Shifa-4B-SFT-LoRA

Shifa-4B-SFT-LoRA

Shifa-4B is a medical reasoning model built on Qwen3.5-4B. This repository contains the Stage-1 QLoRA adapters from supervised fine-tuning on medical chain-of-thought data.

The model learns to reason step-by-step inside <think>...</think> blocks before producing a final answer — leveraging Qwen3.5's native hybrid-thinking architecture.

This is the LoRA adapter only. For the ready-to-use merged model, see naazimsnh02/Shifa-4B-SFT.

Benchmark Results (Stage-1 SFT)

MedGemma targets are from the google/medgemma-4b-it model card. Evaluated on 500 samples per benchmark using greedy decoding.

Training Details

Data

~395K examples blended from three medical reasoning datasets plus a general-instruction mix to prevent catastrophic forgetting:

Hyperparameters

Framework

• Unsloth 2026.6.7
• TRL 0.22.2 (SFTTrainer)
• Transformers 5.12.1
• PyTorch 2.8.0+cu128

Usage

from unsloth import FastModel

model, tokenizer = FastModel.from_pretrained(
    model_name="naazimsnh02/Shifa-4B-SFT-LoRA",
    max_seq_length=4096,
    load_in_4bit=True,
)
FastModel.for_inference(model)

messages = [
    {"role": "user", "content": "A 45-year-old male presents with sudden chest pain radiating to the left arm. What is the most likely diagnosis and initial workup?"},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_new_tokens=1024, do_sample=False)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Roadmap

• Stage-2 (GRPO): Reinforcement learning with verifiable MCQ rewards on MedQA + MedMCQA to strengthen overall medical reasoning accuracy.

Limitations

• This is a research model. Not intended for clinical use or medical decision-making.
• Performance on PubMedQA is limited; literature-based reasoning requires further tuning.
• The model inherits biases and limitations from its base model and training data.

License

Apache 2.0 — same as the base model (Qwen3.5-4B).