zhaohq/GSPO-7B-v5-main
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:May 15, 2026Architecture:Transformer Warm
The zhaohq/GSPO-7B-v5-main is a 7.6 billion parameter language model fine-tuned from Qwen/Qwen2.5-Math-7B. This model was trained using the GRPO method, as introduced in the DeepSeekMath paper, to enhance mathematical reasoning capabilities. It is designed for tasks requiring advanced mathematical problem-solving and logical deduction. The model leverages a 32768 token context length for complex inputs.
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Model Overview
The zhaohq/GSPO-7B-v5-main is a 7.6 billion parameter language model, fine-tuned from the Qwen/Qwen2.5-Math-7B base model. It utilizes a substantial 32768 token context length, making it suitable for processing lengthy and complex inputs.
Key Capabilities
- Enhanced Mathematical Reasoning: This model was specifically trained using the GRPO (Gradient-based Reward Policy Optimization) method, a technique detailed in the DeepSeekMath paper. This training approach aims to significantly improve the model's ability to handle mathematical problems and logical reasoning tasks.
- Fine-tuned with TRL: The model's fine-tuning process leveraged the TRL library, a framework for Transformer Reinforcement Learning, indicating a focus on optimizing performance through advanced training methodologies.
Good For
- Applications requiring strong mathematical problem-solving.
- Tasks that benefit from advanced logical reasoning capabilities.
- Research and development in areas involving complex numerical or symbolic manipulation.