MNLP_M2_document_encoder safetensors download | Hugging Bay

Q: What is this model?

pmdlt/MNLP_M2_document_encoder is an embedding model for sentence similarity and retrieval published by pmdlt, added to Hugging Bay Sep 24, 2026. Model from Hugging Face for sentence similarity: pmdlt/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: 91.8 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: 91.8 MB. The original release is at https://huggingface.co/pmdlt/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-pmdlt-mnlp-m2-document-encoder/safety on Hugging Bay.

Q: Will pmdlt/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.

pmdlt

MNLP_M2_document_encoder

Can I run MNLP_M2_document_encoder on my computer?

It depends on your memory, processor, file format and runtime. Hugging Bay offers 8 hosted files; check the file list for the exact selection. A file-size fit is an estimate, not a successful run or speed promise. Read the license before use.

MNLP_M2_document_encoder is an embedding model for sentence similarity and retrieval. Model weights are learned parameters; a runtime loads them. Hosting files does not prove that a complete model runs.

As of the availability check on 2026-10-22.

Recorded SHA-256 for model.safetensors: 1377e9af0ca0b016a9f2aa584d6fc71ab3ea6804fae21ef9fb1416e2944057ac. File evidence · Machine-readable facts · Run evidence and any recorded receipts · Proof Lab measurement policy. A checksum verifies byte identity, not safety; this answer asserts no measured runtime result.

Updated 2026-10-21 · Page text: As of 2026-10-21 · Content revision

Model metadata: As of 2026-09-24 at the model source. Popularity counters are source-reported, not measured by Hugging Bay. Availability checked 2026-10-22.

pmdlt/MNLP_M2_document_encoder

Model details

sentence similarity and retrievalLicense: apache-2.0SHA-256 recorded for 8 of 12 filessafetensors

Content controls: not assessed.

The catalog lists this model for checking whether two sentences mean similar things. Catalog sources: license, cardMetadata.license, task, cardMetadata.pipelineTag.

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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-pmdlt-mnlp-m2-document-encoder/model.safetensors",
    "model.safetensors",
)

Other ways

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8 hosted of 12 listed · Hashes recorded. Date not recorded.

Selected downloadmodel.safetensors91 MB · 1 file Download · 91 MB (1 file)

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Check your download →

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

8 hosted of 12 listed

Hashes recorded. Date not recorded.

File	Größe	SHA-256	Quelle	Herunterladen
Weights
`model.safetensors` Default	90.9 MB	`1377e9af…944057ac` `1377e9af0ca0b016a9f2aa584d6fc71ab3ea6804fae21ef9fb1416e2944057ac`	Hosted	Herunterladen
Tokenizer & config
`1_Pooling/config.json`	296 bytes	—	Upstream only	File metadata
`config.json`	617 bytes	`1e634130…ea242692` `1e6341309a0ecdb315759c9582825b4af1b5cb00227b07ed73a3575bea242692`	Hosted	Herunterladen
`config_sentence_transformers.json`	205 bytes	`09c0f8dc…c37bbdfe` `09c0f8dca2d8cdc0a43dcb43bf779443b4af49b055d42bf21ba8b5dcc37bbdfe`	Hosted	Herunterladen
`sentence_bert_config.json`	53 bytes	`fc1993fd…2a9cd4b7` `fc1993fde0a95c24ec6c022539d41cf6e2f7c9721e5415d6fb6897472a9cd4b7`	Hosted	Herunterladen
`special_tokens_map.json`	695 bytes	`5d5b662e…a350503a` `5d5b662e421ea9fac075174bb0688ee0d9431699900b90662acd44b2a350503a`	Hosted	Herunterladen
`tokenizer.json`	711.7 KB	`da0e7993…4d2c62a0` `da0e79933b9ed51798a3ae27893d3c5fa4a201126cef75586296df9b4d2c62a0`	Hosted	Herunterladen
`tokenizer_config.json`	1.46 KB	`c128cee6…017ca1f0` `c128cee663cc23ce44cf253471d2a538d1623c7c3ead5e465b13519a017ca1f0`	Hosted	Herunterladen
`vocab.txt`	231.5 KB	—	Upstream only	File metadata
`modules.json`	349 bytes	`84e40c8e…7cf642cf` `84e40c8e006c9b1d6c122e02cba9b02458120b5fb0c87b746c41e0207cf642cf`	Hosted	Herunterladen
Docs
`.gitattributes`	1.52 KB	—	Upstream only	File metadata
`README.md`	10.5 KB	—	Upstream only	File metadata

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In the folder where you saved model.safetensors, run:

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shasum -a 256 -- 'model.safetensors'

Linux

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Windows

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MNLP_M2_document_encoder

Can I run MNLP_M2_document_encoder on my computer?

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