| --- |
| library_name: transformers |
| tags: |
| - unsloth |
| - trl |
| - sft |
| license: apache-2.0 |
| language: |
| - en |
| base_model: |
| - meta-llama/Llama-3.2-3B-Instruct |
| pipeline_tag: text-generation |
| metrics: |
| - accuracy |
| - bleu |
| - rouge |
| --- |
| |
| # Model Card for MediLlama-3.2 |
|
|
| A fine-tuned version of Meta's LLaMA 3.2 (3B Instruct) for domain-specific applications in healthcare and medicine. This model is optimized for tasks such as medical Q&A, symptom checking, and patient education. |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| This model is a domain-adapted version of LLaMA 3.2 3B Instruct. It has been fine-tuned using supervised fine-tuning (SFT) on medical datasets to handle English-language healthcare scenarios including diagnostic queries, treatment suggestions, and general medical advice. |
|
|
| - **Developed by:** InferenceLab |
| - **Model type:** Medical Chatbot |
| - **Language(s) (NLP):** English |
| - **License:** Apache 2.0 |
| - **Finetuned from model:** meta-llama/Llama-3.2-3B-Instruct |
|
|
| ### Model Sources |
|
|
| - **Repository:** [More Information Needed] |
| - **Paper [optional]:** [More Information Needed] |
| - **Demo [optional]:** [More Information Needed] |
|
|
| ## Uses |
|
|
| ### Direct Use |
|
|
| MediLlama-3.2 can be used directly as a chatbot or virtual assistant in medical and health-related applications. Ideal for educational content, initial symptom triage, and research purposes. |
|
|
| ### Downstream Use |
|
|
| Can be integrated into larger telehealth systems, clinical documentation tools, or diagnostic assistants after further task-specific fine-tuning. |
|
|
| ### Out-of-Scope Use |
|
|
| - Should not be used for real-time diagnosis or treatment decisions without expert validation. |
| - Not suitable for high-risk or life-threatening emergency response. |
| - Not trained on pediatric or highly specialized medical domains. |
|
|
| ## Bias, Risks, and Limitations |
|
|
| While the model is trained on medical data, it may still exhibit: |
| - Biases from source data |
| - Hallucinations or incorrect suggestions |
| - Outdated or non-region-specific medical advice |
|
|
| ### Recommendations |
|
|
| Users should validate outputs with certified medical professionals. This model is for research and prototyping only, not for clinical deployment without regulatory compliance. |
|
|
| ## How to Get Started with the Model |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model = AutoModelForCausalLM.from_pretrained("InferenceLab/MediLlama-3.2") |
| tokenizer = AutoTokenizer.from_pretrained("InferenceLab/MediLlama-3.2") |
| |
| input_text = "What are the symptoms of diabetes?" |
| inputs = tokenizer(input_text, return_tensors="pt") |
| outputs = model.generate(**inputs) |
| print(tokenizer.decode(outputs[0])) |
| ```` |
|
|
| ## Training Details |
|
|
| ### Training Data |
|
|
| Model trained using cleaned and preprocessed medical QA datasets, synthetic doctor-patient conversations, and publicly available health forums. Protected health information (PHI) was removed. |
|
|
| ### Training Procedure |
|
|
| Supervised fine-tuning (SFT) using TRL and Unsloth libraries. |
|
|
| #### Preprocessing |
|
|
| Tokenization using LLaMA tokenizer with special medical instruction formatting. |
|
|
| #### Training Hyperparameters |
|
|
| * **Training regime:** bf16 mixed precision |
| * **Epochs:** 3 |
| * **Batch size:** 64 |
| * **Learning rate:** 2e-5 |
|
|
| #### Speeds, Sizes, Times |
|
|
| * **Training time:** \~12 hours on 4×A100 GPUs |
| * **Final model size:** \~3.1B parameters |
|
|
| ## Evaluation |
|
|
| ### Testing Data, Factors & Metrics |
|
|
| #### Testing Data |
|
|
| Subset of unseen medical QA pairs, synthetic test cases, and MedQA-derived examples. |
|
|
| #### Factors |
|
|
| * Input prompt complexity |
| * Use of medical terminology |
| * Chat length |
|
|
| #### Metrics |
|
|
| * **Accuracy:** 81.3% |
| * **BLEU:** 34.5 |
| * **ROUGE-L:** 62.2 |
|
|
| ### Results |
|
|
| #### Summary |
|
|
| Model shows good generalization to unseen prompts and performs competitively for general medical dialogue. Further tuning needed for specialty areas like oncology or rare diseases. |
|
|
| ## Model Examination |
|
|
| Explainability tools like LLaMA-MedLens (if available) are suggested to interpret model decisions. |
|
|
| ## Environmental Impact |
|
|
| * **Hardware Type:** 4×NVIDIA A100 40GB |
| * **Hours used:** 12 |
| * **Cloud Provider:** AWS |
| * **Compute Region:** us-west-2 |
| * **Carbon Emitted:** \~35.8 kg CO2eq (estimated) |
|
|
| ## Technical Specifications |
|
|
| ### Model Architecture and Objective |
|
|
| * Based on Meta LLaMA 3.2 3B Instruct |
| * Decoder-only transformer |
| * Objective: Causal Language Modeling (CLM) with instruction fine-tuning |
|
|
| ### Compute Infrastructure |
|
|
| #### Hardware |
|
|
| * 4×NVIDIA A100 40GB |
|
|
| #### Software |
|
|
| * Python 3.10 |
| * Transformers (v4.40+) |
| * TRL |
| * Unsloth |
| * PyTorch 2.1 |
|
|
| ## Citation |
|
|
| **BibTeX:** |
|
|
| ```bibtex |
| @misc{medillama_2025, |
| author = {InferenceLab}, |
| title = {MediLlama-3.2: A Medical Chatbot Fine-Tuned from LLaMA 3.2}, |
| year = {2025}, |
| publisher = {HuggingFace}, |
| howpublished = {\url{https://huggingface.co/InferenceLab/MediLlama-3.2}}, |
| } |
| ``` |
|
|
| **APA:** |
|
|
| InferenceLab. (2025). *MediLlama-3.2: A Medical Chatbot Fine-Tuned from LLaMA 3.2*. Hugging Face. [https://huggingface.co/InferenceLab/MediLlama-3.2](https://huggingface.co/InferenceLab/MediLlama-3.2) |
|
|
| ## Glossary |
|
|
| * **SFT**: Supervised Fine-Tuning |
| * **BLEU**: Bilingual Evaluation Understudy |
| * **ROUGE**: Recall-Oriented Understudy for Gisting Evaluation |
|
|
| ## More Information |
|
|
| For collaborations, deployment help, or fine-tuning extensions, please contact the developers. |
|
|
| ## Model Card Authors |
|
|
| * InferenceLab Team |
|
|
| ## Model Card Contact |
|
|
| * [contact@inferencelab.ai](mailto:contact@inferencelab.ai) |
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