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Cataloged from furiosa-ai/bge-reranker-v2-m3
All models by furiosa-ai →This repository contains
BAAI/bge-reranker-v2-m3
together with a Furiosa Executable Bundle (FXB) for running it on
FuriosaAI RNGD with
Furiosa-LLM.
The same model also runs on other frameworks (such as FlagEmbedding, Sentence
Transformers, and Transformers); for usage with those, see the upstream
BAAI/bge-reranker-v2-m3
model card.
BGE Reranker v2 M3 is a multilingual cross-encoder reranking model developed by
BAAI on the XLM-RoBERTa encoder architecture. Given a query and a set of
candidate documents, it produces relevance scores used to reorder retrieval
results in retrieval-augmented generation (RAG) and search pipelines. Its
intended use is the same as the upstream
BAAI/bge-reranker-v2-m3,
and it is released under the
Apache License 2.0.
XLMRobertaForSequenceClassificationNo lower-bit quantization is applied. This repository retains the upstream FP32 checkpoint weights; Furiosa-LLM downcasts them to BF16 for W16A16 execution by the FXB.
On RNGD, bge-reranker-v2-m3 runs with a tensor-parallel size of 8 PEs, which maps to a single RNGD card (8 PEs per card).
To run this model with Furiosa-LLM, follow the examples below after installing Furiosa-LLM and its prerequisites. You can use the model either online through the OpenAI-compatible server or offline through the Furiosa-LLM Python API.
Serve the model by passing its furiosa-ai/<repo> identifier:
When the server is ready, you will see:
The server exposes a /v1/rerank endpoint compatible with the Cohere and Jina
rerank APIs. Send a query and candidate documents with curl; the server
returns the documents ordered by their sigmoid-normalized relevance_score:
You can pass top_n to keep only the most relevant documents. To score
query-document pairs directly instead of reranking, the server also exposes a
/v1/score endpoint.
For offline use, load the model with the LLM constructor (the FXB shipped in
the repo is discovered automatically) and call score with a query and the
candidate documents to obtain relevance scores:
furiosa-llm serve) — full OpenAI-compatible API reference, including the Rerank and Score APIsBAAI/bge-reranker-v2-m3 — upstream model card# Launch the server, listening on port 8000 by defaultfuriosa-llm serve furiosa-ai/bge-reranker-v2-m3INFO: Started server process [27507]INFO: Waiting for application startup.INFO: Application startup complete.INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)curl http://localhost:8000/v1/rerank \ -H "Content-Type: application/json" \ -d '{ "model": "furiosa-ai/bge-reranker-v2-m3", "query": "What is deep learning?", "documents": [ "Deep learning is a subset of machine learning using neural networks.", "Python is a popular programming language for data science.", "Neural networks are inspired by biological neural networks." ] }' \ | python -m json.toolfrom furiosa_llm import LLM
query = "What is deep learning?"documents = [ "Deep learning is a subset of machine learning using neural networks.", "Python is a popular programming language for data science.",]
with LLM("furiosa-ai/bge-reranker-v2-m3") as llm: outputs = llm.score(query, documents) for document, output in zip(documents, outputs, strict=True): print(f"score={output.outputs.score:.4f} {document}")