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Qwen3-0.6B-IF-Expert/README.md

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---
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 models 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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