e-6_5ep
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.5344
- eval_runtime: 3.6222
- eval_samples_per_second: 55.215
- eval_steps_per_second: 6.902
- epoch: 4.992
- step: 390
## 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-06
- train_batch_size: 2
- eval_batch_size: 8
- seed: 0
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- PEFT 0.9.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.19.1
|
{"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "mohsenfayyaz/Meta-Llama-3-8B-Instruct_medical_bios_5000_Lora_lr2e-6_5ep", "results": []}]}
|
mohsenfayyaz/Meta-Llama-3-8B-Instruct_medical_bios_5000_Lora_lr2e-6_5ep
| null |
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null |
2024-04-24T19:37:11+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. -->
# 0.001_ablation_4iters_bs128_nodpo_iter_1
This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the updated and the original datasets.
## 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: 2
- total_train_batch_size: 128
- 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": ["updated", "original"], "base_model": "HuggingFaceH4/mistral-7b-sft-beta", "model-index": [{"name": "0.001_ablation_4iters_bs128_nodpo_iter_1", "results": []}]}
|
ShenaoZhang/0.001_ablation_4iters_bs128_nodpo_iter_1
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:HuggingFaceH4/mistral-7b-sft-beta",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T19:37:15+00:00
|
null | null |
{}
|
shutianma/pubmed_filter1
| null |
[
"region:us"
] | null |
2024-04-24T19:37:16+00:00
|
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