luckeciano/Qwen-2.5-7B-DrGRPO-Adam-FisherMaskToken-1e-4-v2_4051

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-DrGRPO-Adam-FisherMaskToken-1e-4-v2_4051 is a 7.6 billion parameter language model, fine-tuned from Qwen/Qwen2.5-Math-7B. It leverages the DigitalLearningGmbH/MATH-lighteval dataset and was trained using the GRPO method, which is designed to enhance mathematical reasoning. This model is specifically optimized for tasks requiring advanced mathematical problem-solving capabilities, building upon the strong base of the Qwen 2.5 architecture with a 32K context length.

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Model Overview

This model, luckeciano/Qwen-2.5-7B-DrGRPO-Adam-FisherMaskToken-1e-4-v2_4051, is a 7.6 billion parameter language model derived from the Qwen/Qwen2.5-Math-7B base. It has been fine-tuned using the TRL library on the DigitalLearningGmbH/MATH-lighteval dataset.

Key Capabilities

  • Enhanced Mathematical Reasoning: The model's primary differentiator is its training with the GRPO (Gradient Regularized Policy Optimization) method, as introduced in the research paper "DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models". This method specifically aims to improve the model's ability to handle complex mathematical problems.
  • Qwen 2.5 Architecture: Benefits from the robust Qwen 2.5 base model, providing a strong foundation for general language understanding and generation.
  • Extended Context Length: Supports a context length of 32,768 tokens, allowing for processing longer inputs and maintaining coherence over extended mathematical problems or discussions.

Good For

  • Mathematical Problem Solving: Ideal for applications requiring precise and advanced mathematical reasoning, such as solving equations, proofs, or complex arithmetic.
  • Research and Development: Useful for researchers exploring the impact of GRPO and similar training methodologies on mathematical capabilities in large language models.
  • Educational Tools: Can be integrated into tools designed to assist with or generate solutions for mathematical challenges.

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