Instructions to use pere/tt5x-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pere/tt5x-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="pere/tt5x-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pere/tt5x-small") model = AutoModel.from_pretrained("pere/tt5x-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from pere/tt5x-small: direct link, hf CLI and curl.
- Browser
- Download file 1.35 kB
-
https://huggingface.co/pere/tt5x-small/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://pere/tt5x-small/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/pere/tt5x-small/resolve/main/flax_model.msgpack
1.35 kB
- Xet hash:
- 9f739fb8026c09a58b09aa11cf7be3073da8995efb2dd65664027c7a31f51206
- Size of remote file:
- 1.35 kB
- SHA256:
- 8235a3151278990f75e3c874a627c7d19333f921c1d1388ce462866bd7a83ed2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.