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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 | 56,123 verified teacher traces on 28,615 prompts (CaseHOLD train 54,030 rows + bar-exam MCQ 2,093 rows), 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 2) |
| recipe | SFT, 2 epochs, LR 1e-5 (5% warmup), max length 20,480, flatten packing, bf16 + ZeRO-2; final checkpoint (step 104) |
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-law (this) | – | – | – | 73.9 | – | – |
| Qwen3-4B-OT3-2ep (student init) | 69.8 | 13.7 | 75.2 | 63.2 | 58.3 | 24.4 |
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-law (this) | 45.4 | 42.9 | 40.8 | 37.0 | 48.2 | 29.0 |
| 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-law"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, dtype="auto", device_map="auto")messages = [{"role": "user", "content": "Which of the following holdings best completes the citing text? (CaseHOLD-style question with five candidate holdings)"}]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-law --max-model-len 40960 (the same sampling settings apply).
| 27.7 |