Qwen3.5-9B-Medical-Reasoning
Granite-4.1-3B-Medical-Reasoning is a fine-tuned 3-billion parameter model optimized for complex clinical reasoning and medical question-answering. By leveraging chain-of-thought (CoT) fine-tuning on medical reasoning traces, the model generates explicit step-by-step diagnostic reasoning inside <think> tags prior to delivering a final medical conclusion.
Key Highlights
- Reasoning Capabilities: Fine-tuned using step-by-step medical reasoning datasets to prevent superficial guessing and encourage clinical chain-of-thought.
Evaluation Results
TBC.
Training Details
- Base Model:
ibm-granite/granite-4.1-3b - Fine-Tuning Dataset:
FreedomIntelligence/medical-o1-reasoning-SFT(Subset:-EM, Split:Train) - Fine-Tuning Framework: Unsloth (LoRA fine-tuning)
- Hardware: 1x NVIDIA A100 (40GB VRAM)
- Prompt Format:
<|start_of_role|>(user or system)<|end_of_role>,<|end_of_text|>)
How to Use
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
model_path = "ibm-granite/granite-4.1-3b"
tokenizer = AutoTokenizer.from_pretrained(model_path)
# drop device_map if running on CPU
model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
model.eval()
# change input text as desired
chat = [
{ "role": "user", "content": "A 45-year-old male presents with sudden chest pain, diaphoresis, and radiation to the left jaw. What is the most likely diagnosis?" },
]
chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
# tokenize the text
input_tokens = tokenizer(chat, return_tensors="pt").to(device)
# generate output tokens
output = model.generate(**input_tokens,
max_new_tokens=100)
# decode output tokens into text
output = tokenizer.batch_decode(output)
# print output
print(output[0])
Intended Use & Limitations
Intended Use
- Medical research and evaluation of chain-of-thought capabilities in compact LLMs.
- AI-assisted clinical reasoning benchmark comparison and analysis.
Medical Disclaimer
IMPORTANT: This model is built for research and evaluation purposes only. It is not a certified medical device and should never be used for direct patient diagnosis, treatment advice, or real-world clinical decision-making. Always consult a qualified medical professional for health-related decisions.