Instructions to use pere/tt5x-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pere/tt5x-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="pere/tt5x-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pere/tt5x-base") model = AutoModel.from_pretrained("pere/tt5x-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from pere/tt5x-base: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/pere/tt5x-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://pere/tt5x-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/pere/tt5x-base/resolve/main/flax_model.msgpack
892 MB
- Xet hash:
- 5a63c437ef7a23c82460ed01cf6cfeb10a099c91cdcab122b27fc8e95ecee741
- Size of remote file:
- 892 MB
- SHA256:
- 384605911828086db47a5f3e1868c1ffbb748e51b81db30e59cd58964866a4a1
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