bge m3 GGUF Model creator : BAAI Original model : bge m3 GGUF quantization : based on llama.cpp release 61408e7f For more details please refer to our github repo: https://github.com/FlagOpen/FlagEmbedding BGE M3 (paper, code) In this project, we introduce BGE M3, which is distinguished for its versatility in Multi Functionality, Multi Linguality, and Multi Granularity. Multi Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi vector retrieval, and sparse retrieval. Multi Linguality: It can support more than 100 working languages. Multi Granularity: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to 8192 tokens. Some suggestions for retrieval pipeline in RAG We recommend to use the following pipeline: hybrid retrieval + re ranking. Hybrid retrieval leverages the strengths of various methods, offering higher accuracy and stronger generalization capabilities. A classic example: using both embedding retrieval and the BM25 algorithm. Now, you can try to use BGE M3, which supports both embedding and sparse retrieval. This allows you to obtain token…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy