MNLP_M3_document_encoder safetensors download | Hugging Bay

Q: What is this model?

Jenjamin3000/MNLP_M3_document_encoder is an embedding model for sentence similarity and retrieval published by Jenjamin3000, added to Hugging Bay Sep 24, 2026. Model from Hugging Face for sentence similarity: Jenjamin3000/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 8 listed files; full count and size unknown. Full repo upstream: 3.82 GB. The recommended download, model.safetensors, is 438 MB.

Q: What can I download here?

Hosted here: at least 8 listed files; full count and size unknown. Full repo upstream: 3.82 GB. The original release is at https://huggingface.co/Jenjamin3000/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-jenjamin3000-mnlp-m3-document-encoder/safety on Hugging Bay.

Q: Will Jenjamin3000/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.

Jenjamin3000

MNLP_M3_document_encoder

Jenjamin3000/MNLP_M3_document_encoder

Model details

sentence similarity and retrievalLicense: apache-2.0SHA-256 recorded for 20 of 28 filessafetensors · ONNX · BIN

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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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-jenjamin3000-mnlp-m3-document-encoder/model.safetensors",
    "model.safetensors",
)

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8 hosted of 28 listed · Hashes recorded. Date not recorded.

Selected downloadmodel.safetensors438 MB · 1 file Download · 438 MB (1 file)

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

8 hosted of 28 listed

Hashes recorded. Date not recorded.

File	Size	SHA-256	Source	Download
Weights
`model.safetensors` Default	438 MB	`78c0197b…05615658` `78c0197b6159d92658e319bc1d72e4c73a9a03dd03815e70e555c5ef05615658`	Hosted	Download
`onnx/model.onnx`	435.8 MB	`74187b16…c47941bd` `74187b16d9c946fea252e120cfd7a12c5779d8b8b86838a2e4c56573c47941bd`	Upstream only	File metadata
`onnx/model_O1.onnx`	435.7 MB	`5c0b4700…96e384b0` `5c0b47004076ab40bf15a2c52b98a53e985ebb84faaeeb6d2551768f96e384b0`	Upstream only	File metadata
`onnx/model_O2.onnx`	435.7 MB	`14d01256…708c1793` `14d01256f5f3d2245b15b596173bca4367c9405fde5700dd7fb4e110708c1793`	Upstream only	File metadata
`onnx/model_O3.onnx`	435.7 MB	`dd555107…9bbcb310` `dd55510706038d0817b7d41bf2078f01472e4865190584ad624e8ab79bbcb310`	Upstream only	File metadata
`onnx/model_O4.onnx`	217.9 MB	`cab2a541…3e635310` `cab2a54139fc4fd5b8e2a23cb5729ee28dc44cfde685ad3356d533653e635310`	Upstream only	File metadata
`onnx/model_qint8_arm64.onnx`	110.1 MB	`c392a9c5…666d197f` `c392a9c545c7d4438a16fed8287a76a576b27eaf029c1c23bbf78a7a666d197f`	Upstream only	File metadata
`onnx/model_qint8_avx512.onnx`	110.1 MB	`c392a9c5…666d197f` `c392a9c545c7d4438a16fed8287a76a576b27eaf029c1c23bbf78a7a666d197f`	Upstream only	File metadata
`onnx/model_qint8_avx512_vnni.onnx`	110.1 MB	`c392a9c5…666d197f` `c392a9c545c7d4438a16fed8287a76a576b27eaf029c1c23bbf78a7a666d197f`	Upstream only	File metadata
`onnx/model_quint8_avx2.onnx`	110.2 MB	`aa5c2717…9159b237` `aa5c27172d77bbd1cbae3628cbac4b26d7c12adabff25d2d4285d0f29159b237`	Upstream only	File metadata
`openvino/openvino_model.bin`	435.6 MB	`5c3279d8…2468dec0` `5c3279d833888eaab745e24b652126c5a71375af185ac21aa47e112e2468dec0`	Upstream only	File metadata
`openvino/openvino_model_qint8_quantized.bin`	110 MB	`fde0c650…414895ce` `fde0c650018f5e244f793316b666aaf4758d4e19072f430e59eb2bcc414895ce`	Upstream only	File metadata
`pytorch_model.bin`	438 MB	`a8fd120b…88928146` `a8fd120b1a0032e70ff3d4b8ab8e46a6d01c2cb08ffe7c007a021c1788928146`	Upstream only	File metadata
Tokenizer & config
`1_Pooling/config.json`	190 bytes	—	Upstream only	File metadata
`config.json`	571 bytes	`d46a3e04…5906cfa0` `d46a3e04ded82bba22528424480697d394eeda6a27484e08c5bb2bdf5906cfa0`	Hosted	Download
`config_sentence_transformers.json`	116 bytes	`061ca9d3…dc5ac9f3` `061ca9d39661d6c6d6de5ba27f79a1cd5770ea247f8d46412a68a498dc5ac9f3`	Hosted	Download
`data_config.json`	39.3 KB	—	Upstream only	File metadata
`openvino/openvino_model.xml`	432.8 KB	—	Upstream only	File metadata
`openvino/openvino_model_qint8_quantized.xml`	741.9 KB	—	Upstream only	File metadata
`sentence_bert_config.json`	53 bytes	`cabfacde…748f1667` `cabfacded9272091a06ff595a46ef027a76ddf4ac9e77d0fcf11c605748f1667`	Hosted	Download
`special_tokens_map.json`	239 bytes	`9ef40e9c…fa75f8ba` `9ef40e9c160511bf3f46ceb71f1471dafa1e9473d5120bb816c36b2efa75f8ba`	Hosted	Download
`tokenizer.json`	466 KB	`b8be2c30…48f1d842` `b8be2c30ba5dd723a6d5ee26d013da103d5408d92ddcb23747622f9e48f1d842`	Hosted	Download
`tokenizer_config.json`	363 bytes	`67f2ff7e…2fffc268` `67f2ff7e223518e729869bb3a70f0caf8368fe549383fc11cfe2dfb42fffc268`	Hosted	Download
`train_script.py`	13.1 KB	—	Upstream only	File metadata
`vocab.txt`	231.5 KB	—	Upstream only	File metadata
`modules.json`	349 bytes	`84e40c8e…7cf642cf` `84e40c8e006c9b1d6c122e02cba9b02458120b5fb0c87b746c41e0207cf642cf`	Hosted	Download
Docs
`.gitattributes`	1.23 KB	—	Upstream only	File metadata
`README.md`	10.4 KB	—	Upstream only	File metadata

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

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sha256sum -- 'model.safetensors'

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MNLP_M3_document_encoder

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