Fine-tuning a Large Language Model (LLM) is often unnecessary for many commercial applications, but it can be useful for tasks requiring specific chat formats, domain knowledge, or cost-effective, specialized tasks. Fine-tuning involves data preparation, including deduplication and removal of personal information, and can be done using techniques like LoRa (Low-Rank Adaptation) or QLoRA. Using reinforcement learning with human feedback (RLHF) or direct preference optimization (DPO) can align models with human preferences. For fine-tuning and hosting, cloud platforms like AWS SageMaker and collaborative tools like HuggingFace are recommended.
Table of contents
Llm Fine Tuning Guide: Do You Need It and How to Do ItWhen to fine-tuneDataData EvaluationDataSet FormatsFine-Tuning techniquesFull re-trainingLoRaQLoRAFine-tuning with (Human) Preference AlignmentReinforcement Learning with Human Feedback (RLHF)Direct Preference Optimization (DPO)What to use for fine-tuning experiments and hostingSort: