luckeciano/Qwen-2.5-7B-GRPO-Adam-HessianMaskToken-1e-2-Symmetric-v2_2319
The luckeciano/Qwen-2.5-7B-GRPO-Adam-HessianMaskToken-1e-2-Symmetric-v2_2319 is a 7.6 billion parameter language model, fine-tuned from Qwen/Qwen2.5-Math-7B. It was trained using the GRPO method on the DigitalLearningGmbH/MATH-lighteval dataset, specializing it for mathematical reasoning tasks. This model is designed to enhance performance in complex mathematical problem-solving, leveraging its base Qwen architecture and specific training methodology. Its primary use case is in applications requiring advanced mathematical capabilities.
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Overview
This model, luckeciano/Qwen-2.5-7B-GRPO-Adam-HessianMaskToken-1e-2-Symmetric-v2_2319, is a 7.6 billion parameter language model built upon the Qwen/Qwen2.5-Math-7B base. It has been specifically fine-tuned to excel in mathematical reasoning tasks, utilizing the GRPO (Generalized Reinforcement Learning with Policy Optimization) method. The training was conducted on the DigitalLearningGmbH/MATH-lighteval dataset, a specialized dataset for mathematical evaluation.
Key Capabilities
- Enhanced Mathematical Reasoning: Optimized for solving complex mathematical problems through targeted fine-tuning.
- GRPO Training: Leverages the GRPO method, introduced in the DeepSeekMath paper, for improved performance in mathematical contexts.
- Qwen 2.5 Architecture: Benefits from the robust base architecture of the Qwen 2.5 series.
Good for
- Mathematical Problem Solving: Ideal for applications requiring high accuracy in mathematical reasoning and computation.
- Research in RLHF for Math: Useful for researchers exploring the impact of methods like GRPO on language models for mathematical domains.
- Educational Tools: Can be integrated into systems designed to assist with or evaluate mathematical understanding.