The matchup we have all been waiting for! A fine-tuned upstart underdog against the reigning champion heavyweight LLM! Will our tiny model prevail?
While my model is training, let’s take a moment to pause and reflect on the process so far, its thorns and roses, and make a few more Bert puns while we’re at it!
You can’t have RAG without a retriever, but what exactly is a retriever? And what kind of retriever am I going to use? This post will take a trip with BERT, and talk about some recent innovations with the model. Let’s jump in! Parts of a retriever All retrievers have at least three things in common: 1. A corpus of information is stored somewhere, somehow 2. When presented with an incoming query, they do some stuff to process it 3.
OK, here’s my question: Why do all these RAG apps use giant f**king models?? Default Langchain RAG tutorial - ChatGPT 3.5 Turbo (175B param) Random RAG tutorial from Google - ChatGPT 3.5 Turbo (175B param) Another tutorial - Llama 2 (70B param) There are more, but you get the point. I’m worked up about this because these foundation models- chatGPT, Claude, Bard, the whole lot- are so freaking powerful that using them for information retrieval is super inefficient.