MNLP_M2_document_encoder safetensors download | Hugging Bay

Q: What is this model?

pgilliar/MNLP_M2_document_encoder is an embedding model for sentence similarity and retrieval published by pgilliar, added to Hugging Bay Sep 24, 2026. Model from Hugging Face for sentence similarity: pgilliar/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: 976.9 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: 976.9 MB. The original release is at https://huggingface.co/pgilliar/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-pgilliar-mnlp-m2-document-encoder/safety on Hugging Bay.

Q: Will pgilliar/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.

pgilliar

MNLP_M2_document_encoder

pgilliar/MNLP_M2_document_encoder

Model details

sentence similarity and retrievalLicense: apache-2.0SHA-256 recorded for 22 of 30 filessafetensors · ONNX · BIN · H5

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

Other ways

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8 hosted of 30 listed · Hashes recorded. Date not recorded.

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

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

8 hosted of 30 listed

Hashes recorded. Date not recorded.

File	आकार	SHA-256	स्रोत	डाउनलोड करें
Weights
`model.safetensors` Default	90.9 MB	`53aa5117…b128d9db` `53aa51172d142c89d9012cce15ae4d6cc0ca6895895114379cacb4fab128d9db`	Hosted	डाउनलोड करें
`onnx/model.onnx`	90.4 MB	`6fd5d72f…16046452` `6fd5d72fe4589f189f8ebc006442dbb529bb7ce38f8082112682524616046452`	Upstream only	File metadata
`onnx/model_O1.onnx`	90.4 MB	`1391c6fc…299a88ec` `1391c6fc20b5530250bc15cbe1f47578ffeca55ab0551d335cc668b6299a88ec`	Upstream only	File metadata
`onnx/model_O2.onnx`	90.3 MB	`1de39050…d9a16faf` `1de3905029190b398c7d300b530e320cf4b5e7d3dfb9af1429ebd73fd9a16faf`	Upstream only	File metadata
`onnx/model_O3.onnx`	90.3 MB	`a44f671e…538b83ff` `a44f671e364dddbac31f203f07b91be6b0a35e51936e5ebfab65b6d9538b83ff`	Upstream only	File metadata
`onnx/model_O4.onnx`	45.2 MB	`1667d7f3…6160c485` `1667d7f3ba669048b13a96ee3a44456d5e42c8f44588ae8b603430e16160c485`	Upstream only	File metadata
`onnx/model_qint8_arm64.onnx`	23 MB	`4278337f…71902474` `4278337fd0ff3c68bfb6291042cad8ab363e1d9fbc43dcb499fe91c871902474`	Upstream only	File metadata
`onnx/model_qint8_avx512.onnx`	23 MB	`4278337f…71902474` `4278337fd0ff3c68bfb6291042cad8ab363e1d9fbc43dcb499fe91c871902474`	Upstream only	File metadata
`onnx/model_qint8_avx512_vnni.onnx`	23 MB	`4278337f…71902474` `4278337fd0ff3c68bfb6291042cad8ab363e1d9fbc43dcb499fe91c871902474`	Upstream only	File metadata
`onnx/model_quint8_avx2.onnx`	23 MB	`b941bf19…18a0fd21` `b941bf19f1f1283680f449fa6a7336bb5600bdcd5f84d10ddc5cd72218a0fd21`	Upstream only	File metadata
`openvino/openvino_model.bin`	90.3 MB	`8b86cab4…fa0d392a` `8b86cab4722e2aefab310cf96d4d5a9eb3b187f7d9670a082afc55c7fa0d392a`	Upstream only	File metadata
`openvino/openvino_model_qint8_quantized.bin`	22.9 MB	`c92ea4af…12da289b` `c92ea4af3c6bc7b4a0f3b3d61b147c850f4dbdd7c9e7beee0c0c70dc12da289b`	Upstream only	File metadata
`pytorch_model.bin`	90.9 MB	`c3a85f23…33baf256` `c3a85f238711653950f6a79ece63eb0ea93d76f6a6284be04019c53733baf256`	Upstream only	File metadata
Tokenizer & config
`1_Pooling/config.json`	190 bytes	—	Upstream only	File metadata
`config.json`	612 bytes	`953f9c0d…d8908b41` `953f9c0d463486b10a6871cc2fd59f223b2c70184f49815e7efbcab5d8908b41`	Hosted	डाउनलोड करें
`config_sentence_transformers.json`	116 bytes	`061ca9d3…dc5ac9f3` `061ca9d39661d6c6d6de5ba27f79a1cd5770ea247f8d46412a68a498dc5ac9f3`	Hosted	डाउनलोड करें
`data_config.json`	39.3 KB	—	Upstream only	File metadata
`openvino/openvino_model.xml`	211.3 KB	—	Upstream only	File metadata
`openvino/openvino_model_qint8_quantized.xml`	368 KB	—	Upstream only	File metadata
`rust_model.ot`	90.9 MB	`2d98d96d…53007aea` `2d98d96d278348988f2744e6445b8bc16d921c3f6e17c667362f3cb353007aea`	Upstream only	File metadata
`sentence_bert_config.json`	53 bytes	`fc1993fd…2a9cd4b7` `fc1993fde0a95c24ec6c022539d41cf6e2f7c9721e5415d6fb6897472a9cd4b7`	Hosted	डाउनलोड करें
`special_tokens_map.json`	112 bytes	`303df45a…5ec200e3` `303df45a03609e4ead04bc3dc1536d0ab19b5358db685b6f3da123d05ec200e3`	Hosted	डाउनलोड करें
`tf_model.h5`	91 MB	`24c06a74…bc597646` `24c06a7429b843d46e40c6b167122053921bf94dce2e5550ea5c07fabc597646`	Upstream only	File metadata
`tokenizer.json`	466.2 KB	`be50c362…11572037` `be50c3628f2bf5bb5e3a7f17b1f74611b2561a3a27eeab05e5aa30f411572037`	Hosted	डाउनलोड करें
`tokenizer_config.json`	350 bytes	`acb92769…6c61576b` `acb92769e8195aabd29b7b2137a9e6d6e25c476a4f15aa4355c233426c61576b`	Hosted	डाउनलोड करें
`train_script.py`	13.2 KB	—	Upstream only	File metadata
`vocab.txt`	231.5 KB	—	Upstream only	File metadata
`modules.json`	349 bytes	`84e40c8e…7cf642cf` `84e40c8e006c9b1d6c122e02cba9b02458120b5fb0c87b746c41e0207cf642cf`	Hosted	डाउनलोड करें
Docs
`.gitattributes`	1.52 KB	—	Upstream only	File metadata
`README.md`	10.5 KB	—	Upstream only	File metadata

डाउनलोड की जाँच करें

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

Compare the result with the recorded SHA-256.

MNLP_M2_document_encoder

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