How to fine-tune your large language model (LLM) at home by using QLoRa and LoRa
Make your customized chatbot in Google Colab
abstract:
I still vividly recall my first encounter with ChatGPT; it was a recent experience that left me utterly fascinated. This initial interaction sparked a curiosity in me about the possibility of tailoring ChatGPT to specific regional needs and preferences. In this post, I aim to walk you through the process of creating your very own version of ChatGPT, tailored to your specific requirements. We’ll begin our journey by exploring the fundamentals of transformers, the pivotal technology that powers ChatGPT. Understanding this technology is crucial, as it forms the backbone of the model, enabling its advanced language processing capabilities.
In the second part, we’ll delve into the various sizes and capacities of different LLMs, discussing how each size is suited for different applications and needs. Afterwards, we will explore the diverse methodologies for fine-tuning these large-scale models, ensuring they meet your specific goals and perform optimally in your intended context. This discussion will include an in-depth look at innovative techniques like QLoRA (Quantized Low-Rank Adaptation) and LoRA (Low-Rank Adaptation), which are instrumental in fintuning LLMs. These advanced methods allow for efficient and effective customization, making your version of ChatGPT not only more responsive but also more aligned with your unique requirements. Stay with me as we embark on this exciting journey to create a personalized ChatGPT model.
motivation and important takeaways:
Fine-tuning a model like ChatGPT is crucial for enhancing its performance and adaptability to specific tasks or contexts. The main objective of fine-tuning is to modify and adjust the model’s parameters to more accurately understand and respond to the nuances of your specialized domains. This customization is essential in applications ranging from customer service to personalized AI interactions, where the model must understand and respond accurately to a diverse range of queries and conversational styles. However, the process is not without its challenges, especially when it comes to large models like ChatGPT.
The primary challenge in fine-tuning large models lies in their size and complexity. ChatGPT, built on a transformer architecture, contains millions of parameters, making it a resource-intensive task to fine-tune. Each parameter needs to be adjusted carefully to improve the model’s performance on specific tasks without losing its general capabilities. This requires a significant amount of computational power and data, and managing these resources effectively is a considerable challenge. Additionally, there’s a risk of overfitting, where the model becomes too specialized to fine-tune data and loses its ability to generalize, which is crucial for a versatile AI model like ChatGPT.
QLoRa (Quantized Low Rank Adaptation) is a powerful tool in this context. It addresses the challenges of fine-tuning by enabling more efficient and targeted modifications of the model’s parameters. QLoRA works by identifying and adapting only the most critical parameters, reducing the overall computational burden. This approach, combined with quantization techniques, streamlines the fine-tuning process, making it more accessible and less resource-intensive. With QLoRA, it becomes possible to fine-tune large models like ChatGPT more effectively, ensuring they remain versatile while being adept at handling specific tasks or understanding particular linguistic nuances. This method opens up new possibilities for customizing AI models to suit a wide array of applications, making them more useful and relevant in various industries and contexts.
we’re going to thoroughly explore the innovative methodologies of Low Rank Adaptation (LoRa) and Quantized Low-Rank Adaptation (QLoRa) in the context of fine-tuning large language models (LLMs). We delve deep into the mechanics of these techniques, highlighting their unique capability to efficiently fine-tune vast models by selectively adjusting crucial parameters. This post also sheds light on the practical application of LoRa and QLoRa in LLMs, examining both the benefits and the challenges they present. A significant portion was dedicated to providing practical, executable code examples, illustrating the implementation process of these fine-tuning techniques. Lastly, the report underscored the wide-ranging real-world applications of fine-tuned LLMs, emphasizing the transformative impact of LoRa and QLoRa in various sectors within the AI and machine learning landscape.
To understand QLoRa effectively, a solid grasp of linear algebra, particularly matrix operations, and a basic understanding of matrix rank, is helpful, along with knowledge of the transformer structure in neural networks.
Learning Objective in this story:
Understanding the Importance of Fine-Tuning in AI Models:
— Comprehend why fine-tuning ChatGPT and similar models is crucial for enhancing their performance in specialized domains.
— Recognize the role of fine-tuning in improving model responses to specific types of queries and conversational styles.
Challenges of Fine-Tuning Large Models:
— Learn about the difficulties in fine-tuning large models like ChatGPT due to their size and complexity.
Introduction to QLoRA (Quantized Low Rank Adaptation):
— Explore QLoRa as a solution to the challenges of fine-tuning, focusing on its efficiency in modifying crucial model parameters.
— Understand how quantization techniques in QLoRA contribute to streamlining the fine-tuning process.
Exploring LoRa and QLoRa Methodologies:
— Delve into the mechanics of Low Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA) in fine-tuning large language models (LLMs).
Practical Application and Real-World Impact:
— Review practical code examples demonstrating the implementation of LoRa and QLoRa in fine-tuning.
how it exactly works:
Transformers
Transformers, introduced in the groundbreaking paper “Attention is All You Need”, have revolutionized machine learning, particularly in natural language processing. At the heart of transformers is the attention mechanism, which allows the model to process different parts of the input data (like words in a sentence) in parallel, rather than sequentially as previous models did. This approach greatly improves efficiency and enables the model to capture complex relationships in the data. The self-attention component of this mechanism computes the relevance of each part of the input to other parts, allowing the model to understand context and relationships within the data.
