Nepali_Embedding_Model safetensors download | Hugging Bay

Q: What is this model?

universalml/Nepali_Embedding_Model is an embedding model for sentence similarity and retrieval published by universalml, added to Hugging Bay Jun 30, 2026. Model from Hugging Face: universalml/Nepali_Embedding_Model

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. The full repository size is not recorded. The recommended download, model.safetensors, is 2.24 GB.

Q: What can I download here?

Hosted here: at least 8 listed files; full count and size unknown. Full repo upstream: unknown. The original release is at https://huggingface.co/universalml/Nepali_Embedding_Model.

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-universalml-nepali-embedding-model/safety on Hugging Bay.

Q: Will universalml/Nepali_Embedding_Model 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.

universalml

Nepali_Embedding_Model

Can I run Nepali_Embedding_Model 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.

Nepali_Embedding_Model is an embedding model for sentence similarity and retrieval. Model weights are learned parameters; a runtime loads them. A hosted file is an individual file Hugging Bay can serve. SHA-256 identifies exact file bytes. The availability date above applies to this file record, not the upstream metadata update date.

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

Recorded SHA-256 for model.safetensors: 9896ca3aa5099620001a7eba78c5dbf064c5b531ca15184e4913f40ef1549cd0. Exact file evidence · Machine-readable source facts · Run evidence and any recorded receipts. A checksum fingerprints only the named file bytes; it does not prove safety, quality, a complete multi-file selection or a successful run.

Updated 2026-10-21 · Page-copy source revision 8871bab84e1cf80aea1686a944ec335078a6c036 · Source files: apps/web/lib/model-page-template.mjs, apps/web/lib/model-page-v3.mjs, apps/web/lib/aeo-page.mjs · Full revision record

Model metadata: As of 2026-09-28 at the model source. Popularity counters are source-reported, not measured by Hugging Bay. Availability checked 2026-10-22.

universalml/Nepali_Embedding_Model

Model details

sentence similarity and retrievalLicense: apache-2.0SHA-256 recorded for 10 of 11 filessafetensors

Content controls: not assessed.

The catalog lists this model for checking whether two sentences mean similar things. Catalog sources: license, task.

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-universalml-nepali-embedding-model/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 11 listed · Hashes recorded. Date not recorded.

Selected downloadmodel.safetensors2.24 GB · 1 file Download · 2.24 GB (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 11 listed

Hashes recorded. Date not recorded.

File	आकार	SHA-256	स्रोत	डाउनलोड करें
Weights
`model.safetensors` Default	2.24 GB	`9896ca3a…f1549cd0` `9896ca3aa5099620001a7eba78c5dbf064c5b531ca15184e4913f40ef1549cd0`	Hosted	डाउनलोड करें
Tokenizer & config
`1_Pooling/config.json`	297 bytes	`4e103b8e…907b3822` `4e103b8e2a31573ea88570523c2db4f13d34e09a26333e8a889268ed907b3822`	Upstream only	File metadata
`config.json`	725 bytes	`2145eef2…630da88d` `2145eef200a6aa3014fa8c553ff7bbdcd35611f27d4f05cf8648f54d630da88d`	Hosted	डाउनलोड करें
`config_sentence_transformers.json`	201 bytes	`f68c198a…c626332d` `f68c198aa2a236ce20f13c86cac99867a16d0578ec7c37517be93055c626332d`	Hosted	डाउनलोड करें
`sentence_bert_config.json`	53 bytes	`ec8e29d6…5a8d0d51` `ec8e29d6dcb61b611b7d3fdd2982c4524e6ad985959fa7194eacfb655a8d0d51`	Hosted	डाउनलोड करें
`special_tokens_map.json`	964 bytes	`8c785abe…4cf10835` `8c785abebea9ae3257b61681b4e6fd8365ceafde980c21970d001e834cf10835`	Hosted	डाउनलोड करें
`tokenizer.json`	17.1 MB	`883b0371…0f137085` `883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085`	Hosted	डाउनलोड करें
`tokenizer_config.json`	1.18 KB	`49f06d15…489f8175` `49f06d15a18f81855c338f8ab6241ca2e502a65b62968be69b74f094489f8175`	Hosted	डाउनलोड करें
`modules.json`	349 bytes	`84e40c8e…7cf642cf` `84e40c8e006c9b1d6c122e02cba9b02458120b5fb0c87b746c41e0207cf642cf`	Hosted	डाउनलोड करें
Docs
`README.md`	4.18 KB	`5e81cfef…c55bf8e6` `5e81cfef24657f00d51f68b5dea3b9d3b3c4ad92aef72264d8ac6188c55bf8e6`	Upstream only	File metadata
`.gitattributes`	0 bytes	—	Upstream only	File metadata

डाउनलोड की जाँच करें

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.

Nepali_Embedding_Model

Can I run Nepali_Embedding_Model on my computer?

डाउनलोड की जाँच करें

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