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  # SimpleSD-4B-thinking
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- This model was produced using **Simple Self-Distillation (SimpleSD)**, 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=1.1, top_p=0.95, top_k=20
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  - **Evaluation sampling:** temperature=0.7, top_p=0.95, top_k=20
 
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  # SimpleSD-4B-thinking
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+ 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.
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  - **Self-distillation sampling:** temperature=1.1, top_p=0.95, top_k=20
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  - **Evaluation sampling:** temperature=0.7, top_p=0.95, top_k=20