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by richardbaihe - opened
README.md
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library_name: transformers
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#
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This model was produced using **Simple Self-Distillation (SSD)**, a method that improves code generation by fine-tuning a language model on its own sampled outputs—without rewards, verifiers, teacher models, or reinforcement learning.
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- **Base model:** [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
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- **Variant:** instruct
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- **Self-distillation sampling:** temperature=1.6, top_p=0.8, top_k=20
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- **Evaluation sampling:** temperature=1.1, top_p=0.8, top_k=20
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- They are not optimized Qwen releases.
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- They don't represent a broader open-source model strategy.
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## Method
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SSD 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, SSD yields large gains on competitive programming benchmarks, with improvements concentrating on harder problems. The mechanism traces to resolving a *precision–exploration conflict*: SSD reshapes token distributions in a context-dependent way so that a single global decoding configuration becomes far more effective at evaluation time.
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## Paper
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**Embarrassingly Simple Self-Distillation Improves Code Generation**
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```
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## License
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This model is released under the [Apple Machine Learning Research Model License](https://huggingface.co/apple/
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library_name: transformers
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# SimpleSD-4B-instruct
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This model was produced using **Simple Self-Distillation (SSD)**, a method that improves code generation by fine-tuning a language model on its own sampled outputs—without rewards, verifiers, teacher models, or reinforcement learning.
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- **Self-distillation sampling:** temperature=1.6, top_p=0.8, top_k=20
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- **Evaluation sampling:** temperature=1.1, top_p=0.8, top_k=20
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- They are not optimized Qwen releases.
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- They don't represent a broader open-source model strategy.
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("apple/SimpleSD-4B-instruct")
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tokenizer = AutoTokenizer.from_pretrained("apple/SimpleSD-4B-instruct")
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```
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## Method
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SSD 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, SSD yields large gains on competitive programming benchmarks, with improvements concentrating on harder problems. The mechanism traces to resolving a *precision–exploration conflict*: SSD reshapes token distributions in a context-dependent way so that a single global decoding configuration becomes far more effective at evaluation time.
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## Paper
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[**Embarrassingly Simple Self-Distillation Improves Code Generation**](https://arxiv.org/abs/2604.01193)
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```bibtex
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@misc{zhang2026embarrassinglysimpleselfdistillationimproves,
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title={Embarrassingly Simple Self-Distillation Improves Code Generation},
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author={Ruixiang Zhang and Richard He Bai and Huangjie Zheng and Navdeep Jaitly and Ronan Collobert and Yizhe Zhang},
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year={2026},
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eprint={2604.01193},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2604.01193},
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}
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```
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## License
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This model is released under the [Apple Machine Learning Research Model License](https://huggingface.co/apple/SimpleSD-4B-instruct/blob/main/LICENSE).
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