emna04/mathtutor-qwen2.5-math-7b-merged
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Apr 12, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
emna04/mathtutor-qwen2.5-math-7b-merged is a 7.6 billion parameter language model based on the Qwen2.5 architecture, specifically fine-tuned for mathematical tasks. This model integrates a LoRA adapter with the unsloth/Qwen2.5-Math-7B base model, leveraging a 32768 token context length. It is designed to excel in mathematical reasoning and problem-solving applications.
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Model Overview
emna04/mathtutor-qwen2.5-math-7b-merged is a specialized language model with 7.6 billion parameters, built upon the robust Qwen2.5 architecture. This model is a result of merging a fine-tuned LoRA adapter (emna04/mathtutor-qwen2.5-math-7b-lora) with its base model, unsloth/Qwen2.5-Math-7B.
Key Capabilities
- Mathematical Proficiency: The primary focus of this merged model is to enhance performance in mathematical reasoning and problem-solving.
- Qwen2.5 Foundation: Benefits from the strong base capabilities of the Qwen2.5 architecture.
- Extended Context Window: Supports a substantial context length of 32768 tokens, allowing for processing longer and more complex mathematical problems or discussions.
Good for
- Mathematical Applications: Ideal for tasks requiring accurate mathematical computations, logical reasoning, and problem-solving.
- Educational Tools: Can be integrated into platforms for tutoring, generating math exercises, or explaining mathematical concepts.
- Research and Development: Suitable for researchers exploring advanced mathematical AI or developing specialized tools in quantitative fields.