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Current best practices for training LLMs from scratch Current best practices for training LLMs from scratch Conclusion Whether it’s OpenAI,...
INSTRUCTION TUNING At this point, let’s assume we have a pre-trained, general-purpose LLM. If we did our job well, our...
BIAS AND TOXICITY There are potential risks associated with large-scale, general-purpose language models trained on web text. Which is to...
MODEL EVALUATION Typically, pre-trained models are evaluated on diverse language model datasets to assess their ability to perform logical reasoning,...
PRE-TRAINING STEPS Training a multi-billion parameter LLM is usually a highlyexperimental process with lots of trial and error. Normally, theteam...
DATASET PRE-PROCESSING In this section, we’ll cover both data adjustments (like deduplication and cleaning) and the pros and cons of...
DATASET COLLECTION Bad data leads to bad models. But careful processing of high-quality, high-volume, diverse datasets directly contributes to model...
Introduction Although we’re only a few years removed from the transformer breakthrough, LLMs have already grown massively in performance, cost,...
THE SCALING LAWS Before you dive into training, it’s important to cover how LLMs scale. Understanding scaling lets you effectively...
Retrieval-Augmented Generation (RAG) is a powerful technique in AI that combines large language models with real-time access to external data...
