luckeciano/Qwen-2.5-7B-GRPO-Base-v2_1723
TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 13, 2025Architecture:Transformer Featherless Exclusive Cold
The luckeciano/Qwen-2.5-7B-GRPO-Base-v2_1723 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 in mathematical reasoning. This model is designed to excel in complex mathematical problem-solving and related analytical tasks.
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Overview
This model, luckeciano/Qwen-2.5-7B-GRPO-Base-v2_1723, is a 7.6 billion parameter language model. It is a fine-tuned variant of the Qwen/Qwen2.5-Math-7B base model, specifically enhanced for mathematical reasoning capabilities.
Key Capabilities
- Enhanced Mathematical Reasoning: The model has been fine-tuned on the
DigitalLearningGmbH/MATH-lightevaldataset, which focuses on mathematical problems, significantly improving its ability to handle complex mathematical tasks. - GRPO Training Method: It utilizes the GRPO (Generalized Reinforcement Learning with Policy Optimization) training method, as introduced in the DeepSeekMath paper, to optimize its performance in mathematical contexts.
- Qwen 2.5 Architecture: Built upon the Qwen 2.5 architecture, it inherits a robust foundation for language understanding and generation.
Good For
- Mathematical Problem Solving: Ideal for applications requiring advanced mathematical reasoning, calculations, and problem-solving.
- Research in Mathematical AI: Suitable for researchers exploring methods to improve language models' mathematical abilities.
- Educational Tools: Can be integrated into tools designed to assist with or generate mathematical content and solutions.