Are Smaller Language Models Obsolete? We Argue Not.

Introduction

Ah, the eternal debate: to be a jack of all trades or a master of one? With the emergence of AI products like ChatGPT and more, you might be wondering where that leaves smaller models designed for specific tasks. Are they obsolete? We argue not. Read on to find out why!

At Pyq, we’re believers in open source models that are designed for specific tasks. In this blog post, we'll explore the advantages of using them, and why they can be an essential tool in the developer's arsenal. We will discuss their pros and cons, compare them to popular, larger (which we also love and use) and describe how they can be useful to you in your next project.

"At Pyq, we’re believers in open source models that are designed for specific tasks. In this blog post, we'll explore the advantages of using them, and why they can be an essential tool in the developer's arsenal.

Language Model Use Cases

The emergence of large language models (LLMs) such as Chat GPT and others has brought the world’s attention to the AI space like never before. They are remarkably good at a multitude of use-cases, and have helped people and businesses simplify their processes in their relatively short existence. But there’s another genre of models that can be equally useful, if not better: smaller ones designed for specific tasks.

Using Smaller Language Model Benefits

We posit that a lot of common tasks, spanning industries, applications and more can be fulfilled by these smaller models. Here are a few of their key benefits:

1. Improved Performance: One of the key benefits of using small, specific open source machine learning models is that they can often deliver better performance than larger, more general models. This is because small models are designed to perform a specific task, making them more focused and efficient. This results in faster inference times, lower memory usage, and more accurate results.
2. Cheaper: Small models need less memory and compute, and still perform the task you need. This is mostly relevant when you’re getting a ton of usage (which, if you’re reading this, I hope you will), but it’s worth noting even in the early stages of your company.
3. Customization: Small, specific open source machine learning models can be easily customized to suit a particular application or use case. Developers can tweak the model's parameters or even train their own models based on their data, making it possible to achieve highly accurate results for specific tasks. This level of customization is often not possible with larger, more general models, which may not be optimized for a particular use case.

The Need for Fine-tuning

The biggest limitation of these models is that they sometimes need to be fine-tuned to perform well on your specific task. This entails generating a dataset to train the model on, and knowing how to fine-tune a model in practice.

To help address this, we are working on a way to easily fine-tune some of our most popular models by ingesting your dataset.

Start trying out popular, specific models on Pyq today. Free credits included.