Qwen3-0.6B-Math-Expert

Qwen3-0.6B-Math-Expert

This project performs full fine-tuning on the Qwen3-0.6B language model to enhance its mathematical problem-solving and reasoning capabilities. Training was conducted exclusively on the OpenMathReasoning-mini dataset, and the model was optimized using the bfloat16 (bf16) data type.

Training Procedure

1. Dataset Preparation

   • The unsloth/OpenMathReasoning-mini dataset was used.
   • Each example was formatted in Chain-of-Thought (CoT) style, pairing math problems with step-by-step intermediate reasoning.

2. Model Loading and Configuration

   • Qwen3 base model weights were loaded via the unsloth library in bf16 precision.
   • All layers were updated (full_finetuning=True) to adapt the model for mathematical reasoning.

3. Supervised Fine-Tuning

   • Leveraged the Hugging Face TRL library with the Supervised Fine-Tuning (SFT) approach.
   • The model was trained to generate both correct answers and corresponding reasoning chains.

Purpose and Outcome

• The model’s reasoning capacity for math problems was significantly improved through single-dataset, full fine-tuning in bf16 precision.
• Outputs include both intermediate reasoning steps and final solutions, providing transparent and interpretable results.

License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.

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