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--- |
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license: llama3 |
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language: |
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- en |
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- ja |
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- zh |
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base_model: |
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- meta-llama/Meta-Llama-3-8B |
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pipeline_tag: text-generation |
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library_name: transformers |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** [More Information Needed] |
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- **Funded by [optional]:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [More Information Needed] |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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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. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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| | EN | | | | | |
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|--------------------------|-----------|-------|-------|---------|----------| |
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| Model | MedQA-4op | MedQA | MMLU | MedMCQA | PubMedQA | |
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| Gemma -7B | 52.92 | 47.56 | 69.74 | 48.67 | 73.44 | |
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| Llama2 -7B | 36.59 | 28.79 | 44.19 | 36.27 | 32.80 | |
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| Llama3 -8B | 58.52 | 52.76 | 70.02 | 55.25 | 75.05 | |
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| Swallow -8B -v0.1 | 47.87 | 45.66 | 65.87 | 49.99 | 64.39 | |
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| med alpaca -7B | 37.62 | 30.99 | 51.75 | 34.23 | 39.84 | |
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| Meditron -7B | 35.09 | 29.10 | 46.96 | 29.88 | 20.52 | |
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| Open BioLLM -8B | 40.14 | 50.08 | 73.15 | 56.23 | 65.39 | |
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| DISC -MedLLM | 37.46 | 32.89 | 50.00 | 36.96 | 33.20 | |
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| Apollo -7B | 54.97 | 49.68 | 68.73 | 53.48 | 75.86 | |
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| ELAINE -medLLM | 56.15 | 50.39 | 67.62 | 53.74 | 71.83 | |
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| ELAINE -medLLM -instruct | 58.36 | 55.84 | 72.79 | 54.48 | 73.24 | |
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| | ZH | | | JA | | | |
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|--------------------------|-----------|-------|--------|---------|---------|-------| |
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| Model | MedQA-4op | MedQA | CMExam | JJSIMQA | IgakuQA | DenQA | |
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| Gemma -7B | 48.90 | 44.55 | 38.60 | 27.61 | 35.80 | 25.14 | |
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| Llama2 -7B | 29.52 | 24.85 | 23.55 | 12.61 | 17.45 | 17.08 | |
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| Llama3 -8B | 51.42 | 44.64 | 39.41 | 28.91 | 33.20 | 23.47 | |
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| Swallow -8B -v0.1 | 47.35 | 40.84 | 35.94 | 37.83 | 45.15 | 29.03 | |
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| med alpaca -7B | 30.81 | 25.17 | 23.40 | 14.35 | 16.55 | 10.83 | |
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| Meditron -7B | 31.10 | 24.47 | 22.61 | 13.48 | 18.15 | 15.56 | |
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| Open BioLLM -8B | 50.37 | 42.59 | 24.59 | 20.87 | 31.50 | 14.44 | |
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| DISC -MedLLM | 47.18 | 46.13 | 41.57 | 23.26 | 27.15 | 23.47 | |
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| Apollo -7B | 65.19 | 60.98 | 51.40 | 25.00 | 37.40 | 24.72 | |
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| ELAINE -medLLM | 57.50 | 52.44 | 44.99 | 35.65 | 45.75 | 29.86 | |
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| ELAINE -medLLM -instruct | 61.59 | 55.71 | 47.19 | 35.22 | 46.35 | 32.36 | |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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## Usage |
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```python |
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import string, time |
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from vllm import LLM, SamplingParams |
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import torch |
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model_name = "kenyano/ELAINE_medLLM" |
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vllm_paralell = 1 |
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questions_ja = [ |
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"尿酸値の値はどこまでが正常値ですか？", |
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] |
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questions_en = [ |
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"What is the normal level of uric acid levels?" , |
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] |
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questions_zh = [ |
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"尿酸的正常水平是多少？", |
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] |
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llm = LLM(model=model_name, |
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trust_remote_code=True, |
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tensor_parallel_size=vllm_paralell, |
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dtype="half", |
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max_model_len=8192) |
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sampling_params = SamplingParams(temperature=0.2, top_p=0.8, max_tokens=200, min_tokens=50) |
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def generate(questions): |
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prompts = [f"Human: \n{question}\n\nAssistant: \n" for question in questions] |
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outputs = llm.generate(prompts,sampling_params) |
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for i, output in enumerate(outputs): |
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prompt = output.prompt |
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generated_text = output.outputs[0].text |
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print("-"*5, "prompt", "-"*5) |
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print(f'{prompt}') |
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print("-"*5, "generaated", "-"*5) |
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print(f'{generated_text}\n') |
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generate(questions_ja) |
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generate(questions_en) |
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generate(questions_zh) |
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```python |
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