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
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- Outdated or non-region-specific medical advice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
import torch
from transformers import pipeline
model_id = "InferenceLab/MediLlama-3.2"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a helpful Medical assistant."},
{"role": "user", "content": "Hi! How are you?"},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
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
- Learning rate: 1e-5
Speeds, Sizes, Times
- Training time: ~12 hours on 4×A100 GPUs
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
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