H2O LLM Studio

This post details the three-stage training process for H2O-Danube3-4B, outlining the percentage of web data and the total tokens processed at each stage. It highlights the benefits of training Small Language Models (SLMs) over Large Language Models (LLMs), including lower training and operational costs, faster inference, and greater customizability. Specific fine-tuning techniques like instruction/chat fine-tuning, causal classification and regression, and DPO/IPO/KTO optimization are mentioned, along with the potential for SLMs to be more accurate for specific use cases and offer a lower Total Cost of Ownership (TCO).