--- 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