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--- |
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license: apache-2.0 |
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tags: |
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- unsloth |
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- trl |
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- sft |
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- code |
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- reasoning |
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datasets: |
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- nvidia/OpenCodeReasoning |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen3-0.6B |
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pipeline_tag: text-generation |
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library_name: transformers |
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--- |
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# Qwen3-0.6B-Code-Expert |
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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. |
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## Training Procedure |
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1. **Dataset Preparation** |
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* `nvidia/OpenCodeReasoning` dataset was used. |
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* Each example consists of code snippets paired with detailed step-by-step reasoning in Chain-of-Thought (CoT) style. |
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2. **Model Loading and Configuration** |
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* Qwen3-0.6B base model weights were loaded via the `unsloth` library in bf16 precision. |
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* Full fine-tuning (`full_finetuning=True`) was applied to all layers for optimal adaptation to code reasoning. |
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3. **Supervised Fine-Tuning** |
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* Employed the Hugging Face TRL library with the Supervised Fine-Tuning (SFT) approach. |
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* The model was trained to generate correct code solutions along with the corresponding reasoning chains. |
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## Purpose and Outcome |
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* The model’s capacity for understanding, reasoning about, and generating code was significantly improved through specialized, single-dataset training in bf16 precision. |
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* Outputs include both intermediate reasoning steps and final code solutions, enabling transparent and interpretable code generation. |
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## License |
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This project is licensed under the Apache License 2.0. See the [LICENSE](./LICENSE) file for details. |
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## Support |
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<a href="https://www.buymeacoffee.com/suayptalha" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a> |
