zhaohq/GRPO-7B-ls-v1-fullepoch-hotpot
zhaohq/GRPO-7B-ls-v1-fullepoch-hotpot is a 7.6 billion parameter language model fine-tuned from Qwen/Qwen2.5-7B. It was trained using the GRPO method, as introduced in the DeepSeekMath paper, to enhance mathematical reasoning capabilities. This model is specifically optimized for tasks requiring advanced reasoning, leveraging its 32768 token context length. Its primary strength lies in complex problem-solving and logical inference, particularly in mathematical domains.
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Model Overview
This model, zhaohq/GRPO-7B-ls-v1-fullepoch-hotpot, is a 7.6 billion parameter language model built upon the Qwen/Qwen2.5-7B architecture. It distinguishes itself through its training methodology, utilizing GRPO (Gradient Regularized Policy Optimization), a technique detailed in the DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models paper. This approach aims to significantly improve the model's reasoning abilities, particularly in mathematical contexts.
Key Capabilities
- Enhanced Reasoning: Fine-tuned with GRPO to boost performance on complex reasoning tasks.
- Mathematical Proficiency: Optimized for mathematical problem-solving, drawing from the DeepSeekMath research.
- Large Context Window: Features a substantial 32768 token context length, enabling processing of extensive inputs for detailed analysis.
Training Details
The model was fine-tuned using the TRL library (version 0.16.0.dev0) and leverages Transformers 4.48.3. The training process is publicly logged and viewable on Weights & Biases.
Use Cases
- Mathematical Problem Solving: Ideal for applications requiring robust mathematical reasoning and calculation.
- Complex Logical Inference: Suitable for tasks that benefit from advanced logical deduction over long contexts.
- Research and Development: A strong candidate for further research into reasoning capabilities of large language models.