xw1234gan/SFT_Qwen2.5-1.5B-Instruct_MATH
The xw1234gan/SFT_Qwen2.5-1.5B-Instruct_MATH is a 1.5 billion parameter instruction-tuned causal language model based on the Qwen2.5 architecture, developed by xw1234gan. This model is specifically fine-tuned for mathematical tasks and reasoning, leveraging a 32,768 token context length. It is designed to excel in environments requiring robust mathematical problem-solving capabilities.
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Model Overview
The xw1234gan/SFT_Qwen2.5-1.5B-Instruct_MATH is a 1.5 billion parameter instruction-tuned model built upon the Qwen2.5 architecture. Developed by xw1234gan, this model is distinguished by its specific fine-tuning for mathematical tasks.
Key Characteristics
- Architecture: Qwen2.5 base model.
- Parameter Count: 1.5 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32,768 tokens, beneficial for complex multi-step mathematical problems.
Primary Use Case
This model is primarily intended for applications requiring strong mathematical reasoning and problem-solving. Its instruction-tuned nature suggests suitability for tasks where precise mathematical outputs are critical.