Hao0oWang/Qwen2.5-Math-7B-16k-think
Hao0oWang/Qwen2.5-Math-7B-16k-think is a 7.6 billion parameter language model based on Qwen2.5-Math-7B-Base, developed by Hao Wang et al. It is specifically fine-tuned for long context reasoning, particularly in mathematical tasks, and features an extended context window of 32768 tokens. This model incorporates modifications to its chat template and rope_theta for enhanced performance in complex reasoning scenarios. Its primary application is in advanced mathematical problem-solving and reasoning tasks requiring extensive contextual understanding.
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Model Overview
Hao0oWang/Qwen2.5-Math-7B-16k-think is a 7.6 billion parameter model derived from the Qwen2.5-Math-7B-Base, developed by Hao Wang and his team. This model is a key component of the CurioSFT framework, which focuses on entropy-preserving supervised fine-tuning for large reasoning models, as detailed in their research paper, "Learning While Staying Curious: Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models."
Key Enhancements
This model distinguishes itself through several critical modifications aimed at improving its reasoning capabilities, especially for long-context scenarios:
- Extended Context Window: The model's context window has been significantly expanded to 32768 tokens, enabling it to process and understand much longer inputs.
- Optimized for Reasoning: Specific adjustments to the
chat_templateandrope_thetahave been implemented to enhance its performance in complex reasoning tasks. - Mathematical Focus: Built upon a math-specific base model, it is particularly adept at handling mathematical problems and logical deductions.
Ideal Use Cases
This model is particularly well-suited for applications requiring:
- Advanced Mathematical Problem Solving: Excelling in tasks that demand deep mathematical understanding and computation.
- Long-Context Reasoning: Effectively processing and reasoning over extensive textual information, beneficial for complex analytical tasks.
- Research in Reasoning Models: Serving as a foundational model for further research and development in large reasoning models, especially within the context of adaptive self-distillation techniques.