MNLP_M3_document_encoder safetensors download | Hugging Bay

Q: What is this model?

adamezzaim/MNLP_M3_document_encoder is an embedding model for text embeddings published by adamezzaim, added to Hugging Bay Jun 30, 2026. Model from Hugging Face for feature extraction: adamezzaim/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: 4.02 GB. The recommended download, model.safetensors, is 1.34 GB.

Q: What can I download here?

Hosted here: at least 8 listed files; full count and size unknown. Full repo upstream: 4.02 GB. The original release is at https://huggingface.co/adamezzaim/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-adamezzaim-mnlp-m3-document-encoder/safety on Hugging Bay.

Q: Will adamezzaim/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.

adamezzaim

MNLP_M3_document_encoder

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

Other ways

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8 hosted of 14 listed · Hashes recorded. Date not recorded.

Selected downloadmodel.safetensors1.34 GB · 1 file Download · 1.34 GB (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	1.34 GB	`45e19549…32f64ae7` `45e1954914e29bd74080e6c1510165274ff5279421c89f76c418878732f64ae7`	Hosted	Herunterladen
`onnx/model.onnx`	1.34 GB	`69ed3f81…0df2a2ce` `69ed3f810d3b6d13f70dff9ca89966f39c0a0e877fb88211be7bcc070df2a2ce`	Upstream only	File metadata
`pytorch_model.bin`	1.34 GB	`51e14ed9…b31c1ac4` `51e14ed95fb897ba8eee3c6d9b5fb4323229a897caaf34053c1b7639b31c1ac4`	Upstream only	File metadata
Tokenizer & config
`1_Pooling/config.json`	191 bytes	—	Upstream only	File metadata
`config.json`	779 bytes	`446712fa…07660896` `446712fac367857b4b1302762fe1cd7bfa8b3c4b77b4dc5d77c4025407660896`	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`	94.6 KB	—	Upstream only	File metadata

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Linux

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Windows

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

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MNLP_M3_document_encoder

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