Qwen3-Embedding

Qwen3-Embedding#

The Qwen3-Embedding series is a family of text-embedding models built on the Qwen3 dense transformer backbone. They map text into dense vector representations for semantic search, retrieval, and similarity matching, with strong multilingual coverage.

FuriosaAI publishes pre-compiled builds of the Qwen3-Embedding models under the furiosa-ai organization on the Hugging Face Hub, each shipping a Furiosa Executable Bundle (FXB) for running it on FuriosaAI RNGD with Furiosa-LLM.

For the related reranking model see Qwen3-Reranker; for the dense Qwen3 chat models see Qwen3 (dense).

Variants#

Model

Quantization

RNGD cards

Notes

furiosa-ai/Qwen3-Embedding-8B

None (16-bit)

1

8B text embedding

  • Architecture: Qwen3 (dense), Qwen3Model

  • Task: Embedding

  • Input / Output: Text / Embeddings (vector)

  • Quantization: No quantization — the model runs in its native 16-bit precision.

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 offline through the Furiosa-LLM Python API or online through the OpenAI-compatible server.

Python API#

Load the artifact and call embed to obtain dense vectors:

from furiosa_llm import LLM

llm = LLM.from_artifacts("furiosa-ai/Qwen3-Embedding-8B")
embeddings = llm.embed(["Hello, world!", "How are you?"])

Online server#

The server exposes an OpenAI-compatible /v1/embeddings endpoint. Launch it the same way as any other model:

# Launch the server, listening on port 8000 by default
furiosa-llm serve furiosa-ai/Qwen3-Embedding-8B

Once it is ready, request embeddings with curl:

curl http://localhost:8000/v1/embeddings \
    -H "Content-Type: application/json" \
    -d '{
    "model": "furiosa-ai/Qwen3-Embedding-8B",
    "input": ["Hello, world!", "How are you?"]
    }' \
    | 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/Qwen3-Embedding-8B",
    input=["Hello, world!", "How are you?"],
)

for data in response.data:
    print(f"Index {data.index}: {len(data.embedding)} dimensions")

Learn more#