zero-shot-classification safetensors download | Hugging Bay

Q: What is this model?

nahiar/zero-shot-classification is an open-weights language model for zero-shot classification published by nahiar, added to Hugging Bay Jun 30, 2026. Model from Hugging Face for zero shot classification: nahiar/zero-shot-classification · 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 7 listed files; full count and size unknown. Full repo upstream: 6.74 GB. The recommended download, model.safetensors, is 2.24 GB.

Q: What can I download here?

Hosted here: at least 7 listed files; full count and size unknown. Full repo upstream: 6.74 GB. The original release is at https://huggingface.co/nahiar/zero-shot-classification.

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-nahiar-zero-shot-classification/safety on Hugging Bay.

Q: Will nahiar/zero-shot-classification 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.

nahiar

zero-shot-classification

nahiar/zero-shot-classification

Model details

zero-shot classificationLicense: mitSHA-256 recorded for 9 of 10 filessafetensors · BIN · H5

Content controls: not assessed.

Listed for classifying text with supplied labels; for developers building text classification tools. Catalog sources: license, cardMetadata.license, 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-nahiar-zero-shot-classification/model.safetensors",
    "model.safetensors",
)

Other ways

Run it on your computerOpen in Hugging Bay appSend to a computerApp links support one GGUF file.

7 hosted of 10 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.

7 hosted of 10 listed

Hashes recorded. Date not recorded.

File	Size	SHA-256	Source	Download
Weights
`model.safetensors` Default	2.24 GB	`8869b0c9…e3a423e4` `8869b0c99ad35ec8a8c92434b54383d2dfd7db8cd460e28b9944a407e3a423e4`	Hosted	Download
`pytorch_model.bin`	2.24 GB	`947e0ceb…a0f8ec57` `947e0cebe09808e3279f38ceeca58af60c1fcebefbdfa54642285f35a0f8ec57`	Upstream only	File metadata
Tokenizer & config
`config.json`	734 bytes	`af751dca…a1e4b404` `af751dca748bcc82c58fe150ce879862a825b1f5bc492d7289dc12e7a1e4b404`	Hosted	Download
`requirements.txt`	67 bytes	`6c8b5e84…2d0bfafb` `6c8b5e843500d9193e762365bc008be7e0a03ca8e3809f9be4bc8ce02d0bfafb`	Hosted	Download
`special_tokens_map.json`	150 bytes	`7638f5bb…e8da1150` `7638f5bbbe86ef6d604ef28ad3647dc690d6d117c81c0d63e885416be8da1150`	Hosted	Download
`tf_model.h5`	2.24 GB	`913bd557…18f73325` `913bd557db7f4e2f85c4d94c4ba30b3342457f6d2d783a8be141ae4018f73325`	Upstream only	File metadata
`tokenizer_config.json`	426 bytes	`72ab8cfc…93410977` `72ab8cfcec9156738111bdc3225be1e9053351b9636569bcf4e822de93410977`	Hosted	Download
`sentencepiece.bpe.model`	5.07 MB	`cfc8146a…e0db2865` `cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865`	Hosted	Download
Docs
`.gitattributes`	459 bytes	—	Upstream only	File metadata
`README.md`	8.1 KB	`b95429a1…c6c097bc` `b95429a170fd10b5e3f9c9a063eb8972fe42c2c0b67636514b74a457c6c097bc`	Hosted	Download

Check a 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.

zero-shot-classification

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