tiny-random-xlm-roberta safetensors download | Hugging Bay

Q: What is this model?

optimum-intel-internal-testing/tiny-random-xlm-roberta is an open-weights language model for general published by optimum-intel-internal-testing, added to Hugging Bay Sep 21, 2026. Model from Hugging Face for llm: optimum-intel-internal-testing/tiny-random-xlm-roberta · pytorch

Q: Can I use it commercially?

License: unknown. Check the upstream license text or ask the model author before commercial use.

Q: How big is it?

Hosted here: 6 files, 9.55 MB. Full repo upstream: 14.2 MB. The recommended download, model.safetensors, is 9.09 MB.

Q: What can I download here?

Hosted here: 6 files, 9.55 MB. Full repo upstream: 14.2 MB.

Q: File pattern scans

Automated file-pattern scans flagged nothing; not a safety guarantee. 6 hosted files carry a published SHA-256 you can re-hash. The machine-readable verdict is at /api/artifacts/hf-model-optimum-intel-internal-testing-tiny-random-xlm-roberta/safety on Hugging Bay.

Q: Will optimum-intel-internal-testing/tiny-random-xlm-roberta 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.

optimum-intel-internal-testing

tiny-random-xlm-roberta

Can I run tiny-random-xlm-roberta on my computer?

It depends on your memory, processor, file format and runtime. Hugging Bay offers 6 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.

tiny-random-xlm-roberta is an open-weights language model for general. 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: 8cd9aefdcce328560a21d7a9bf0c65c4ea2a945a72162efee05faa521c0cfc70. 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 2026-09-21 at the model source. Popularity counters are source-reported, not measured by Hugging Bay. Availability checked 2026-10-22.

optimum-intel-internal-testing/tiny-random-xlm-roberta

Model details

generalLicense: unknownSHA-256 recorded for 8 of 11 filessafetensors · BIN

Content controls: not assessed.

The catalog does not give enough information to describe what this model is for. Catalog source: unknown.

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-optimum-intel-internal-testing-tiny-random-xlm-roberta/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

6 hosted of 11 listed · Hashes recorded. Date not recorded.

Selected downloadmodel.safetensors9.09 MB · 1 file Download · 9.09 MB (1 file)

Open-file help

Content controls: not assessed.

Check your download →

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

6 hosted of 11 listed

Hashes recorded. Date not recorded.

File	Tamaño	SHA-256	Origen	Descargar
Weights
`model.safetensors` Default	9.09 MB	`8cd9aefd…1c0cfc70` `8cd9aefdcce328560a21d7a9bf0c65c4ea2a945a72162efee05faa521c0cfc70`	Hosted	Descargar
`pytorch_model.bin`	4.33 MB	`eb0bccaf…c4b2d559` `eb0bccafb4bee811f2138956ea9e94596e1bfdfc868b5364d7b678fac4b2d559`	Upstream only	File metadata
Tokenizer & config
`config.json`	920 bytes	`4bc33117…1aa16ded` `4bc33117b12fec0c72c3a433734042a0487c9010417fca5110624fd91aa16ded`	Hosted	Descargar
`generation_config.json`	144 bytes	`cdc3cc91…7699ab42` `cdc3cc912fbcccffc929bad5ad222dfb58ea5438290e7b5c9ba277727699ab42`	Hosted	Descargar
`make-tiny-xlm-roberta.py`	4.8 KB	—	Upstream only	File metadata
`sentencepiece.bpe.model`	309.2 KB	`ef67a60b…6faa1588` `ef67a60b933d0da430d4b839301301ada0179b0c71102b0eef4567386faa1588`	Upstream only	File metadata
`special_tokens_map.json`	238 bytes	`58dc0a3d…85e53a83` `58dc0a3d15e3bad4861bcf2dee770ea327344d185182d4c5d2bf253285e53a83`	Hosted	Descargar
`tokenizer.json`	466.3 KB	`91442440…07ece947` `91442440b17efb24b2c9aa4a79431be606ba0a850b0cba1ff408e30707ece947`	Hosted	Descargar
`tokenizer_config.json`	327 bytes	`f998ef74…456b1c1c` `f998ef74b999a23775eb90c18e1d8775319a998f8c7c5fa58296f272456b1c1c`	Hosted	Descargar
Docs
`.gitattributes`	1.56 KB	—	Upstream only	File metadata
`README.md`	80 bytes	—	Upstream only	File metadata

Comprobar una descarga

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.

tiny-random-xlm-roberta

Can I run tiny-random-xlm-roberta on my computer?

Comprobar una descarga

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