waleko/Qwen2.5-Math-7B-RoPE-300k

TEXT GENERATIONConcurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 2, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

waleko/Qwen2.5-Math-7B-RoPE-300k is a 7.6 billion parameter mathematical language model developed by Qwen, part of the Qwen2.5-Math series. It is specifically designed to solve English and Chinese math problems using both Chain-of-Thought (CoT) and Tool-integrated Reasoning (TIR). This model demonstrates significant performance improvements on mathematical benchmarks compared to its predecessor, Qwen2-Math, making it highly specialized for complex mathematical and algorithmic tasks.

Loading preview...

Overview

waleko/Qwen2.5-Math-7B-RoPE-300k is a 7.6 billion parameter model from the Qwen2.5-Math series, developed by Qwen. This series represents an upgrade from the earlier Qwen2-Math models, focusing on enhanced mathematical problem-solving capabilities. It supports both English and Chinese math problems, utilizing advanced reasoning techniques.

Key Capabilities

  • Mathematical Reasoning: Primarily designed for solving mathematical problems, distinguishing it from general-purpose LLMs.
  • Chain-of-Thought (CoT): Leverages CoT for improved reasoning, showing significant performance gains on Chinese and English mathematics benchmarks.
  • Tool-integrated Reasoning (TIR): Incorporates TIR to enhance computational accuracy and handle complex mathematical or algorithmic tasks, such as finding roots of equations or computing eigenvalues. The 7B-Instruct variant achieves 85.3 on the MATH benchmark using TIR.
  • Multilingual Support: Capable of solving math problems in both English and Chinese.

When to Use This Model

This model is highly specialized for mathematical tasks. It is not recommended for general-purpose tasks outside of solving math problems. Developers should consider this model for applications requiring:

  • Accurate mathematical problem-solving.
  • Reasoning through complex mathematical concepts.
  • Integration with tools for precise computation and symbolic manipulation.

For fine-tuning or completion tasks, the base model Qwen2.5-Math-7B is recommended, while Qwen2.5-Math-7B-Instruct is suitable for conversational or instruction-following mathematical interactions.

Finetunes
1 models