MNLP_M3_document_encoder safetensors download | Hugging Bay

Q: What is this model?

igzi/MNLP_M3_document_encoder is an embedding model for text embeddings published by igzi, added to Hugging Bay Jun 30, 2026. Model from Hugging Face for feature extraction: igzi/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: 134.5 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: 134.5 MB. The original release is at https://huggingface.co/igzi/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-igzi-mnlp-m3-document-encoder/safety on Hugging Bay.

Q: Will igzi/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.

igzi

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. 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: ea1d11a3f23d14fe09fc1826fc7944e89c09a634d2217d57a21dd136805ee3e8. 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.

igzi/MNLP_M3_document_encoder

Model details

text embeddingsLicense: mitSHA-256 recorded for 8 of 12 filessafetensors

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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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-igzi-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.

See app requirements

8 hosted of 12 listed · Hashes recorded. Date not recorded.

Selected downloadmodel.safetensors133 MB · 1 file Download · 133 MB (1 file)

Open-file help

Content controls: not assessed.

Check your download →

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

8 hosted of 12 listed

Hashes recorded. Date not recorded.

File	Tamanho	SHA-256	Origem	Baixar
Weights
`model.safetensors` Default	133.5 MB	`ea1d11a3…805ee3e8` `ea1d11a3f23d14fe09fc1826fc7944e89c09a634d2217d57a21dd136805ee3e8`	Hosted	Baixar
Tokenizer & config
`1_Pooling/config.json`	296 bytes	—	Upstream only	File metadata
`config.json`	661 bytes	`e97203ba…7b67d892` `e97203ba01863b970fee7535331243994a30e195c9bfafeefe2685ae7b67d892`	Hosted	Baixar
`config_sentence_transformers.json`	205 bytes	`97e69a49…cce1dbd2` `97e69a490e5689de8aef17b1c76706a7a4be85cdd3a4dc3834b2a7b5cce1dbd2`	Hosted	Baixar
`sentence_bert_config.json`	52 bytes	`84e39fda…2a693cbf` `84e39fda68ccbff05bfa723ae9c0e70e23e2ec373b76e0f8c6e71af72a693cbf`	Hosted	Baixar
`special_tokens_map.json`	695 bytes	`5d5b662e…a350503a` `5d5b662e421ea9fac075174bb0688ee0d9431699900b90662acd44b2a350503a`	Hosted	Baixar
`tokenizer.json`	711.4 KB	`d241a60d…1f9e5c66` `d241a60d5e8f04cc1b2b3e9ef7a4921b27bf526d9f6050ab90f9267a1f9e5c66`	Hosted	Baixar
`tokenizer_config.json`	1.27 KB	`7b082b48…171186a0` `7b082b48a08a0b5c5939d99472e33addce3d96202155e33f7928cecd171186a0`	Hosted	Baixar
`vocab.txt`	231.5 KB	—	Upstream only	File metadata
`modules.json`	349 bytes	`84e40c8e…7cf642cf` `84e40c8e006c9b1d6c122e02cba9b02458120b5fb0c87b746c41e0207cf642cf`	Hosted	Baixar
Docs
`.gitattributes`	1.52 KB	—	Upstream only	File metadata
`README.md`	94.8 KB	—	Upstream only	File metadata

Verificar um download

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

macOS

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_M3_document_encoder

Can I run MNLP_M3_document_encoder on my computer?

Verificar um download

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