MNLP_M2_document_encoder safetensors download | Hugging Bay

Q: What is this model?

Ruthvikkk/MNLP_M2_document_encoder is an embedding model for sentence similarity and retrieval published by Ruthvikkk, added to Hugging Bay Jun 30, 2026. Model from Hugging Face for sentence similarity: Ruthvikkk/MNLP_M2_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: 7 files, 67.5 MB. Full repo upstream: 802.9 MB. The recommended download, model.safetensors, is 66.7 MB.

Q: What can I download here?

Hosted here: 7 files, 67.5 MB. Full repo upstream: 802.9 MB. The original release is at https://huggingface.co/Ruthvikkk/MNLP_M2_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-ruthvikkk-mnlp-m2-document-encoder/safety on Hugging Bay.

Q: Will Ruthvikkk/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.

Ruthvikkk

MNLP_M2_document_encoder

Can I run MNLP_M2_document_encoder on my computer?

It depends on your memory, processor, file format and runtime. Hugging Bay offers 7 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_M2_document_encoder is an embedding model for sentence similarity and retrieval. Model weights are learned parameters; a runtime loads them. Hosting files does not prove that a complete model runs.

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

Recorded SHA-256 for model.safetensors: 9a1eb90bbac323ea08aa5629b624fe6ae75db121b904799c2266a1e2c2de22d2. File evidence · Machine-readable facts · Run evidence and any recorded receipts · Proof Lab measurement policy. A checksum verifies byte identity, not safety; this answer asserts no measured runtime result.

Updated 2026-10-21 · Page text: As of 2026-10-21 · Content revision

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.

Ruthvikkk/MNLP_M2_document_encoder

Model details

sentence similarity and retrievalLicense: mitSHA-256 recorded for 16 of 23 filesBIN · safetensors · ONNX · 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.

Can I run it?View as JSON →

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

Other ways

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7 hosted of 23 listed · Hashes recorded. Date not recorded.

Selected downloadmodel.safetensors67 MB · 1 file Download · 67 MB (1 file)

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

7 hosted of 23 listed

Hashes recorded. Date not recorded.

File	Größe	SHA-256	Quelle	Herunterladen
Weights
`model.safetensors` Default	66.7 MB	`9a1eb90b…c2de22d2` `9a1eb90bbac323ea08aa5629b624fe6ae75db121b904799c2266a1e2c2de22d2`	Hosted	Herunterladen
`coreml/feature-extraction/float32_model.mlpackage/Data/com.apple.CoreML/weights/weight.bin`	133.1 MB	`d6d0949a…0cf95846` `d6d0949a2bd68a0de745908f51f9d4b868fcbb6ebf2fc41bd3a01df80cf95846`	Upstream only	File metadata
`onnx/model.onnx`	133.1 MB	`0b01312b…c488f1f8` `0b01312b59bec0a2558a626f2937be4cbe4bb16d1511560153f598cec488f1f8`	Upstream only	File metadata
`onnx/model_O4.onnx`	66.6 MB	`f39634c5…3671c795` `f39634c5a62a4337dd141b343df8df7f6ee7eaa8606e7882d2c893cd3671c795`	Upstream only	File metadata
`onnx/model_qint8_avx512_vnni.onnx`	34.1 MB	`c9434b8d…881e34cd` `c9434b8d71617919a3ef61f1fafea4b15b4e02d782cc287623158713881e34cd`	Upstream only	File metadata
`openvino/openvino_model.bin`	132.9 MB	`62020507…6a5fd039` `6202050706e8482a98a95c6d85849be7111612e735ea161ebe4b0b826a5fd039`	Upstream only	File metadata
`openvino/openvino_model_qint8_quantized.bin`	33.8 MB	`fa05bae0…3cc999ca` `fa05bae0e1b00f49ff69cfb37b894da10601ccb1a4f55879dfc4c5063cc999ca`	Upstream only	File metadata
`pytorch_model.bin`	66.8 MB	`34409967…d688f125` `344099675ecefb2dc886e6dcc1fba7ccc0c66dbf455e8aa289035ee8d688f125`	Upstream only	File metadata
Tokenizer & config
`1_Pooling/config.json`	190 bytes	—	Upstream only	File metadata
`config.json`	583 bytes	`00273cdf…5943dc59` `00273cdfb3e05c7304d180270bdef8adae45023e7bdfc396338bdd815943dc59`	Hosted	Herunterladen
`coreml/feature-extraction/float32_model.mlpackage/Data/com.apple.CoreML/model.mlmodel`	122 KB	`33e5309e…a3c4e45a` `33e5309eae5adc5d19d5421def0ad5ec9dfc462546bab56fc986a189a3c4e45a`	Upstream only	File metadata
`coreml/feature-extraction/float32_model.mlpackage/Manifest.json`	617 bytes	—	Upstream only	File metadata
`modules.json`	385 bytes	`73348a3d…f75fe43e` `73348a3d8f8ae4cc5f851dc6b98be3ea819390b814e83656d5eebbadf75fe43e`	Hosted	Herunterladen
`openvino/openvino_model.xml`	363.1 KB	—	Upstream only	File metadata
`openvino/openvino_model_qint8_quantized.xml`	664.1 KB	—	Upstream only	File metadata
`sentence_bert_config.json`	57 bytes	`948201d8…ee986529` `948201d8329907aae938fa62f9ceeed53f5694dacc2b87b9f3b78b37ee986529`	Hosted	Herunterladen
`special_tokens_map.json`	125 bytes	`b6d346be…76237ee3` `b6d346be366a7d1d48332dbc9fdf3bf8960b5d879522b7799ddba59e76237ee3`	Hosted	Herunterladen
`tf_model.h5`	133.7 MB	`38d78f5c…cdfdc036` `38d78f5cca85e9bfb60faf70f29d28f0946f3d8caba6f82cc45766e8cdfdc036`	Upstream only	File metadata
`tokenizer.json`	711.7 KB	`da0e7993…4d2c62a0` `da0e79933b9ed51798a3ae27893d3c5fa4a201126cef75586296df9b4d2c62a0`	Hosted	Herunterladen
`tokenizer_config.json`	394 bytes	`e1790949…a40c8ba7` `e1790949631401af1bfb6c9c7aeec7fcf612e274d73579d99f704faea40c8ba7`	Hosted	Herunterladen
`vocab.txt`	231.5 KB	—	Upstream only	File metadata
Docs
`.gitattributes`	1.52 KB	—	Upstream only	File metadata
`README.md`	68.1 KB	—	Upstream only	File metadata

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MNLP_M2_document_encoder

Can I run MNLP_M2_document_encoder on my computer?

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