{"id":33219,"date":"2023-08-29T03:00:00","date_gmt":"2023-08-29T10:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=33219"},"modified":"2023-08-29T09:17:11","modified_gmt":"2023-08-29T16:17:11","slug":"the-countles-worth-of-fine-tuning-foundation-models","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/08\/29\/the-countles-worth-of-fine-tuning-foundation-models\/","title":{"rendered":"The Countless Worth of Fine-Tuning Foundation Models"},"content":{"rendered":"\n

Foundation models have surged to the fore of modern machine learning applications for numerous reasons. Their generative capabilities\u2014including videos, images, and text\u2014are unrivaled. They readily perform a multiplicity of tasks, as epitomized by the utility of Large Language Models (LLMs) in everything from transcription to summarization.<\/p>\n\n\n\n

But most importantly, they\u2019re readily repurposed from one dataset, application, and domain to another, without requiring undue time, financial resources, and energy.<\/p>\n\n\n\n

Applying these models to related tasks\u2014such as employing a model designed to classify cars to classify vans\u2014is known as fine-tuning. The capacity for organizations to deploy foundation models for mission critical enterprise use cases ultimately hinges on model fine-tuning, which is vital for building applications with them.<\/p>\n\n\n\n

According to Monster API<\/a> CEO Saurabh Vij, \u201cMost developers either want to access foundation models, or fine-tune them for domain specific tasks, or for their own custom datasets.\u201d<\/p>\n\n\n\n

In addition to expediting the time required to repurpose foundation models for a developer\u2019s own particular needs, model fine-tuning furthers the democratization of data science. Contemporary solutions in this space utilize codeless fine-tuning options in cloud platforms (which include both GPUs and foundation models on demand) that are accessible through APIs.<\/p>\n\n\n\n

Users can quickly avail themselves of pre-trained models, fine-tune them for their specific use cases, and broaden the accessibility and impact of advanced machine learning throughout the enterprise.<\/p>\n\n\n\n

Model Fine-Tuning<\/strong><\/p>\n\n\n\n

Conceptually, model fine-tuning is one of the many forms of transfer learning<\/a> in which \u201cyou\u2019re taking a pre-trained model and then you\u2019re now re-training it for another task,\u201d Vij explained. \u201cThe best part is you don\u2019t need to spend hundreds of thousands of dollars to do this. You can do this for 200 to 300 dollars.\u201d When building an object detection model from scratch to identify cars, for example, users would have to give it hundreds of thousands, if not millions, of examples for its initial training.<\/p>\n\n\n\n

However, to apply that model to a similar task like recognizing trucks, one could simply fine-tune it and avoid lengthy (and costly) re-training from scratch. \u201cWhat we understand is there is so many common features, like headlights, the structure, wheels,\u201d Vij noted. \u201cAll those things are similar for both use cases. So, what if we could just remove one or two layers of the car\u2019s model and add new layers for the trucks? This is called fine-tuning.\u201d<\/p>\n\n\n\n

Parameter-Efficient Fine-Tuning<\/strong><\/p>\n\n\n\n

There is an abundance of approaches to fine-tuning foundation models. Parameter-Efficient Fine-Tuning<\/a> is one such method that involves training fewer parameters while rectifying typical constraints pertaining to communication and storage. When asked about the cost-effective method of fine-tuning mentioned earlier, Vij characterized it as \u201ca type of transfer learning, which is Parameter-Efficient Fine-Tuning, and one of the techniques is LoRA, which is quickly becoming an industry standard.\u201d<\/p>\n\n\n\n

Low-Rank Adaption (LoRA)<\/a> is renowned for its ability to reduce costs and increase efficiency for fine-tuning machine learning models. Some of the reasons fine-tuning can become expensive (when other approaches are invoked) is because \u201cdeep learning models are very large models and they have a lot of memory requirements,\u201d Vij commented. \u201cThere\u2019s a lot of parameters in them and their weights are very heavy.\u201d<\/p>\n\n\n\n

LoRA is emerging as a preferrable technique for fine-tuning these models, partially because it\u2019s an alternative to full fine-tuning. According to Vij, \u201cWith LoRA, what happens is you don\u2019t need to fine-tune the entire model, but you can fine-tune a specific part of the model, which can be adapted to learn for the new dataset.\u201d LoRA enables users to quickly fine-tune models to identify the salient traits from a new dataset\u2014without building a new model. The computational paradigm underpinning LoRA is partly based on reductions, which improves its overall efficiency. \u201cWe\u2019re using this approach because lots of computations reduce a lot,\u201d Vij revealed.<\/p>\n\n\n\n

Foundation Models <\/strong><\/p>\n\n\n\n

Once they adopt the proper techniques, organizations have the latitude to fine-tune several foundation models to meet their enterprise AI needs\u2014particularly if they require generative capabilities. Some of the more useful models  include:<\/p>\n\n\n\n