MNLP_M3_document_encoder safetensors download | Hugging Bay

Q: What is this model?

ushakov15/MNLP_M3_document_encoder is an embedding model for text embeddings published by ushakov15, added to Hugging Bay Sep 24, 2026. Model from Hugging Face for feature extraction: ushakov15/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 7 listed files; full count and size unknown. Full repo upstream: 1.21 GB. The recommended download, model.safetensors, is 1.19 GB.

Q: What can I download here?

Hosted here: at least 7 listed files; full count and size unknown. Full repo upstream: 1.21 GB. The original release is at https://huggingface.co/ushakov15/MNLP_M3_document_encoder.

Q: File pattern scans

Automated file-pattern scans flagged nothing; not a safety guarantee. 7 hosted files carry a published SHA-256 you can re-hash. The machine-readable verdict is at /api/artifacts/hf-model-ushakov15-mnlp-m3-document-encoder/safety on Hugging Bay.

Q: Will ushakov15/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.

ushakov15

MNLP_M3_document_encoder

ushakov15/MNLP_M3_document_encoder

Model details

text embeddingsLicense: apache-2.0SHA-256 recorded for 7 of 12 filessafetensors

Content controls: not assessed.

The catalog lists this model for turning text into vectors. Catalog sources: license, cardMetadata.license, cardMetadata.libraryName, modality, task, cardMetadata.pipelineTag.

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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-ushakov15-mnlp-m3-document-encoder/model.safetensors",
    "model.safetensors",
)

Other ways

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

Selected downloadmodel.safetensors1.19 GB · 1 file Download · 1.19 GB (1 file)

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

7 hosted of 12 listed

Hashes recorded. Date not recorded.

File	Taille	SHA-256	Source	Télécharger
Weights
`model.safetensors` Default	1.19 GB	`0437e45c…d48e23fd` `0437e45c94563b09e13cb7a64478fc406947a93cb34a7e05870fc8dcd48e23fd`	Hosted	Télécharger
Tokenizer & config
`1_Pooling/config.json`	313 bytes	—	Upstream only	File metadata
`config.json`	727 bytes	`b5bf1f51…a19feaa9` `b5bf1f51fc45be473a54718cef92448d90a1be001bf9b9a44b8c7f10a19feaa9`	Hosted	Télécharger
`config_sentence_transformers.json`	215 bytes	`10667c72…105c6961` `10667c72ddb772627bf1780cb7f86af8e2ae0032b8c243c731172064105c6961`	Hosted	Télécharger
`generation_config.json`	117 bytes	`28396d42…dbfb65fb` `28396d421a2108acce96383f6a7de78008f7f1b17f807958f3c14c51dbfb65fb`	Hosted	Télécharger
`merges.txt`	1.67 MB	—	Upstream only	File metadata
`tokenizer.json`	11.4 MB	`def76fb0…f8b4b50a` `def76fb086971c7867b829c23a26261e38d9d74e02139253b38aeb9df8b4b50a`	Hosted	Télécharger
`tokenizer_config.json`	9.71 KB	`253153d0…f517e6a0` `253153d0738ceb4c668d2eff957714dd2bea0b56de772a9fdccd96cbf517e6a0`	Hosted	Télécharger
`vocab.json`	2.78 MB	—	Upstream only	File metadata
`modules.json`	349 bytes	`84e40c8e…7cf642cf` `84e40c8e006c9b1d6c122e02cba9b02458120b5fb0c87b746c41e0207cf642cf`	Hosted	Télécharger
Docs
`.gitattributes`	1.57 KB	—	Upstream only	File metadata
`README.md`	16.3 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

sha256sum -- 'model.safetensors'

Windows

Get-FileHash -LiteralPath 'model.safetensors' -Algorithm SHA256

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MNLP_M3_document_encoder

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