初始化项目，由ModelHub XC社区提供模型

Model: apple/SimpleSD-4B-thinking
Source: Original Platform

README.md

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+---
+license: apple-amlr
+base_model:
+- Qwen/Qwen3-4B-Thinking-2507
+tags:
+- self-distillation
+- code-generation
+library_name: transformers
+---
+
+# SimpleSD-4B-thinking
+
+This model is an example of the **Simple Self-Distillation (SimpleSD)** method that improves code generation by fine-tuning a language model on its own sampled outputs—without rewards, verifiers, teacher models, or reinforcement learning. Please see the paper below for more information. This uses Qwen for initialization.
+
+- **Self-distillation sampling:** temperature=1.1, top_p=0.95, top_k=20
+- **Evaluation sampling:** temperature=0.7, top_p=0.95, top_k=20
+
+paper: https://arxiv.org/abs/2604.01193
+
+code: https://github.com/apple/ml-ssd
+
+
+## Notes
+- These are research checkpoints for reproducibility.
+- They are not optimized Qwen releases.
+- They don't represent a broader open-source model strategy.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("apple/SimpleSD-4B-thinking")
+tokenizer = AutoTokenizer.from_pretrained("apple/SimpleSD-4B-thinking")
+```
+
+## Method
+
+SimpleSD samples solutions from the base model using non-unit temperature and top-k/top-p truncation, then fine-tunes on those samples via standard supervised learning. Despite its simplicity, SimpleSD yields large gains on competitive programming benchmarks, with improvements concentrating on harder problems. The mechanism traces to resolving a *precision–exploration conflict*: SimpleSD reshapes token distributions in a context-dependent way so that a single global decoding configuration becomes far more effective at evaluation time.
+
+## Results
+
+LiveCodeBench (%)
+
+| Model | LCBv6 pass@1 | LCBv6 pass@5 | LCBv5 pass@1 | LCBv5 pass@5 |
+|---|---|---|---|---|
+| Qwen3-4B-Thinking-2507 (base) | 54.5 | 67.5 | 59.6 | 70.3 |
+| **+ SimpleSD (this model)** | **57.8** (+3.3) | **71.4** (+3.9) | **63.1** (+3.5) | **74.7** (+4.4) |
+
+## Paper
+
+[**Embarrassingly Simple Self-Distillation Improves Code Generation**](https://arxiv.org/abs/2604.01193)
+
+```bibtex
+@misc{zhang2026embarrassinglysimpleselfdistillationimproves,
+      title={Embarrassingly Simple Self-Distillation Improves Code Generation},
+      author={Ruixiang Zhang and Richard He Bai and Huangjie Zheng and Navdeep Jaitly and Ronan Collobert and Yizhe Zhang},
+      year={2026},
+      eprint={2604.01193},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2604.01193},
+}
+```
+
+
+## License
+
+This model is released under the [Apple Machine Learning Research Model License](https://huggingface.co/apple/SimpleSD-4B-thinking/blob/main/LICENSE).