MNLP_M2_document_encoder safetensors download | Hugging Bay

Q: What is this model?

arnaultsta/MNLP_M2_document_encoder is an embedding model for text embeddings published by arnaultsta, added to Hugging Bay Jun 30, 2026. Model from Hugging Face for feature extraction: arnaultsta/MNLP_M2_document_encoder · sentence transformers

Q: Can I use it commercially?

It is released under the mit license. Commercial use is allowed. Permissive open license: commercial use, modification, and redistribution are permitted.

Q: How big is it?

Hosted here: at least 8 listed files; full count and size unknown. Full repo upstream: 401.1 MB. The recommended download, model.safetensors, is 133.5 MB.

Q: What can I download here?

Hosted here: at least 8 listed files; full count and size unknown. Full repo upstream: 401.1 MB. The original release is at https://huggingface.co/arnaultsta/MNLP_M2_document_encoder.

Q: File pattern scans

Automated file-pattern scans flagged nothing; not a safety guarantee. 8 hosted files carry a published SHA-256 you can re-hash. The machine-readable verdict is at /api/artifacts/hf-model-arnaultsta-mnlp-m2-document-encoder/safety on Hugging Bay.

Q: Will arnaultsta/MNLP_M2_document_encoder run on my computer?

We can’t check the fit; we don’t know your machine. We don’t have your machine’s specs, so no check ran and no verdict is shown. Answer two quick questions and we’ll estimate, from the memory you tell us, whether it fits your machine. Or skip it; you can still download; fit isn’t checked.

arnaultsta

MNLP_M2_document_encoder

arnaultsta/MNLP_M2_document_encoder

Model details

text embeddingsLicense: mitSHA-256 recorded for 10 of 14 filessafetensors · ONNX · BIN

Content controls: not assessed.

Listed for turning text into vectors; for developers building text similarity tools. Catalog sources: license, cardMetadata.license, cardMetadata.libraryName, modality, task, cardMetadata.pipelineTag.

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This Python snippet downloads the selected file. Running the model needs its own library and setup.

from urllib.request import urlretrieve

urlretrieve(
    "https://huggingbay.xyz/api/downloads/hf-model-arnaultsta-mnlp-m2-document-encoder/model.safetensors",
    "model.safetensors",
)

Other ways

Run it on your computerOpen in Hugging Bay appSend to a computerApp links support one GGUF file.

8 hosted of 14 listed · Hashes recorded. Date not recorded.

Selected downloadmodel.safetensors133 MB · 1 file Download · 133 MB (1 file)

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