Hriday75/qwen2.5-3b-cardio-chat

Qwen2.5-3B Cardiology Chat Assistant (SFT)

Model Details

Model Description

This is a highly specialized, supervised fine-tuned (SFT) LoRA adapter for the Qwen2.5-3B model. While the Phase 1 model learned dense medical science, this Phase 2 model was specifically instruction-tuned to communicate those facts with excellent bedside manner. It is designed to act as an empathetic, conversational cardiology expert.

• Developed by: Hriday75
• Model type: LoRA Adapter (PEFT)
• Language: English
• Finetuned from base model: unsloth/Qwen2.5-3B-bnb-4bit (via Phase 1 Cardiology LoRA)
• Training Stage: Phase 2 (Supervised Fine-Tuning / Instruction Tuned)

Uses

This model is intended to be used in a conversational interface (like a chat app). It is designed to answer patient questions about heart conditions, explain cardiology reports simply, and maintain an empathetic, helpful tone.

Training Details

Training Data

This adapter was trained on structured conversational datasets featuring thousands of back-and-forth patient/doctor interactions. The primary dataset used was ruslanmv/ai-medical-chatbot, which was formatted into the standard ChatML structure (System, User, Assistant).

Training Procedure

Trained using unsloth and the TRL (Transformer Reinforcement Learning) SFTTrainer for high-efficiency instruction tuning.

How to Get Started with the Model

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

# 1. Load the base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-3B-bnb-4bit")
tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen2.5-3B-bnb-4bit")

# 2. Attach this Cardiology Chat Adapter
model = PeftModel.from_pretrained(base_model, "Hriday75/qwen2.5-3b-cardio-chat")

# 3. Format your chat prompt
messages = [
    {"role": "system", "content": "You are a helpful, empathetic cardiology expert."},
    {"role": "user", "content": "Doctor, what does it mean if my ECG shows a prolonged QT interval?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
)