How LLMs Work: Pre-Training to Post-Training, Neural Networks, Hallucinations, and Inference
Briefly

from towardsdatascience.com1 month ago
The article demystifies large language models (LLMs) by explaining their fundamental building blocks, including the critical phases of pre-training and post-training. Pre-training involves gathering vast amounts of high-quality text data, which are then cleaned up to form a diverse dataset essential for effective language learning. The author references Andrej Karpathy's popular YouTube video that explores the concepts deeply but notes its length might deter some viewers, thus providing a concise explanation instead. This is the first part of a two-part series that aims to clarify how LLMs function ultimately leading to their use today.
The process of building LLMs involves two key phases: pre-training and post-training, where the foundational understanding of language is established via large datasets.
towardsdatascience.comhttps://towardsdatascience.com/how-llms-work-pre-training-to-post-training-neural-networks-hallucinations-and-inference/
Andrej Karpathy's 3.5-hour YouTube video provides deep insights into LLMs, inspiring a breakdown for those who may not have the time to watch the entire presentation.
towardsdatascience.comhttps://towardsdatascience.com/how-llms-work-pre-training-to-post-training-neural-networks-hallucinations-and-inference/
Read at towardsdatascience.com
#large-language-models #ai-fundamentals #machine-learning #neural-networks
[
|
]