bge-m3
This repository contains a BF16 build of
BAAI/bge-m3, together with a Furiosa
Executable Bundle (FXB) for running it on FuriosaAI RNGD
with Furiosa-LLM.
The base model also runs on other frameworks (such as FlagEmbedding, Sentence
Transformers, and Transformers); for usage with those, see the upstream
BAAI/bge-m3 model card.
Overview
BGE-M3 is a multilingual text-embedding model developed by BAAI on the
XLM-RoBERTa encoder architecture. The upstream model unifies dense, sparse
lexical, and multi-vector retrieval. Furiosa-LLM exposes its 1,024-dimensional,
CLS-pooled dense embeddings for semantic search, retrieval, and similarity
matching. Its intended use is the same as the upstream
BAAI/bge-m3, and it is released under the
MIT License.
- Architecture: XLM-RoBERTa (dense encoder),
XLMRobertaModel - Input / Output: Text / Dense embeddings (vector)
- Supported Inference Engine: Furiosa LLM
- Supported Hardware: FuriosaAI RNGD
Quantization
The FuriosaAI build uses BF16 weights without a lower-bit quantization scheme.
Parallelism Strategy
On RNGD, bge-m3 runs with a tensor-parallel size of 8 PEs, which maps to a single RNGD card (8 PEs per card).
Usage
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.
Launch the server
Serve the model by passing its furiosa-ai/<repo> identifier:
# Launch the server, listening on port 8000 by default
furiosa-llm serve furiosa-ai/bge-m3
When the server is ready, you will see:
INFO: 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)
Basic Usage
The server exposes an OpenAI-compatible /v1/embeddings endpoint. BGE-M3 does
not require an instruction prefix for queries. Request dense embeddings with
curl:
curl http://localhost:8000/v1/embeddings \
-H "Content-Type: application/json" \
-d '{
"model": "furiosa-ai/bge-m3",
"input": [
"What is BGE-M3?",
"BGE-M3 is a multilingual text-embedding model."
]
}' \
| python -m json.tool
Because the endpoint is OpenAI-compatible, you can also use the OpenAI Python client:
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
response = client.embeddings.create(
model="furiosa-ai/bge-m3",
input=[
"What is BGE-M3?",
"BGE-M3 is a multilingual text-embedding model.",
],
)
for data in response.data:
print(f"Index {data.index}: {len(data.embedding)} dimensions")
Advanced Usage
For offline use, load the model with the LLM constructor (the FXB shipped in
the repo is discovered automatically) and call embed to obtain L2-normalized
dense vectors. Their dot product is therefore the cosine similarity:
from furiosa_llm import LLM
texts = [
"What is BGE-M3?",
"BGE-M3 is a multilingual text-embedding model.",
]
with LLM("furiosa-ai/bge-m3") as llm:
outputs = llm.embed(texts)
embeddings = [output.outputs.embedding for output in outputs]
similarity = sum(a * b for a, b in zip(*embeddings, strict=True))
print(f"Cosine similarity: {similarity:.4f}")
Learn more
- Furiosa-LLM Server (
furiosa-llm serve) — full OpenAI-compatible API reference, including the Embeddings API - Furiosa-LLM — Furiosa-LLM documentation and API reference
BAAI/bge-m3— upstream model card