We explore a general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) — models which combine pre-trained parametric and non-parametric memory for language …

Here's the bottom line (BLUF for you DoD folks): I'm interested in hearing what models you are using for high quality embeddings. I'm interested in RAG retrieval.

Mar 26, 2025 · In this tutorial, we've built a complete RAG pipeline using Transformer Lab documentation, fine-tuned an embedding model, and quantitatively compared performance …

May 15, 2025 · This post explored building a Retrieval-Augmented Generation (RAG) system using transformer models from the Hugging Face library. We’ve implemented each system …

Jul 5, 2024 · Recap: Embedding models create fixed-length vector representations of text, focusing on semantic meaning for tasks like similarity comparison.

Nov 30, 2024 · This article starts with the transformers and looks at its shortcomings as an embedding model. It then gives an overview of BERT and deep dives into Sentence BERT …

Nov 15, 2024 · You are recommended to use embedding models that are deployed in watsonx: the IBM-developed slate models or the third party models listed below. Please read through …

Nov 3, 2023 · We have a variety of embedding models to choose from, including OpenAI, CohereAI, and open-source sentence transformers. Additionally, there are several rerankers …

Aug 30, 2024 · In this tutorial, we will see why embeddings are important for RAG, and how to choose the best embedding model for your RAG application.

Apr 10, 2025 · Learn how to choose the best & most optimal embeddings for your RAG models based on the applications, with our comprehensive selection guide.