Fine-Tune LLMs

Overview

Fine-tuning Large Language Models (LLMs) involves adapting pre-trained models to specific tasks or domains by further training them on task-specific data. This process is particularly popular in natural language processing (NLP) tasks where LLMs, such as GPT (Generative Pre-trained Transformer) models, have demonstrated remarkable performance across various domains.

Fine Tuning Techniques

1. Parameter-efficient Fine-tuning techniques (PEFT)

• PEFT methods only fine-tune a small number of (extra) model parameters - significantly decreasing computational and storage costs - while yielding performance comparable to a fully fine-tuned model. This makes it more accessible to train and store large language models (LLMs) on consumer hardware.
• For example Lora - Qlora, Prefix Tuning, P-Tuning, Prompt Tuning, AdaLoRA, Multi-task prompt tuning, etc.

2. Full-model Fine-tuning

• Full model fine-tuning, also known as end-to-end fine-tuning, involves updating all parameters of a pre-trained language model (LM) during training on a specific downstream task. Unlike adapter-based learning, where only task-specific adapters are trained while keeping the pre-trained model parameters fixed, full model fine-tuning modifies all parameters of the original pre-trained model.

3. Prompt based Fine-tuning

• Prompt-based fine-tuning involves fine-tuning a pre-trained language model (LM) by providing task-specific prompts or instructions during the training process. Instead of training the model to directly predict the next word or token in a sequence, the model is trained to generate text conditioned on a given prompt, which guides its generation toward the desired task or domain.
• For example One shot learning, Few shot learning

Happy Coding!!