In large language models (LLMs) like ChatGPT, after the model processes the input, it produces a final hidden vector - a compact representation that summarizes the context up to that point. To convert this vector back into actual words, the model uses an unembedding matrix, effectively reversing the embedding step. This unembedding matrix is essentially the transpose of the original embedding matrix. It projects the hidden vector into the vocabulary space, producing a numerical score for every p

 The future of Large Language Models is unfolding at PyTorch...

We just released "Large Language Models with Semantic Search”, built with Cohere, and taught by Jay Alammar and Luis Serrano. Search is a key part of many applications. Say, you need to retrieve documents or products in response to a user query; how can LLMs help? You’ll learn about (i) Embeddings, to retrieve a collection of documents loosely related to a query, and (ii) LLM assisted re-ranking, to rank them precisely according to relevance. You’ll also go through code showing how to tie all th