gbert-large safetensors download (1.35 GB) | Hugging Bay

Q: What is this model?

deepset/gbert-large is an open-weights language model for masked-language modeling published by deepset, added to Hugging Bay Jun 29, 2026. Model from Hugging Face: deepset/gbert-large

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: 6 files, 1.35 GB. Full repo upstream: 4.17 GB. The recommended download, model.safetensors, is 1.35 GB.

Q: What can I download here?

Hosted here: 6 files, 1.35 GB. Full repo upstream: 4.17 GB. The original release is at https://huggingface.co/deepset/gbert-large.

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-deepset-gbert-large/safety on Hugging Bay.

Q: Will deepset/gbert-large 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.

deepset

gbert-large

deepset/gbert-large

masked-language modelingLicense: mitHashes recordedsafetensors · BIN · H5

Content controls: not assessed.

Model from Hugging Face: deepset/gbert-large

View as JSON →

6 hosted of 9 listed

Hashes recorded. Date not recorded.

File	Tamanho	SHA-256	Origem	Baixar
Weights
`model.safetensors` Default	1.35 GB	`9bedbb71…9b1bd2f0` `9bedbb712a8c9de0ee710030ecd47616a52dc533409859c53a2e77069b1bd2f0`	Hosted	Baixar
`pytorch_model.bin`	1.35 GB	`486f35f6…b222088b` `486f35f65525e99baa4e2c801a92fb4986fc9c8c18e3fba2d890fdb3b222088b`	Upstream only	File metadata
Tokenizer & config
`config.json`	363 bytes	`22056b0f…604f0713` `22056b0fccfd69aa1d83728833dd781f4209b29b75493da340c4e1cd604f0713`	Hosted	Baixar
`tf_model.h5`	1.48 GB	`3f8af0d8…379a8f46` `3f8af0d8ba799382ab50b6359d3b0ffa4d59751da21b2bdbf544e296379a8f46`	Upstream only	File metadata
`tokenizer_config.json`	83 bytes	`25f2b98f…dca10378` `25f2b98fcd4c39add7f30fa8e129c9d7d1fc2d27a37d615ae6452a47dca10378`	Hosted	Baixar
`vocab.txt`	239.8 KB	`bf186f7f…f13039a3` `bf186f7fb9cb667d811c08b45ce64d1e8e262277bb053f05d1aef3e9f13039a3`	Hosted	Baixar
Docs
`.gitattributes`	399 bytes	`a368a21c…5ccc6f59` `a368a21c472071feec9095f63d5f7d3433826c1c476a6995f072b6755ccc6f59`	Upstream only	File metadata
`README.md`	2.94 KB	`5695c1b2…417bef82` `5695c1b2c061ff954dbfc79683edbb95610ead018c212218995e24f6417bef82`	Hosted	Baixar
`license.txt`	1.05 KB	`3e6d3306…7a1cd5a2` `3e6d33063d766183a848ffabfabf0ec945be81bd9f7eb230d0f69ceb7a1cd5a2`	Hosted	Baixar

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.

gbert-large

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