This tutorial outlines a streamlined process for training and deploying custom language models using the hf-llm-trainer skill in Spaces. It covers setting up an environment, fine-tuning a model on customer support data through supervised fine-tuning (SFT), evaluating performance improvements before and after training, and testing with real-world examples. The guide also explains how to choose between SFT, direct preference optimization (DPO), and group relative policy optimization (GRPO) based on dataset characteristics. It includes cost considerations for different hardware options and emphasizes the importance of validating datasets to prevent errors. Finally, it details converting trained models into GGUF format for efficient local deployment.
Read the full article at Towards AI - Medium
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