SeaFill2025/Qwen3-4B-SFT

Qwen3-4B-SFT:

Qwen3-4B-SFT is a reasoning-focused model derived from Qwen3-4B-Base via full-parameter fine-tuning on the verl framework.

There is a notable shortage of reproducible 'warm-start' SFT bases in open-source practice, this model bridges the gap between base models and reinforcement learning models. Optimally aligned for Chain-of-Thought (CoT) and instruction following, it serves as a robust warm-start for Reinforcement Learning.

Benchmark Snapshot

• Compared to the Base (4B) model, Qwen3-4B-SFT demonstrates significant performance improvements in reasoning and mathematics. The reported figures represent the Pass@1 accuracy, calculated as the average of dataset-level accuracies across 16 independent runs.

Dataset	Base (4B)	Qwen3-4B-SFT (this model)	Improvement (Absolute)
AIME 2024	11.25%	20.8%	+9.55%
AIME 2025	6.46%	19.4%	+12.94%
AMC 2023	31.09%	58.0%	+26.91%
GPQA-Diamond	7.77%	29.1%	+21.33%

• Dataset card used for SFT: https://huggingface.co/datasets/96kevinli29/SFT-Dataset

Qwen3-style reasoning and instruction following

Minimal pattern (illustrative):

<|im_start|>user
… Among options A–D, which is correct? Reason step by step and put the final letter in \boxed{}.
<|im_end|>

<|im_start|>assistant
<think>
Compare A vs B vs C vs D against the stem; eliminate …; D remains consistent with …
</think>
Step-by-step: … (short derivation in the visible channel)
Final answer: \boxed{D}
<|im_end|>

Use a large enough max_new_tokens on hard math so both the reasoning block and the visible \boxed{…} line fit before generation stops.

Configuration Notes

• Template: Trained with the Qwen chat template; learns to end responses with <|im_end|> (151645).
• Suggested Configuration:

  {
    "eos_token_id": 151645
  }
  

You may adjust settings according to your training or deployment needs.

Training Infrastructure

• Cluster: MeluXina Supercomputer (LuxProvide)
• Node Config: 4 NVIDIA-A100 GPUs per node.
• Final SFT Run: 12 Node-hours (16× A100 for 3 hours)
• Total R&D Investment: ~700 Node-hours (Includes data ablation, hyperparameter sweeps, and extensive benchmark evaluation.)

Project Links

• Training code repository: https://github.com/96kevinli29/base-model-sft-verl

Limitations

• Not optimized for factual correctness in all domains
• May still produce hallucinations or unsafe outputs
• Performance is sensitive to prompt style and decoding settings

Citation

If you use this model, please cite this checkpoint, bibTeX for this release :

@misc{qwen3-4b-sft-2026,
  title        = {{Qwen3-4B-SFT}: Supervised Fine-Tuned {Qwen3}-4B for Reasoning},
  author       = {Hongyang Li, Xiao Li and {Sea-Fill Community}},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/SeaFill2025/Qwen3-4B-SFT}},
  note         = {Checkpoint trained with verl; warm-start for pre-RL alignment research. Maintained by Sea-Fill Community.}
}