bge m3 GGUF EU AI Act Compliance Profile

bge-m3-GGUF

Model creator: BAAI<br/> Original model: bge-m3<br/> 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 weights (similar to the BM25) without any additional cost when generate dense embeddings. To use hybrid retrieval, you can refer to Vespa and Milvus.

• As cross-encoder models, re-ranker demonstrates higher accuracy than bi-encoder embedding model

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