In this course, you’ll learn LLM fine-tuning, including LoRA and QLoRA fine-tuning techniques, and apply LLM quantization to efficiently adapt large AI models like Llama 3 with hands-on exercises.

finetuning2.tar.gz

quantization

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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

Reduces the computational cost of the model.

Increases the number of trainable parameters of the model.


Decreases the number of trainable parameters of the model.

Reduces the precision of weights of the model.

What is the effect of increasing the rank value in LoRA?


What is the effect of increasing the rank value in LoRA?

Consider that we are fine-tuning an LLM (with 13B parameters) for a completely new dataset compared to its pretraining data. Which option will be *true* for optimal results of the fine-tuned model?

Consider that we are fine-tuning an LLM (with 13B parameters) for a completely new dataset compared to its pretraining data. Which option will be true for optimal results of the fine-tuned model?

Consider a pretrained LLM with a weight matrix of dimension [1024 x 1024] that is fine-tuned using LoRA. If the rank for low-rank matrices is 32, what is the approximate percentage of trainable parameters compared to directly fine-tuning the original matrix?

Consider a pretrained LLM with a weight matrix of dimension [1024 x 1024] that is fine-tuned using LoRA. If the rank for low-rank matrices is 32, what is the approximate percentage of trainable parameters compared to directly fine-tuning the original matrix?

It compresses the model's weights from 32-bit floating point to 4-bit format.

It updates the quantized weights of the model during fine-tuning.



It ensures that each quantization bin has an equal number of values assigned from the input tensor.

It scales weights to fit within the range [−1,1] before compression.

Which option is *not true* about 4-bit NormalFloat(NF4) quantization?

Which option is not true about 4-bit NormalFloat(NF4) quantization?

It minimizes the rounding errors during quantization.


It allows quantization constants to be reused across blocks to save memory.


It speeds up the quantization process by applying it to multiple blocks simultaneously.



It ensures that quantization bins are used efficiently by splitting input tensors into blocks.


Why is block-wise quantization used in QLoRA?

Why is block-wise quantization used in QLoRA?

Test your understanding of LoRA by taking this quiz.

Getting Started

Basics of Fine-Tuning

Exploring LoRA

Wrap Up

Quiz: Exploring LoRA