Home Artificial Intelligence High quality-Tune Your LLM Without Maxing Out Your GPU Demand for Bespoke LLMs Why Do We High quality-tune? The Dataset

High quality-Tune Your LLM Without Maxing Out Your GPU Demand for Bespoke LLMs Why Do We High quality-tune? The Dataset

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High quality-Tune Your LLM Without Maxing Out Your GPU
Demand for Bespoke LLMs
Why Do We High quality-tune?
The Dataset

How you may fine-tune your LLMs with limited hardware and a good budget

Towards Data Science
Image by Creator: Generated with Midjourney

With the success of ChatGPT, we’ve got witnessed a surge in demand for bespoke large language models.

Nonetheless, there was a barrier to adoption. As these models are so large, it has been difficult for businesses, researchers, or hobbyists with a modest budget to customize them for their very own datasets.

Now with innovations in parameter efficient fine-tuning (PEFT) methods, it’s entirely possible to fine-tune large language models at a comparatively low price. In this text, I show easy methods to achieve this in a Google Colab.

I anticipate that this text will prove invaluable for practitioners, hobbyists, learners, and even hands-on start-up founders.

So, if that you must mock up an inexpensive prototype, test an idea, or create a cool data science project to face out from the gang — keep reading.

Businesses often have private datasets that drive a few of their processes.

To offer you an example, I worked for a bank where we logged customer complaints in an Excel spreadsheet. An analyst was liable for categorising these complaints (manually) for reporting purposes. Coping with 1000’s of complaints every month, this process was time-consuming and liable to human error.

Had we had the resources, we could have fine-tuned a big language model to perform this categorisation for us, saving time through automation and potentially reducing the speed of incorrect categorisations.

Inspired by this instance, the rest of this text demonstrates how we will fine-tune an LLM for categorising consumer complaints about financial services.

The dataset comprises real consumer complaints data for financial services and products. It’s open, publicly available data published by the Consumer Financial Protection Bureau.

There are over 120k anonymised complaints, categorised into roughly 214 “subissues”.

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