KhaledAalnobani/Qwen2.5-Math-1.5B-2
KhaledAalnobani/Qwen2.5-Math-1.5B-2 is a 1.5 billion parameter Qwen2-based language model developed by KhaledAalnobani. This model is specifically fine-tuned for mathematical tasks, leveraging the Qwen2.5 architecture. It was trained using Unsloth and Huggingface's TRL library, enabling faster training. The model is designed to excel in mathematical reasoning and problem-solving within its 32768 token context length.
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Model Overview
KhaledAalnobani/Qwen2.5-Math-1.5B-2 is a 1.5 billion parameter language model based on the Qwen2.5 architecture, developed by KhaledAalnobani. This model has been specifically fine-tuned to enhance its capabilities in mathematical reasoning and problem-solving.
Key Characteristics
- Architecture: Built upon the Qwen2.5 model family.
- Parameter Count: Features 1.5 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, beneficial for complex mathematical problems requiring extensive context.
- Training Efficiency: The model was trained with Unsloth and Huggingface's TRL library, which facilitated a 2x faster fine-tuning process.
Primary Use Case
This model is primarily intended for applications requiring strong mathematical understanding and problem-solving abilities. Its fine-tuning makes it particularly suitable for tasks involving numerical reasoning, equations, and other math-centric challenges.