Alelcv27/Qwen3-4B-INST-Math
Alelcv27/Qwen3-4B-INST-Math is a 4 billion parameter instruction-tuned Qwen3 model developed by Alelcv27, fine-tuned for mathematical tasks. This model leverages Unsloth and Huggingface's TRL library for accelerated training, offering a 32768 token context length. It is optimized for efficient performance in mathematical reasoning and problem-solving applications.
Loading preview...
Model Overview
Alelcv27/Qwen3-4B-INST-Math is a 4 billion parameter instruction-tuned language model based on the Qwen3 architecture, developed by Alelcv27. This model was specifically fine-tuned for mathematical tasks, making it suitable for applications requiring numerical reasoning and problem-solving capabilities. It features a substantial context length of 32768 tokens, allowing it to process longer and more complex mathematical prompts.
Key Characteristics
- Architecture: Qwen3-based, instruction-tuned.
- Parameter Count: 4 billion parameters.
- Context Length: Supports up to 32768 tokens.
- Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training.
Ideal Use Cases
- Mathematical Reasoning: Excels in tasks requiring logical deduction and numerical computation.
- Problem Solving: Suitable for applications involving complex mathematical problems.
- Efficient Deployment: Its optimized training process suggests potential for efficient inference in mathematical contexts.