Tanmay3004/llama_medical

LLaMA 3.2 - 3B Medical Fine-Tuned Model

Model Description

This model is a fine-tuned version of the LLaMA 3.2 - 3B large language model, optimized for medical question-answering tasks. The fine-tuning was conducted using Low-Rank Adaptation (LoRA) on a Medical QA dataset.

Fine-Tuning Approach

LoRA allows for efficient parameter tuning by introducing trainable low-rank matrices that adapt the model weights without modifying the pre-trained parameters extensively.

LoRA Rank Selection

• The model was trained with LoRA rank 4.
• Higher ranks allow for more detailed adaptations but increase computational overhead.
• The initial training was performed with a lower rank for efficiency, with plans to increase rank in future iterations based on performance evaluations.

Training Configuration

• Base Model: LLaMA 3.2 - 3B
• Dataset: Medical Question-Answer dataset
• Training Framework: Hugging Face transformers
• Optimizer: AdamW with fp16 for mixed precision training
• Batch Size: 1 (with gradient accumulation of 4)
• Epochs: 1
• Learning Rate: 2e-5
• Evaluation & Logging: Weights & Biases (W&B) integration
• Model Sharing: Pushed to Hugging Face Model Hub

Performance & Metrics

The training logs (attached below) indicate:

• Loss Reduction: Training loss gradually decreases, indicating convergence.
• Learning Rate Decay: Follows a linear decay schedule.
• Gradient Norm Stability: Controlled within acceptable limits.
• Global Steps & Epoch Tracking: The model completes training within the expected steps.

Usage

To load and use the model:

import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

os.environ["HF_KEY"] = "enter key to access your hugging face account in order to import the model"

model_name = "Tanmay3004/llama_medical"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=os.getenv("HF_KEY"))
model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=os.getenv("HF_KEY"))

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

template = "Question:\n{question}\n\nAnswer:\n{answer}"

prompt = template.format(
    question="What are the treatments for Paget's Disease of Bone?",
    answer=""
)

def generate_medical_response(prompt):
    inputs = tokenizer(prompt, return_tensors="pt").to(device)  # Move inputs to the same device
    outputs = model.generate(**inputs, max_new_tokens=100)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generate_medical_response(prompt))

Future Improvements

• Increase LoRA rank for improved adaptation.
• Further dataset augmentation for broader generalization.
• Optimize inference for real-time applications in medical AI systems.