LLM Tuning
The process of adapting a model to a new domain or set of custom use cases by training the model on new data
Fine Tuning
Large language model (LLM) fine-tuning is the process of taking pre-trained models and further training them on smaller, specific datasets to refine their capabilities and improve performance in a particular task or domain. Fine-tuning is about turning general-purpose models and turning them into specialized models. It bridges the gap between generic pre-trained models and the unique requirements of specific applications, ensuring that the language model aligns closely with human expectations.
Supervised fine-tuning (SFT)
Supervised fine-tuning means updating a pre-trained language model using labeled data to do a specific task. The data used has been checked earlier. This is different from unsupervised methods, where data isn't checked. Usually, the initial training of the language model is unsupervised, but fine-tuning is supervised.
Methods for fine-tuning LLMs
- Instruction fine-tuning
- Full fine-tuning
- Parameter-efficient fine-tuning (PEFT)