Summary
In this chapter, we covered the process of fine-tuning LLMs. We started with a definition of fine-tuning and general considerations to take into account if you have to decide to fine-tune your LLM.
We then went hands-on with practical sections on fine-tuning. We covered a scenario where, starting from a base BERT model, we wanted a powerful review sentiment analyzer. To do so, we fine-tuned the base model on the IMDB dataset using a full-code approach with Hugging Face Python libraries.
Fine-tuning is a powerful technique to further customize LLMs toward your goal. However, along with many other aspects of LLMs, it comes with some concerns and considerations in terms of ethics and security. In the next chapter, we are going to delve deeper into that, sharing how to establish guardrails with LLMs and, more generally, how governments and countries are approaching the problem from a regulatory perspective.
