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Getting Started with Large Language Models: Key Things to Know

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As a machine learning engineer who has witnessed the rise of Large Language Models (LLMs), I find it daunting to comprehend how the ecosystem surrounding LLMs is developing. Every week, I come across new tools and techniques related to LLMs on my Twitter feed. It can be difficult to keep up with ways in which the LLM ecosystem is evolving. And if you’re just starting to use LLMs, the stream may seem to be moving too quickly to jump in! The good news is that we've now reached a point where there are reliable and easy-to-use tools to work with LLMs. While more advanced tools will likely appear in the future, the current choices for fine-tuning models or making predictions are pretty impressive. They empower you to create some truly powerful applications. In this post, I dive into the core principles of LLMs and the tools and techniques you’ll need to get started with LLMs. A Large Language Model, as the name implies, refers to a model trained on large datasets to comprehend and generate

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