1. Fine-tuning
In this video, we will learn the key challenges of pre-training an LLM and examine how fine-tuning addresses those concerns.
2. Where are we?
Not everyone needs to train an LLM from scratch, as pre-trained models from industry leaders can be fine-tuned for specific tasks.
Hence, we will first explore fine-tuning, while pre-training will be covered in the next chapter.
3. Fine-tuning analogy
Pre-training can be thought of as similar to how children learn to speak a language by observing their surroundings at home and school.
As they enter college and choose to specialize in a specific area, such as medicine, they fine-tune their language understanding based on specific vocabulary and language patterns unique to that domain.
This allows them to communicate more effectively with others in their chosen field.
4. "Largeness" challenges
Fine-tuning is an effective approach used to help LLMs overcome certain challenges.
We have discussed the scale and uses of LLMs in various NLP applications, but the "largeness" of these models also presents several challenges.
Building these models requires powerful computers and specialized infrastructure due to the massive amounts of data and computational resources involved.
Additionally, efficient model training methods and the availability of high-quality training data are essential for optimal model performance.
5. Computing power
One major challenge is the high computational cost of training and deploying LLMs. The sheer size of these models requires a significant amount of memory, processing power, and infrastructure which is quite expensive and difficult to manage.
An LLM may require a few hundred thousand Central Processing Units (CPUs) and tens of thousands of Graphic Processing Units (GPUs) compared to 4-8 CPUs and 0-2 GPUs in a personal computer.
This level of computing power requires large-scale infrastructure, which can be extremely expensive to set up and maintain.
6. Efficient model training
Training an LLM is another key challenge, as it requires significant training time, often weeks or even months.
Efficient model training can lead to faster training times and reduce costs.
Training an LLM might take as much as 355 years of processing on a single GPU.
7. Data availability
Another challenge is the need for high-quality training data to accurately learn the complexities and subtleties of language.
For instance, an LLM is trained on a few hundred gigabytes (GBs) of text data equivalent to more than a million books.
That's a massive amount of data to process!
8. Overcoming the challenges
Fine-tuning addresses some of these challenges by adapting a pre-trained model for specific tasks.
Pre-trained language models typically learn from large, general-purpose datasets and are not optimized for specific tasks.
However, because of the general language structure and flow they learn, they might be an ideal candidate for fine-tuning to a specific problem or dataset.
We will explore the pre-training process in more detail in the next chapter.
9. Fine-tuning vs. Pre-training
Fine-tuning is more effective since it can help a model learn, or be trained, using a single CPU and GPU, while pre-training may require thousands of CPUs and GPUs to train efficiently.
Additionally, fine-tuning can take hours or days, while training a model from scratch may take weeks or months.
Furthermore, fine-tuning requires only a small amount of data, typically ranging from a few hundred megabytes to a few gigabytes, compared to hundreds of gigabytes as are necessary for pre-training.
10. Let's practice!
Let's review the challenges of building LLMs and explore the importance of fine-tuning them before learning about some fine-tuning techniques!