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stephantulkens/
NIFE-mxbai-embed-large-v1

Name: NIFE-mxbai-embed-large-v1
Availability: InStock

safetensors

mixedbread-ai/mxbai-embed-large-v1

finetune

mit

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NIFE mxbai embed large v1

This is a NIFE model finetuned from mixedbread-ai/mxbai-embed-large-v1. It is fully aligned with its base model mixedbread-ai/mxbai-embed-large-v1, and can be used to perform Inference Free querying using an index made by this model. It can also be used in standalone mode.

Model Details

Model Description

Model Type: Static Model
Base model: mixedbread-ai/mxbai-embed-large-v1
Output Dimensionality: 1024 dimensions

Full Model Architecture

SentenceTransformer(
  (0): StaticEmbedding(
    (embedding): EmbeddingBag(107962, 1024, mode='mean')
  )
  (1): Normalize()
)

Usage

Direct Usage (NIFE)

First install the NIFE library

pip install -U pynife

Then you can run the model as follows:

python

from nife import load_as_router

model = load_as_router("stephantulkens/NIFE-mxbai-embed-large-v1")

query = "What is the capital of France?"
query_embeddings = model.encode_query(query)

# Five locales near France
index_doc = model.encode_document(["Paris is the largest city in France", "Lyon is pretty big", "Antwerp is really great, and in Belgium", "Berlin is pretty gloomy in winter", "France is a country in Europe"])

similarity = model.similarity(query_vec, index_doc)
print(similarity)
# It correctly retrieved the document containing the statement about paris.
# tensor([[0.7065, 0.5012, 0.3596, 0.2765, 0.6648]])

Training Details

Citation

BibTeX

bibtex

@software{Tulkens2025pyNIFE,
  author       = {St\'{e}phan Tulkens},
  title        = {pyNIFE: nearly inference free embeddings in python},
  year         = {2025},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.17512919},
  url          = {https://github.com/stephantulkens/pynife},
  license      = {MIT},
  note         = {If you use NIFE, please cite the following work.},
}