1. Fine-tuned Llama-3-8B into a domain-specific data science code assistant using supervised fine-tuning (SFT); built the full training-data pipeline, curating, cleaning, and ChatML-formatting a 50K-example instruction corpus. 2. Built a QLoRA training pipeline (Hugging Face PEFT, 4-bit quantization) that cut GPU memory use by ∼75%, making full training runs feasible on a single consumer GPU. 3. Built a held-out evaluation harness benchmarking fine-tuned outputs against base Llama-3-8B on SQL and pandas code-generation tasks, scoring with ROUGE-L and BLEU. 4. Reduced catastrophic forgetting by mixing general-instruction regularization data into the training set, preserving base-model performance on non-domain tasks.