A fine-tuned Llama 3 model specialized for healthcare scenarios across four medical roles: GP, nurse, midwife, and obstetrician. This model provides professional medical information in a conversational format.

Model Specifications

Base Model: meta-llama/Llama-3.2-3B-Instruct
Fine-Tuning Method: Low-Rank Adaptation (LoRA)
Training Data: 1,581 examples across 4 healthcare roles
- GP: 396 examples
- Midwife: 394 examples
- Nurse: 395 examples
- Obstetrician: 396 examples
Training Configuration:
- LoRA rank (r): 8
- LoRA alpha: 24
- Dropout: 0.103
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Usage

This model is fine-tuned for healthcare applications, covering four medical roles:

General Practitioner (GP)
Nurse
Midwife
Obstetrician

Python Code Example

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model_id = "MedLlama-3-Healthcare"  # Replace with your Hugging Face model ID
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    torch_dtype=torch.float16, 
    device_map="auto"
)

# Set up role context - choose one: "gp", "nurse", "midwife", "obstetrician"
role = "gp"

# Format prompt with role context
prompt = f"[INST] I want you to act as a {role}. What can you tell me about diabetes management? [/INST]"

# Tokenize and generate
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_length=1024,
    temperature=0.7,
    top_p=0.9,
    pad_token_id=tokenizer.eos_token_id
)

# Decode and print response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Prompt Format

For best results, use prompts in the following format:

[INST] I want you to act as a {role}. {your medical question or scenario} [/INST]

Limitations and Ethical Considerations

This model is intended for research and development purposes only
The model is not a replacement for professional medical advice
Always consult with qualified healthcare professionals for medical decisions
The model may produce inaccurate information and should not be used in clinical settings
The model was fine-tuned on limited data and may not cover all healthcare scenarios

iamrsps/MedLlama-3-Healthcare

MedLlama-3-Healthcare

Model Specifications

Usage

Python Code Example

Prompt Format

Limitations and Ethical Considerations

iamrsps/
MedLlama-3-Healthcare