Fine-Tuning LLMs: Use Case Examples

Author(s): Leo Tisljaric, PhD

Originally published on Towards AI.

From LLM-based machine translations to text generation fine-tuning, find all the theory, examples, and code in one place.
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In one of his recent interviews, famous AI scientist Yann LeCun said that we do not have to seek general intelligence because even humans do not have it as „machines“ that have very specific and limited knowledge and skills. I totally agree with that statement, and this article will show you how to apply this paradigm to LLMs by fine-tuning and specializing them to do only one task. The main finetuning goal is to adapt the base LLM, which is not great in generating even simple statements, to generate a meaningful text according to the finetuning objective that can be question answering, summarization, chat, translation, or similar.

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You can use this article as a knowledge base with finetuning examples which you use in your daily work, or as a learning material that will help you to grasp the quite complicated finetuning process.

Article’s content:

About FinetuningHow to finetune?Use cases (translation, text classification, text generation)CodeConclusion

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Published via Towards AI