Large Language Models, like OpenAI's GPT-4 or Google's PaLM, have taken the world of artificial intelligence by storm. Yet most companies don't currently have the ability to train these models, and are completely reliant on only a handful of large tech firms as providers of the technology.

At Replit, we've invested heavily in the infrastructure required to train our own Large Language Models from scratch. In this blog post, we'll provide an overview of how we train LLMs, from raw data to deployment in a user-facing production environment. We'll discuss the engineering challenges we face along the way, and how we leverage the vendors that we believe make up the modern LLM stack: Databricks, Hugging Face, and MosaicML.

While our models are primarily intended for the use case of code generation, the techniques and lessons discussed are applicable to all types of LLMs, including general language models. We plan to dive deeper into the gritty details of our process in a series of blog posts over the coming weeks and months.