Techniques for fine-tuning models
In this section, we’ll discuss two fine-tuning methods: the traditional full fine-tuning approach and advanced techniques such as PEFT, which integrates optimizations to attain comparable results to full fine-tuning but with higher efficiency and reduced memory and computational expenses.
Full fine-tuning
Full fine-tuning refers to the approach where all parameters/weights of a pretrained model are adjusted using a task-specific dataset. It’s a straightforward method and is generally effective, but it might require a considerable amount of data to avoid overfitting and compute, especially for large models.
The challenges with generic full fine-tuning methods include updating all the model parameters of the LLMs for every downstream task. Here are some more issues to consider:
- High compute and memory requirements: Full fine-tuning can increase the cost of compute exorbitantly, result in large memory requirements, and also...
