Qwen3-0.6B-IF-Expert

This project performs full fine-tuning on the Qwen3-0.6B language model to enhance its instruction-following and reasoning capabilities. Training was conducted on the patrickfleith/instruction-freak-reasoning dataset using bfloat16 (bf16) precision for efficient optimization.

Training Procedure

1. Dataset Preparation

   • The patrickfleith/instruction-freak-reasoning dataset was used.
   • Each example contains a complex instruction paired with an in-depth reasoning-based response.
   • Prompts were structured to encourage chain-of-thought style outputs when applicable.

2. Model Loading and Configuration

   • Qwen3 base model weights were loaded via the unsloth library in bf16 precision.
   • All model layers were fully updated (full_finetuning=True) to effectively adapt the model to instruction understanding and stepwise response generation.

3. Supervised Fine-Tuning

   • Fine-tuning was conducted using the Hugging Face TRL library with the Supervised Fine-Tuning (SFT) approach.
   • The model was trained to follow detailed instructions, reason logically, and generate structured responses.

Purpose and Outcome

• The model’s ability to follow complex instructions and explain its reasoning process has been significantly enhanced.
• It generates both coherent reasoning steps and conclusive answers, improving transparency and usability for instruction-based tasks.

License

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

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