Apply-Lightweight-Fine-Tuning-to-a-Foundation-Model

• Lightweight fine-tuning is one of the most important techniques for adapting foundation models, because it allows you to modify foundation models for your needs without needing substantial computational resources.

• In this project, a parameter-efficient fine-tuning using the Hugging Face peft library is applied.

Project Summary

In this project, all of the essential components of a PyTorch + Hugging Face training and inference process are implemeted together. The steps are as the following:

• Load a pre-trained model and evaluate its performance
• Perform parameter-efficient fine tuning using the pre-trained model
• Perform inference using the fine-tuned model and compare its performance to the original model

Key Concepts

Hugging Face PEFT allows you to fine-tune a model without having to fine-tune all of its parameters.

Training a model using Hugging Face PEFT requires two additional steps beyond traditional fine-tuning:

• Creating a PEFT config
• Converting the model into a PEFT model using the PEFT config

Inference using a PEFT model is almost identical to inference using a non-PEFT model. The only difference is that it must be loaded as a PEFT model.