What Is Fine-Tuning? And Its Methods For Best AI Performance
Fine-tuning is the act of modifying a learned model for particular tasks or use cases. It is now a standard deep learning method, especially for developing foundation models for generative artificial intelligence
The process of fine-tuning foundation models is essential to making them entirely appropriate for real-world application, as well as to customizing them to your or your company’s distinct tone and use cases
A crucial step in the LLM development cycle is fine-tuning, which enables the basic foundation models’ linguistic capabilities to be modified for a range of applications, including coding, chatbots, and other creative and technical fields
Models may be customized to represent a brand’s tone by using intricate behavioral patterns and unique graphic styles or by starting each discussion with a pleasant greeting
LLMs may use their broad language skills to specialized assignments. Llama 2 models from Meta include basic foundation models, chatbot-tuned variations (Llama-2-chat), and code-tuned variants
Addressing edge cases: Your model may need to handle circumstances not addressed in pre-training. Using annotated samples to fine-tune a model helps guarantee such scenarios are handled properly
Your organization may have a proprietary data pipeline relevant to your use case. No training is needed to add this information into the model via fine-tuning