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# Bootes-Qwen3\_Coder-Reasoning
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> Bootes-Qwen3\_Coder-Reasoning is a fine-tuned variant of the Qwen3-4B architecture, optimized for high-accuracy code reasoning and structured logical task completion. Trained on the CodeAlpaca\_20K dataset and additional curated programming corpora, this model is designed to perform technical coding, reasoning, and instruction-following tasks with lightweight computational requirements.
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* Occasionally over-justifies code when brevity is desired
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## References
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1. Qwen2.5 Technical Report – [https://arxiv.org/pdf/2412.15115](https://arxiv.org/pdf/2412.15115)
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2. CodeAlpaca Dataset – [https://github.com/sahil280114/codealpaca](https://github.com/sahil280114/codealpaca)
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3. YaRN: Context Window Extension for LLMs – [https://arxiv.org/pdf/2309.00071](https://arxiv.org/pdf/2309.00071)
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# Bootes-Qwen3\_Coder-Reasoning
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> Bootes-Qwen3\_Coder-Reasoning is a fine-tuned variant of the Qwen3-4B architecture, optimized for high-accuracy code reasoning and structured logical task completion. Trained on the CodeAlpaca\_20K dataset and additional curated programming corpora, this model is designed to perform technical coding, reasoning, and instruction-following tasks with lightweight computational requirements.
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* Occasionally over-justifies code when brevity is desired
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## References
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1. Qwen2.5 Technical Report – [https://arxiv.org/pdf/2412.15115](https://arxiv.org/pdf/2412.15115)
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2. CodeAlpaca Dataset – [https://github.com/sahil280114/codealpaca](https://github.com/sahil280114/codealpaca)
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3. YaRN: Context Window Extension for LLMs – [https://arxiv.org/pdf/2309.00071](https://arxiv.org/pdf/2309.00071)
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