MNLP_M3_document_encoder safetensors download | Hugging Bay

Q: What is this model?

Jenjamin3000/MNLP_M3_document_encoder is an embedding model for sentence similarity and retrieval published by Jenjamin3000, added to Hugging Bay Sep 24, 2026. Model from Hugging Face for sentence similarity: Jenjamin3000/MNLP_M3_document_encoder · sentence transformers

Q: Can I use it commercially?

It is released under the apache-2.0 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: 3.82 GB. The recommended download, model.safetensors, is 438 MB.

Q: What can I download here?

Hosted here: at least 8 listed files; full count and size unknown. Full repo upstream: 3.82 GB. The original release is at https://huggingface.co/Jenjamin3000/MNLP_M3_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-jenjamin3000-mnlp-m3-document-encoder/safety on Hugging Bay.

Q: Will Jenjamin3000/MNLP_M3_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.

Jenjamin3000

MNLP_M3_document_encoder

Jenjamin3000/MNLP_M3_document_encoder

Model details

sentence similarity and retrievalLicense: apache-2.0SHA-256 recorded for 20 of 28 filessafetensors · ONNX · BIN

Content controls: not assessed.

Listed for comparing sentences; for developers building text similarity tools. Catalog sources: license, cardMetadata.license, task, cardMetadata.pipelineTag.

View as JSON →

Your next step

Use it from code

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-jenjamin3000-mnlp-m3-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 28 listed · Hashes recorded. Date not recorded.

Selected downloadmodel.safetensors438 MB · 1 file Download · 438 MB (1 file)

Open-file help

Content controls: not assessed.

Check your download →

Compare your file’s SHA-256 with the recorded hash.