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--- |
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license: llama3 |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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- medical |
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- Healthcare & Lifesciences |
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- BioMed |
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base_model: meta-llama/Meta-Llama-3-8B-Instruct |
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thumbnail: https://collaiborate.com/logo/logo-blue-bg-1.png |
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model-index: |
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- name: Bio-Medical-Llama-3.1-8B |
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results: [] |
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datasets: |
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- collaiborateorg/BioMedData |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Bio-Medical |
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This model is a fine-tuned version of https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct on our custom "BioMedData" dataset. |
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## Model details |
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Model Name: Bio-Medical-Llama-3.1-8B |
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Base Model: Llama-3.1-8B-Instruct |
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Parameter Count: 8 billion |
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Training Data: Custom high-quality biomedical dataset |
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Number of Entries in Dataset: 500,000+ |
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Dataset Composition: The dataset comprises both synthetic and manually curated samples, ensuring a diverse and comprehensive coverage of biomedical knowledge. |
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## Model description |
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Bio-Medical-Llama-3.1-8B model is a specialized large language model designed for biomedical applications. It is finetuned from the meta-llama/Meta-Llama-3.1-8B-Instruct model using a custom dataset containing over 500,000 diverse entries. These entries include a mix of synthetic and manually curated data, ensuring high quality and broad coverage of biomedical topics. |
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The model is trained to understand and generate text related to various biomedical fields, making it a valuable tool for researchers, clinicians, and other professionals in the biomedical domain. |
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## Evaluation Metrics |
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Bio-Medical-Llama-3.1-8B model outperforms many of the leading LLMs and find below its metrics evaluated using the Eleuther AI Language Model Evaluation Harness framework against the tasks medmcqa, medqa_4options, mmlu_anatomy, mmlu_clinical_knowledge, mmlu_college_biology, mmlu_college_medicine, mmlu_medical_genetics, mmlu_professional_medicine and pubmedqa. |
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## Intended uses & limitations |
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Bio-Medical-Llama-3.1-8B model is intended for a wide range of applications within the biomedical field, including: |
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1. Research Support: Assisting researchers in literature review and data extraction from biomedical texts. |
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2. Clinical Decision Support: Providing information to support clinical decision-making processes. |
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3. Educational Tool: Serving as a resource for medical students and professionals seeking to expand their knowledge base. |
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## Limitations and Ethical Considerations |
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While Bio-Medical-Llama-3.1-8B model performs well in various biomedical NLP tasks, users should be aware of the following limitations: |
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Biases: The model may inherit biases present in the training data. Efforts have been made to curate a balanced dataset, but some biases may persist. |
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Accuracy: The model's responses are based on patterns in the data it has seen and may not always be accurate or up-to-date. Users should verify critical information from reliable sources. |
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Ethical Use: The model should be used responsibly, particularly in clinical settings where the stakes are high. It should complement, not replace, professional judgment and expertise. |
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## How to use |
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import transformers |
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import torch |
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model_id = "ContactDoctor/Bio-Medical-Llama-3.1-8B" |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model_id, |
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model_kwargs={"torch_dtype": torch.bfloat16}, |
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device_map="auto", |
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) |
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messages = [ |
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{"role": "system", "content": "You are an expert trained on healthcare and biomedical domain!"}, |
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{"role": "user", "content": "I'm a 35-year-old male and for the past few months, I've been experiencing fatigue, increased sensitivity to cold, and dry, itchy skin. What is the diagnosis here?"}, |
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] |
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prompt = pipeline.tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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terminators = [ |
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pipeline.tokenizer.eos_token_id, |
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pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>") |
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] |
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outputs = pipeline( |
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prompt, |
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max_new_tokens=256, |
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eos_token_id=terminators, |
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do_sample=True, |
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temperature=0.6, |
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top_p=0.9, |
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) |
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print(outputs[0]["generated_text"][len(prompt):]) |
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### Contact Information |
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For further information, inquiries, or issues related to Biomed-LLM, please contact: |
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Email: [email protected] |
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Website: https://www.contactdoctor.in |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 12 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.03 |
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- training_steps: 2000 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- PEFT 0.11.0 |
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- Transformers 4.40.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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### Citation |
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If you use Bio-Medical LLM in your research or applications, please cite it as follows: |
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@misc{ContactDoctor_Bio-Medical-Llama-3.1-8B, |
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author = ContactDoctor, |
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title = {Bio-Medical: A High-Performance Biomedical Language Model}, |
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year = {2024}, |
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howpublished = {https://huggingface.co/ContactDoctor/Bio-Medical-Llama-3.1-8B}, |
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} |
