Alelcv27/Qwen2.5-7B-Math-CoT
Alelcv27/Qwen2.5-7B-Math-CoT is a 7.6 billion parameter Qwen2.5 model, fine-tuned by Alelcv27, specifically optimized for mathematical reasoning and Chain-of-Thought (CoT) tasks. This model leverages the Qwen2.5 architecture and was trained using Unsloth for accelerated fine-tuning. Its primary strength lies in handling complex mathematical problems and generating structured reasoning steps.
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Model Overview
Alelcv27/Qwen2.5-7B-Math-CoT is a 7.6 billion parameter language model, fine-tuned by Alelcv27, based on the Qwen2.5 architecture. This model was specifically developed to enhance performance in mathematical reasoning and Chain-of-Thought (CoT) tasks. It was fine-tuned using Unsloth and Huggingface's TRL library, which enabled a 2x faster training process.
Key Capabilities
- Mathematical Reasoning: Optimized for solving mathematical problems and generating logical steps.
- Chain-of-Thought (CoT): Designed to produce structured and coherent reasoning processes.
- Efficient Fine-tuning: Benefits from accelerated training via Unsloth, indicating potential for further efficient adaptation.
Good For
- Applications requiring robust mathematical problem-solving.
- Tasks that benefit from explicit, step-by-step reasoning.
- Developers looking for a Qwen2.5 variant with a focus on analytical and logical capabilities.