Fine-Tuning Pipeline

Fine-Tuning Pipeline Everything you need to fine-tune open-source LLMs on your own data — from dataset preparation through training to deployment. This pipeline handles LoRA/QLoRA configuration, training data formatting, hyperparameter management, experiment tracking, model merging, and quantized deployment. Designed for teams running fine-tuning on single GPUs or small clusters without deep ML infrastructure expertise. Key Features LoRA & QLoRA Training — Parameter-efficient fine-tuning scripts with automatic rank selection, target module detection, and 4-bit quantization support Dataset Preparation — Convert raw data (CSV, JSON, conversations) into training-ready formats with deduplication, filtering, and train/val/test splits Hyperparameter Management — Predefined configs for common base models (Llama, Mistral, Phi) with recommended learning rates, batch sizes, and schedules Training Monitoring — Real-time loss curves, gradient norms, learning rate schedules, and GPU utilization