Instructions to use deepmind/language-perceiver with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepmind/language-perceiver with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="deepmind/language-perceiver")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("deepmind/language-perceiver") model = AutoModelForMaskedLM.from_pretrained("deepmind/language-perceiver", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from deepmind/language-perceiver: direct link, hf CLI and curl.
- Browser
- Download file 805 MB
-
https://huggingface.co/deepmind/language-perceiver/resolve/699554f25d31b3358f664bc5d9b3b5fa2b7c1111/pytorch_model.bin
- Command line
-
hf download hf://deepmind/language-perceiver@699554f25d31b3358f664bc5d9b3b5fa2b7c1111/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/deepmind/language-perceiver/resolve/699554f25d31b3358f664bc5d9b3b5fa2b7c1111/pytorch_model.bin
805 MB
- Xet hash:
- c402db4b17c8030ec3acd2d62eef14c6a45b492f82a8444a91091b065fb16d30
- Size of remote file:
- 805 MB
- SHA256:
- 941759e1c7489c5ffc7a922551a29e93581868972007aed48ae40e4a525ecba7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.