Gain insights into fine-tuning LLMs with LoRA and QLoRA. Explore parameter-efficient methods, LLM quantization, and hands-on exercises to adapt AI models with minimal resources efficiently.

finetuning2.tar.gz

quantization

lora

qlora

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This course will cover fine-tuning techniques and parameter-efficient adaptations for AI models. It starts with the core concepts of LLM fine-tuning, including its importance in adapting pretrained models to specific tasks. You’ll explore various fine-tuning methods, such as parameter-efficient fine-tuning (PEFT), while understanding key comparisons like pretraining vs. fine-tuning and RAG vs. fine-tuning.

You’ll learn about LLM quantization and focus on int8 and bitsandbytes quantization with hands-on exercises. Moreover, you’ll delve into Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA), which are useful for efficiently fine-tuning large models like Llama 3 on custom datasets. You’ll implement LoRA and QLoRA fine-tuning through interactive sessions, learning to adapt models with minimal resources.

By the end of this course, you’ll gain the expertise to fine-tune and optimize LLMs, becoming skilled in essential model adaptation techniques.

Fine-Tuning LLMs Using LoRA and QLoRA

Larger batch sizes reduce the risk of overfitting but require more memory.

Smaller batch sizes result in faster convergence but require more epochs.


Larger batch sizes speed up the training process but require more memory.


Smaller batch sizes prevent the model from using pretrained knowledge effectively.


Which option is *true* about the batch size?

Which option is true about the batch size?

The model requires more data for training.

The model requires more epochs to improve the performance.

A data scientist trained a model for 10 epochs with a dataset of 20,000 examples. After 6 epochs, the model achieved 93% training accuracy. However, when evaluated on a validation set, the accuracy drops to 81%. 

What is likely to be the problem with the model?


A data scientist trained a model for 10 epochs with a dataset of 20,000 examples. After 6 epochs, the model achieved 93% training accuracy. However, when evaluated on a validation set, the accuracy drops to 81%.
What is likely to be the problem with the model?

Consider a dataset of 5000 images to fine-tune a model for image classification tasks. If the GPU memory can handle a maximum batch size of 256, how many iterations are needed to complete one epoch?

Consider a dataset of 5000 images to fine-tune a model for image classification tasks. If the GPU memory can handle a maximum batch size of 256, how many iterations are needed to complete one epoch?

Which partial fine-tuning method involves training only the bias-terms of the model?


Which partial fine-tuning method involves training only the bias-terms of the model?

It improves the model performance when the dataset is large and complex.

It reduces the computational cost of fine-tuning.

It increases the training time of the model.

It mitigates the issue of catastrophic forgetting.

**(Select all that apply.)** Which statement is *true* for the parametric efficient fine-tuning (PEFT) technique?

(Select all that apply.) Which statement is true for the parametric efficient fine-tuning (PEFT) technique?

What is the drawback of quantization?

Test your understanding of fine-tuning by taking this quiz.

Getting Started

Basics of Fine-Tuning

Exploring LoRA

Wrap Up

Quiz: Basics of Fine-Tuning