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MMOPD/Qwen3-4B-OT3-medical

sourceHugging Faceapache-2.0updated 13d agoView on Hugging Face

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Qwen3-4B-OT3-medical (medical teacher, SFT)

Qwen3-4B-OT3-medical is the medical domain teacher of the MMOPD study: MMOPD/Qwen3-4B-OT3-2ep further fine-tuned (supervised) on verified long-chain-of-thought answers that a much stronger model (Qwen3.6-35B-A3B, MedQA 94.6%) produced for MedQA training questions. Only traces whose final answer matched the gold letter were kept (rejection sampling), at most 3 distinct traces per question. It raises MedQA from 69.8 to 80.7 while keeping the general Qwen3 thinking format.

Training


init	`MMOPD/Qwen3-4B-OT3-2ep`
data	40,853 verified teacher traces on MedQA-USMLE training questions in 4-option (20,360) and 5-option (20,493) form (15,427 prompt variants), 8-gram decontaminated against the evaluation sets
trace teacher	Qwen3.6-35B-A3B, 8 samples per question at temperature 1.0, keep the correct ones (up to 3, shortest first), thinking part capped at 16k characters
recipe	SFT, 4 epochs, LR 1e-5 (5% warmup), max length 8,192, flatten packing, bf16 + ZeRO-2; final checkpoint (step 3,368)

Evaluation

Domain benchmarks (temperature 1.0, top-p 1.0, long generation budget; accuracy in %):

Model	MedQA	MedXpertQA	PubMedQA	CaseHOLD	FinQA	TAT-QA (EM)
Qwen3-4B-OT3-medical (this)	80.7	22.3	77.2	59.8	54.0	–
Qwen3-4B-OT3-2ep (student init)	69.8	13.7	75.2	63.2	58.3	24.4

PubMedQA = accuracy (macro-F1 57.4 vs 56.6 for the init). General benchmarks (Qwen3 thinking preset: temperature 0.6, top-p 0.95, top-k 20; 32,768 max new tokens; AIME = avg@8, LiveCodeBench v6 / IFEval / IFBench = 1 sample; scores in %):

Model	AIME24	AIME25	AIME26	LiveCodeBench v6	IFEval	IFBench
Qwen3-4B-OT3-medical (this)	52.9	45.0	40.0	39.9	41.2	27.0
Qwen3-4B-OT3-2ep (student init)	66.3	56.3	58.3	51.7	51.0	27.7

Notes

—Weights are stored in bfloat16. Apache-2.0.
—Domain scores are measured at temperature 1.0 because that is the sampling regime in which these models serve as distillation teachers; general benchmarks use the Qwen3 thinking preset.
—Part of the MMOPD model family together with MMOPD/Qwen3-4B-OT3-{1ep,2ep}, MMOPD/Qwen3-1.7B-OT3-{1ep,2ep} and the other domain teachers MMOPD/Qwen3-4B-OT3-{medical,law,finance,if}.

How to use

The models keep the Qwen3 chat template and thinking format (<think> ... </think> before the answer). Use enable_thinking=True and sampling (not greedy); the evaluations below used a 32k-token generation budget.

python

from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "MMOPD/Qwen3-4B-OT3-medical"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype="auto", device_map="auto")
messages = [{"role": "user", "content": "A 45-year-old man presents with sudden severe chest pain radiating to the back and a blood pressure difference between arms. What is the most likely diagnosis?"}]
text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=True)
out = model.generate(**tok(text, return_tensors="pt").to(model.device), max_new_tokens=32768,
                     do_sample=True, temperature=0.6, top_p=0.95, top_k=20)
print(tok.decode(out[0], skip_special_tokens=True))

vLLM: vllm serve MMOPD/Qwen3-4B-OT3-medical --max-model-len 40960 (the same sampling settings apply).