Fine-tuning large language models
The main purpose of LLMs is to provide zero-shot task generalization with prompt engineering. However, we may still want to fine-tune them for specific tasks. Here, we will fine-tune the most popular open source language model, LLaMA, with the SQuAD dataset. Please note that we can merge different datasets into a single dataset with formatting and train the model. If you want, you can try this in a multi-task experiment. Here, for the sake of simplicity, we are proceeding with a single dataset.
We will use the following techniques for fine-tuning an LLM:
- PEFT: As we discussed in Chapter 9, we will update fewer parameters in the model instead of the entire model using
LoRA. The key benefit of Parameter Efficient Fine-Tuning (PEFT) is its significant reduction in the number of parameters to be trained. This reduction accelerates and simplifies the training process – a valuable attribute for large-scale machine learning projects such...
