--- license: apache-2.0 tags: - unsloth - trl - sft - instruction-following - reasoning datasets: - patrickfleith/instruction-freak-reasoning language: - en base_model: - Qwen/Qwen3-0.6B pipeline_tag: text-generation library_name: transformers --- # 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](./LICENSE) file for details. ## Support