No official torrent yet β requesting it tells the flock what to host next. You cannot seed a catalog-only page.
Cataloged from furiosa-ai/e5-mistral-7b-instruct
All models by furiosa-ai βThis repository contains
intfloat/e5-mistral-7b-instruct
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
intfloat/e5-mistral-7b-instruct
model card.
E5-Mistral-7B-Instruct is an instruction-aware text-embedding model built by
fine-tuning the dense Mistral-7B-v0.1 decoder backbone. It produces
4,096-dimensional dense embeddings through last-token pooling and L2
normalization for retrieval and semantic similarity tasks. Although it was
fine-tuned on multilingual data, the upstream model card recommends it
primarily for English. Its intended use is the same as the upstream
intfloat/e5-mistral-7b-instruct,
and it is released under the MIT License.
MistralModelNo quantization β the model uses its upstream FP16 weights.
On RNGD, e5-mistral-7b-instruct 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. E5-Mistral
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
upstream 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) β full OpenAI-compatible API reference, including the Embeddings APIintfloat/e5-mistral-7b-instruct β upstream model card# Launch the server, listening on port 8000 by defaultfuriosa-llm serve furiosa-ai/e5-mistral-7b-instructINFO: 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/e5-mistral-7b-instruct", "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/e5-mistral-7b-instruct", 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/e5-mistral-7b-instruct") 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}")