gguk2on/qwen2.5-7B-rlar_g8_b384_math_vu4
TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 5, 2026Architecture:Transformer Featherless Exclusive Cold
The gguk2on/qwen2.5-7B-rlar_g8_b384_math_vu4 is a 7.6 billion parameter language model, fine-tuned from Qwen/Qwen2.5-7B. It was trained using the GRPO method, specifically designed to enhance mathematical reasoning capabilities. This model is optimized for complex mathematical tasks and problem-solving, making it suitable for applications requiring strong numerical and logical inference.
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Model Overview
This model, gguk2on/qwen2.5-7B-rlar_g8_b384_math_vu4, is a specialized 7.6 billion parameter language model derived from the Qwen2.5-7B architecture. It has undergone fine-tuning using the TRL framework, with a particular focus on improving mathematical reasoning.
Key Capabilities
- Enhanced Mathematical Reasoning: The model was trained with GRPO (Gradient-based Reward Policy Optimization), a method introduced in the DeepSeekMath paper, specifically to push the limits of mathematical problem-solving in open language models.
- Fine-tuned from Qwen2.5-7B: Leverages the robust base capabilities of the Qwen2.5-7B model, further specialized for numerical and logical tasks.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing longer mathematical problems or complex reasoning chains.
Good For
- Mathematical Problem Solving: Ideal for applications requiring accurate and robust mathematical inference, from algebra to more advanced concepts.
- Research and Development: Useful for researchers exploring advanced fine-tuning techniques for domain-specific reasoning, particularly in mathematics.
- Educational Tools: Can be integrated into tools designed to assist with or generate solutions for mathematical challenges.