Transformers typically feature an encoder-decoder structure, where the encoder processes the input and the decoder generates the output, making them highly effective for tasks like language translation and summarization. They consist of multiple layers, each with several ‘heads’ that can focus on different parts of the data simultaneously. These models are trained on large datasets and can be fine-tuned for specific applications, leading to their widespread adoption in various fields beyond NLP, including image recognition. The introduction of transformers has led to the development of highly advanced models such as GPT for text generation and BERT for text understanding, representing a major leap forward in the capabilities of machine learning systems.
QLoRa (Quantized Low-Rank Adaptation) and LoRa (Low-Rank Adaptation) are advanced techniques used for fine-tuning large neural network models, particularly transformers, in a more efficient and computationally less demanding way. Here’s a detailed explanation of how they work:
LoRa (Low Rank Adaptation)
LoRa modifies the weights of a pre-trained neural network in a low-rank manner. Instead of updating all weights directly, LoRa introduces two smaller matrices for each weight matrix that need to be fine-tuned.
Consider a weight matrix W in a transformer model. Instead of updating W directly, LoRa adds a low-rank matrix Delta W , where Delta W = BA. Here B and A are smaller matrices of size d times r and r times d , respectively, where d is the original dimension and r is the rank (much smaller than d). This approach significantly reduces the number of parameters to be learned during fine-tuning.
as you can see in the above image matrix of weights, W will be updated by the Low-Rank Matrix with the rank of r.
QLoRa (Quantized Low-Rank Adaptation)
QLoRa builds upon the concept of LoRa but incorporates quantization to further reduce the computational resources needed.
In QLoRa, the matrices B and A are quantized, meaning their numerical precision is reduced. This can involve reducing the floating-point precision or even using integer representations. Quantization reduces the memory footprint and computational complexity.
QLoRA employs a double quantization approach, effectively compressing a 32-bit model into a 4-bit representation. This process significantly reduces the model’s size and the computational resources required for processing.
toy example :
Transformers consist of multiple layers with self-attention and feed-forward networks. Each of these components has weight matrices that can be large.
In practice, applying LoRa or QLoRa to a transformer involves adding these low-rank matrices to key components like the self-attention. This allows for targeted fine-tuning of specific aspects of the model while keeping the vast majority of the pre-trained weights fixed.
Both LoRa and QLoRa provide a balance between the effectiveness of full-model fine-tuning and the efficiency of parameter-efficient methods, making them particularly useful for adapting large models like GPT-3 or BERT for specific tasks without the need for extensive computational resources.
We’ll use a hypothetical large language model (LLM) with a size of 7 billion parameters and an embedding size of 8,000 as our example.
Assume our LLM has a structure similar to GPT, with layers consisting of weight matrices for self-attention and feed-forward networks.
Let’s focus on a single weight matrix W from one of these layers, with dimensions 8,000 x 8,000 (since the embedding size is 8,000).
Standard Fine-Tuning Approach
In a typical fine-tuning scenario, you would update all elements of W, which amounts to 64,000,000 parameters (8,000 x 8,000) for just this matrix.
Applying LoRa
With LoRa, instead of updating W directly, we introduce two smaller matrices, B and A, where Delta W = BA .
Let’s choose a rank r = 100(significantly smaller than 8,000). Now, B is 8,000 x 100 and A is 100 x 8,000. The number of parameters to optimize becomes the sum of elements in B and A, which is 1,600,000 (8,000 x 100 + 100 x 8,000) — a substantial reduction from 64,000,000.
Enhancing with QLoRa
QLoRa takes this further by quantizing B and A. Let’s say we quantize these matrices to a lower precision, effectively reducing the amount of memory and computation required for each parameter. While the number of parameters (1,600,000) remains the same as in LoRa, the resource requirement per parameter is reduced due to quantization.
This toy example illustrates the core benefit of LoRa and QLoRa: enabling efficient fine-tuning of large models by focusing on a substantially smaller subset of adaptable parameters.
Conclusion and Results
By using LoRa and enhancing it with QLoRa, we’ve managed to reduce the number of parameters that need to be optimized from 64,000,000 to 1,600,000, while still allowing significant adaptability and learning capacity in the model.
This reduction is crucial, especially when dealing with LLMs like the 7B-parameter model, as it makes fine-tuning more feasible and resource-efficient.
The image illustrates that by implementing LoRA on GPT-2 M(FT), the parameter count dramatically decreased from 354.92 million to just 0.35 million, a reduction by a factor of 1000. Despite this substantial decrease in parameters, the performance metrics were not significantly compromised. This indicates that LoRA enables the maintenance of a comparable output quality to an original size model counterpart, even with a much smaller parameter set.
References:
Google Colab code and some questions for assessing your understanding;
