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MMOPD
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Model Details
Model Provider
MMOPD
Model Tree
MMOPD/Qwen3-4B-OT3-2ep
Input Modalities
Output Modalities
Supported Functionality
| 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) |
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 |
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}.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, AutoTokenizerrepo = "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).
| 27.7 |