Mastering LLMs: Privacy, Compression, and Market Trends
Fine-tuning large language models (LLMs) like Mistral 7B at home is now more feasible thanks to techniques like Low Rank Adaptation (LoRA) and its quantized variant QLoRA, which reduce the computational and memory requirements. This guide explores the process of fine-tuning to modify a model's behavior or style using a single GPU, highlighting the importance of data preparation and the impact of hyperparameters. While fine-tuning is resource-intensive, it offers a way to customize models beyond what retrieval augmented generation (RAG) and prompt engineering can achieve.