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MMOPD
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Model Details
Model Provider
MMOPD
Model Tree
Qwen/Qwen3-4B-Base
Input Modalities
Output Modalities
Supported Functionality
| base model | Qwen3-4B-Base |
| data | OpenThoughts3-1.2M (open-thoughts/OpenThoughts3-1.2M), all 1.2M rows, Qwen3 chat template with thinking |
| sequence length | 16,384 tokens, sequence packing (flatten, no cross-example attention) |
| epochs | 2 (4,390 optimizer steps in total, 2,195 per epoch) |
| optimizer | AdamW, peak LR 8e-5, 5% warmup, global batch 512 packed sequences (about 8.1M tokens per step) |
| precision | bf16 compute, ZeRO-2 data parallel (transformers 4.57 / trl 0.29 / DeepSpeed) on A100-80GB |
| final train loss | 0.868 |
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-OT3-2ep (this) | 66.3 | 56.3 | 58.3 | 51.7 | 51.0 | 27.7 |
| Qwen3-4B-OT3-1ep | 60.4 | 51.3 | 55.4 | 47.1 | 46.8 | 27.0 |
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-OT3-2ep (this) | 69.8 | 13.7 | 75.2 | 63.2 | 58.3 | 24.4 |
MMOPD/Qwen3-4B-OT3-{medical,law,finance,if} start from MMOPD/Qwen3-4B-OT3-2ep.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-OT3-2ep"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, dtype="auto", device_map="auto")messages = [{"role": "user", "content": "How many positive integers n < 1000 have the property that n^2 + 1 is divisible by 5?"}]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-OT3-2ep --max-model-len 40960 (the same sampling settings apply).