---
license: apache-2.0
tags:
- unsloth
- trl
- sft
- code
- reasoning
datasets:
- nvidia/OpenCodeReasoning
language:
- en
base_model:
- Qwen/Qwen3-0.6B
pipeline_tag: text-generation
library_name: transformers
---

# Qwen3-0.6B-Code-Expert

This project performs full fine-tuning on the **Qwen3-0.6B** language model to enhance its code reasoning and generation capabilities. Training was conducted exclusively on the `nvidia/OpenCodeReasoning` dataset, and the model was optimized using the bfloat16 (bf16) data type.

## Training Procedure

1. **Dataset Preparation**

   * `nvidia/OpenCodeReasoning` dataset was used.
   * Each example consists of code snippets paired with detailed step-by-step reasoning in Chain-of-Thought (CoT) style.

2. **Model Loading and Configuration**

   * Qwen3-0.6B base model weights were loaded via the `unsloth` library in bf16 precision.
   * Full fine-tuning (`full_finetuning=True`) was applied to all layers for optimal adaptation to code reasoning.

3. **Supervised Fine-Tuning**

   * Employed the Hugging Face TRL library with the Supervised Fine-Tuning (SFT) approach.
   * The model was trained to generate correct code solutions along with the corresponding reasoning chains.

## Purpose and Outcome

* The model’s capacity for understanding, reasoning about, and generating code was significantly improved through specialized, single-dataset training in bf16 precision.
* Outputs include both intermediate reasoning steps and final code solutions, enabling transparent and interpretable code generation.

## License

This project is licensed under the Apache License 2.0. See the [LICENSE](./LICENSE) file for details.

## Support

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