Qwen3-0.6B-IF-Expert

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.

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