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Model: SeaFill2025/Qwen3-4B-SFT Source: Original Platform
2026-07-15 21:07:46 +08:00
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+---
+language:
+  - en
+  - zh
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - qwen3
+  - causal-lm
+  - supervised-fine-tuning
+  - math
+  - reasoning
+  - code
+  - science
+base_model: Qwen/Qwen3-4B-Base
+model-index:
+  - name: Qwen3-4B-SFT
+    results:
+      - task:
+          type: text-generation
+        dataset:
+          name: AIME 2024
+          type: aime2024
+        metrics:
+          - name: accuracy
+            type: accuracy
+            value: 20.8
+      - task:
+          type: text-generation
+        dataset:
+          name: AIME 2025
+          type: aime2025
+        metrics:
+          - name: accuracy
+            type: accuracy
+            value: 19.4
+      - task:
+          type: text-generation
+        dataset:
+          name: AMC 2023
+          type: amc2023
+        metrics:
+          - name: accuracy
+            type: accuracy
+            value: 58.0
+      - task:
+          type: text-generation
+        dataset:
+          name: GPQA-Diamond
+          type: gpqa_diamond
+        metrics:
+          - name: accuracy
+            type: accuracy
+            value: 29.1
+---
+
+## 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.
+
+| Dataset | Base (4B)† | Qwen3-4B-SFT (this model) | Improvement |
+| :--- | :---: | :---: | :---: |
+| **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% |
+
+† Base (4B) figures are taken from [ (arXiv:2602.10885)](https://arxiv.org/pdf/2602.10885).
+
+- Dataset card used for SFT: https://huggingface.co/datasets/96kevinli29/SFT-Dataset
+
+## Qwen3-style reasoning and instruction following
+
+Minimal pattern (illustrative):
+
+```text
+<|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:
+  ```json
+  {
+    "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 :
+
+```bibtex
+@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.}
+}
+```