---
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

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