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Cataloged from furiosa-ai/harrier-oss-v1-0.6b
All models by furiosa-ai →This repository contains microsoft/harrier-oss-v1-0.6b
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 Sentence Transformers and
Transformers); for usage with those, see the upstream
microsoft/harrier-oss-v1-0.6b model card.
Harrier OSS v1 is a family of multilingual text-embedding models developed by
Microsoft. The 0.6B model uses a dense, decoder-only Qwen3 architecture, but it
is trained with Harrier's own multilingual, instruction-aware embedding recipe
rather than the Qwen3-Embedding training recipe. It produces 1,024-dimensional
embeddings through last-token pooling and L2 normalization. It is designed for
retrieval, clustering, semantic similarity, classification, bitext mining, and
reranking. Its intended use is the same as the upstream
microsoft/harrier-oss-v1-0.6b,
and it is released under the MIT License.
Qwen3ModelNo quantization — the model runs in its native BF16 precision.
On RNGD, harrier-oss-v1-0.6b 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 an OpenAI-compatible /v1/embeddings endpoint. Harrier is
instruction-aware: prepend a one-sentence task description to each query in the
Instruct: ...\nQuery: ... format, and do not add the instruction to documents.
For more details, see the
base model card.
Request embeddings with curl:
Because the endpoint is OpenAI-compatible, you can also use the OpenAI Python client:
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:
furiosa-llm serve) — full OpenAI-compatible API reference, including the Embeddings APImicrosoft/harrier-oss-v1-0.6b — upstream model card# Launch the server, listening on port 8000 by defaultfuriosa-llm serve furiosa-ai/harrier-oss-v1-0.6bINFO: 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/embeddings \ -H "Content-Type: application/json" \ -d '{ "model": "furiosa-ai/harrier-oss-v1-0.6b", "input": [ "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: summit define", "Definition of summit: the highest point of a mountain." ] }' \ | python -m json.toolfrom openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
query = ( "Instruct: Given a web search query, retrieve relevant passages that answer the query\n" "Query: summit define")document = "Definition of summit: the highest point of a mountain."
response = client.embeddings.create( model="furiosa-ai/harrier-oss-v1-0.6b", input=[query, document],)
for data in response.data: print(f"Index {data.index}: {len(data.embedding)} dimensions")from furiosa_llm import LLM
query = ( "Instruct: Given a web search query, retrieve relevant passages that answer the query\n" "Query: summit define")document = "Definition of summit: the highest point of a mountain."
with LLM("furiosa-ai/harrier-oss-v1-0.6b") as llm: outputs = llm.embed([query, document]) 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}")