MNLP_M3_document_encoder safetensors download | Hugging Bay

Q: What is this model?

arnaultsta/MNLP_M3_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_M3_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_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-arnaultsta-mnlp-m3-document-encoder/safety on Hugging Bay.

Q: Will arnaultsta/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.

arnaultsta

MNLP_M3_document_encoder

Can I run MNLP_M3_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_M3_document_encoder is an embedding model for text embeddings. Model weights are learned parameters; a runtime loads them. A hosted file is an individual file Hugging Bay can serve. SHA-256 identifies exact file bytes. The availability date above applies to this file record, not the upstream metadata update date.

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

Recorded SHA-256 for model.safetensors: 3c9f31665447c8911517620762200d2245a2518d6e7208acc78cd9db317e21ad. Exact file evidence · Machine-readable source facts · Run evidence and any recorded receipts. A checksum fingerprints only the named file bytes; it does not prove safety, quality, a complete multi-file selection or a successful run.

Updated 2026-10-21 · Page-copy source revision 8871bab84e1cf80aea1686a944ec335078a6c036 · Source files: apps/web/lib/model-page-template.mjs, apps/web/lib/model-page-v3.mjs, apps/web/lib/aeo-page.mjs · Full revision record

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.

arnaultsta/MNLP_M3_document_encoder

Model details

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

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

Other ways

This file cannot open in the app. The app supports one GGUF file at a time.

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

8 hosted of 14 listed

Hashes recorded. Date not recorded.

File	Größe	SHA-256	Quelle	Herunterladen
Weights
`model.safetensors` Default	133.5 MB	`3c9f3166…317e21ad` `3c9f31665447c8911517620762200d2245a2518d6e7208acc78cd9db317e21ad`	Hosted	Herunterladen
`onnx/model.onnx`	133.1 MB	`828e1496…9940cf35` `828e1496d7fabb79cfa4dcd84fa38625c0d3d21da474a00f08db0f559940cf35`	Upstream only	File metadata
`pytorch_model.bin`	133.5 MB	`84868a67…31728b88` `84868a67d847f7f37d1df745b73a4c20b5bc0a797c87a23b265f8a0131728b88`	Upstream only	File metadata
Tokenizer & config
`1_Pooling/config.json`	190 bytes	—	Upstream only	File metadata
`config.json`	743 bytes	`094f8e89…f3084750` `094f8e891b932f2000c92cfc663bac4c62069f5d8af5b5278c4306aef3084750`	Hosted	Herunterladen
`config_sentence_transformers.json`	124 bytes	`940d5f50…8bdd7c56` `940d5f50db195fa6e5e6a4f122c095f77880de259d74b14a65779ed48bdd7c56`	Hosted	Herunterladen
`sentence_bert_config.json`	52 bytes	`84e39fda…2a693cbf` `84e39fda68ccbff05bfa723ae9c0e70e23e2ec373b76e0f8c6e71af72a693cbf`	Hosted	Herunterladen
`special_tokens_map.json`	125 bytes	`b6d346be…76237ee3` `b6d346be366a7d1d48332dbc9fdf3bf8960b5d879522b7799ddba59e76237ee3`	Hosted	Herunterladen
`tokenizer.json`	711.4 KB	`d241a60d…1f9e5c66` `d241a60d5e8f04cc1b2b3e9ef7a4921b27bf526d9f6050ab90f9267a1f9e5c66`	Hosted	Herunterladen
`tokenizer_config.json`	366 bytes	`9261e7d7…76172ab3` `9261e7d79b44c8195c1cada2b453e55b00aeb81e907a6664974b4d7776172ab3`	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`	94.8 KB	—	Upstream only	File metadata

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MNLP_M3_document_encoder

Can I run MNLP_M3_document_encoder on my computer?

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