What is an LLM & fine-tuning?

FLock LLM model tutorial

What is an LLM?

LLMs, or Large Language Models, are advanced language models that use deep learning for tasks like natural language processing and generation. They are pre-trained on extensive data and can be adapted for specific tasks. These models, like ChatGPT and Google's Bard, have gained immense popularity due to their ability to automate processes, save time and money, and personalise interactions.

LLMs, based on Transformer neural networks, predict text and have revolutionised language processing, making older models obsolete.

What is fine-tuning?

Fine-tuning involves further training the pre-trained LLM on a smaller labeled dataset specific to the task, enabling the model to understand and capture relevant patterns. This process enhances the LLM's performance, allowing it to make more accurate predictions and become a powerful tool for various natural language processing tasks.

What do we expect?

Expected Input:

A labeled dataset specific to the downstream task. For example, if the task is sentiment analysis, the input data would consist of sentences or text passages, each labeled with either positive or negative sentiment.

Expected Output:

The LLM's predictions or classifications for the input data. For sentiment analysis, the output would be whether the sentiment of each input sentence is positive or negative.