Fine-Tuning GPT with LoRA using PyTorch

This course walks you through fine-tuning GPT models efficiently using Low-Rank Adaptation (LoRA), a lightweight method that allows you to achieve high performance with minimal computational cost. You’ll learn how to inject LoRA into a pre-trained GPT model using PyTorch and Hugging Face tools — all through a hands-on coding approach.

Module Breakdown

1. Introduction to GPT and LoRA

Duration: 0:00 – 2:58
Gain a foundational understanding of transformer models, GPT architecture, and the motivation behind using LoRA for efficient adaptation.

2. Initializing the Module and Code

Duration: 2:59 – 36:00
Step-by-step walkthrough of setting up the environment, importing necessary libraries, and initializing a base GPT model for fine-tuning.

3. LoRA Fine-Tuning Process

Duration: 37:02 – 38:48
Detailed implementation of the LoRA fine-tuning process:

• Initializing LoRA layers and low-rank parameters

• Loading training data

• Freezing the base model parameters

• Injecting LoRA adapters for targeted fine-tuning

4. Results & Evaluation

Duration: 38:58 – 1:00:00
Compare performance between the base GPT model and the LoRA fine-tuned version. Learn to evaluate improvements using standard metrics and visualize the efficiency of LoRA.

5. Wrap-Up & Conclusion

Duration: 1:01:00 – 1:09:19
Summarizes key takeaways including:

• LoRA’s role in reducing computational overhead

• Use cases across different domains

• How to extend LoRA fine-tuning to other transformer-based tasks

Next Steps Covered in the Course

• Loading pre-trained GPT models

• Integrating and configuring LoRA adapters

• Evaluating model performance post fine-tuning

• Customizing LoRA for domain-specific applications