Lansechen/Qwen-2.5-Base-7B-gen8-math3to5-ghpo-cold20-3Dhint-prompt1-epoch5-cosine0515-v2
Lansechen/Qwen-2.5-Base-7B-gen8-math3to5-ghpo-cold20-3Dhint-prompt1-epoch5-cosine0515-v2 is a 7.6 billion parameter language model fine-tuned from Qwen/Qwen2.5-7B. This model specializes in mathematical reasoning, having been trained on the chenggong1995/math3to5 dataset using the GRPO method. Its primary use case is to enhance performance on mathematical tasks, leveraging a training approach designed to push the limits of mathematical reasoning in open language models.
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Overview
This model, Lansechen/Qwen-2.5-Base-7B-gen8-math3to5-ghpo-cold20-3Dhint-prompt1-epoch5-cosine0515-v2, is a 7.6 billion parameter language model derived from the Qwen/Qwen2.5-7B base model. It has been specifically fine-tuned to excel in mathematical reasoning tasks.
Key Capabilities
- Enhanced Mathematical Reasoning: The model's core strength lies in its ability to process and solve mathematical problems, achieved through fine-tuning on the chenggong1995/math3to5 dataset.
- GRPO Training Method: It utilizes the GRPO (Gradient-based Reward Policy Optimization) method, as introduced in the paper "DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models" (arXiv:2402.03300), to optimize its mathematical performance.
- TRL Framework: The training procedure was implemented using the TRL (Transformer Reinforcement Learning) framework.
Good for
- Mathematical Problem Solving: Ideal for applications requiring strong mathematical reasoning capabilities.
- Research in Mathematical LLMs: Useful for researchers exploring advanced training techniques like GRPO for improving mathematical performance in large language models.