mt0-small safetensors download (1.2 GB) | Hugging Bay

Q: What is this model?

bigscience/mt0-small is an open-weights language model for text generation published by bigscience, added to Hugging Bay Jun 30, 2026. Model from Hugging Face for text generation: bigscience/mt0-small · transformers

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 5 listed files; full count and size unknown. Full repo upstream: 6.39 GB. The recommended download, model.safetensors, is 1.2 GB.

Q: What can I download here?

Hosted here: at least 5 listed files; full count and size unknown. Full repo upstream: 6.39 GB. The original release is at https://huggingface.co/bigscience/mt0-small.

Q: File pattern scans

Automated file-pattern scans flagged nothing; not a safety guarantee. 5 hosted files carry a published SHA-256 you can re-hash. The machine-readable verdict is at /api/artifacts/hf-model-bigscience-mt0-small/safety on Hugging Bay.

Q: Will bigscience/mt0-small 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.

bigscience

mt0-small

bigscience/mt0-small

text generationLicense: apache-2.0Hashes recordedsafetensors · ONNX · BIN

Content controls: not assessed.

Model from Hugging Face for text generation: bigscience/mt0-small · transformers

View as JSON →

5 hosted of 19 listed

Hashes recorded. Date not recorded.

File	Tamaño	SHA-256	Origen	Descargar
Weights
`model.safetensors` Default	1.2 GB	`9e3d320d…9da42daa` `9e3d320de3e92f254a1d03b0a5771ac960e3e151f9ee00bfbb3d0deb9da42daa`	Hosted	Descargar
`onnx/decoder_model.onnx`	1.13 GB	`763b6326…babbc768` `763b6326c43df7c1ac4032991009f20210bc217cdd6e6861ef491bbababbc768`	Upstream only	File metadata
`onnx/decoder_model_merged.onnx`	1.13 GB	`390f8de9…0f9abe2f` `390f8de9e630c728013dc0a583a8ad27fb0a1fc3faf95bdd320748570f9abe2f`	Upstream only	File metadata
`onnx/decoder_with_past_model.onnx`	1.11 GB	`2a9cc37d…99d23e4c` `2a9cc37d8e83d44ba89b125c1537d5567f2797b95a5044a7fe1eee1199d23e4c`	Upstream only	File metadata
`onnx/encoder_model.onnx`	587.9 MB	`b92e6ee2…873d94a9` `b92e6ee2bc3733a0a55232680419852673a5fa6b3bec4a85c132a7c4873d94a9`	Upstream only	File metadata
`pytorch_model.bin`	1.2 GB	`5f423353…50deed11` `5f423353c85760b90fc82bf74a641791aa0884dbd6202326613bd9d850deed11`	Upstream only	File metadata
Tokenizer & config
`config.json`	773 bytes	`1739237f…81fea7de` `1739237f2d091ee2970e28bcebbb410db3559b3e27b1fa5e41dc542481fea7de`	Hosted	Descargar
`onnx/config.json`	749 bytes	—	Upstream only	File metadata
`onnx/generation_config.json`	142 bytes	—	Upstream only	File metadata
`onnx/special_tokens_map.json`	74 bytes	—	Upstream only	File metadata
`onnx/spiece.model`	4.31 MB	`ef78f865…433125d6` `ef78f86560d809067d12bac6c09f19a462cb3af3f54d2b8acbba26e1433125d6`	Upstream only	File metadata
`onnx/tokenizer.json`	16.3 MB	`99cc9998…f85c5ea5` `99cc999819aaabf74898a252863b10d86fbcd86e8b3f65c118ff334ff85c5ea5`	Upstream only	File metadata
`onnx/tokenizer_config.json`	285 bytes	—	Upstream only	File metadata
`spiece.model`	4.31 MB	`ef78f865…433125d6` `ef78f86560d809067d12bac6c09f19a462cb3af3f54d2b8acbba26e1433125d6`	Upstream only	File metadata
`tokenizer.json`	16.3 MB	`93c35780…6b802aa5` `93c3578052e1605d8332eb961bc08d72e246071974e4cc54aa6991826b802aa5`	Hosted	Descargar
`tokenizer_config.json`	430 bytes	`a3eb3d84…f8c0f0d3` `a3eb3d84ce46ad715bcd4b2baf47d2ced133c219d3c3ab45387e0fbbf8c0f0d3`	Hosted	Descargar
`special_tokens_map.json`	74 bytes	`5e4d1f5e…42eec723` `5e4d1f5e759d74cb1c2fe1d165cfc62b5237aa904de759380cd6f43042eec723`	Hosted	Descargar
Docs
`.gitattributes`	1.49 KB	—	Upstream only	File metadata
`README.md`	23.9 KB	—	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.

mt0-small

Comprobar una descarga

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