MNLP_M2_document_encoder safetensors download | Hugging Bay

Q: What is this model?

pgilliar/MNLP_M2_document_encoder is an embedding model for sentence similarity and retrieval published by pgilliar, added to Hugging Bay Sep 24, 2026. Model from Hugging Face for sentence similarity: pgilliar/MNLP_M2_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: 976.9 MB. The recommended download, model.safetensors, is 90.9 MB.

Q: What can I download here?

Hosted here: at least 8 listed files; full count and size unknown. Full repo upstream: 976.9 MB. The original release is at https://huggingface.co/pgilliar/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-pgilliar-mnlp-m2-document-encoder/safety on Hugging Bay.

Q: Will pgilliar/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.

pgilliar

MNLP_M2_document_encoder

pgilliar/MNLP_M2_document_encoder

sentence similarity and retrievalLicense: apache-2.0Hashes recordedsafetensors · ONNX · BIN · H5

Content controls: not assessed.

Model from Hugging Face for sentence similarity: pgilliar/MNLP_M2_document_encoder · sentence transformers

View as JSON →

Machine-readable artifact endpoints

Not run yet

MNLP_M2_document_encoder

Machine-readable artifact endpoints

Cookies