Instructions to use transformers-community/sink_cache with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use transformers-community/sink_cache with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("transformers-community/sink_cache", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from transformers-community/sink_cache: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/transformers-community/sink_cache/resolve/refs%2Fpr%2F2/tokenizer.json
- Command line
-
hf download hf://transformers-community/sink_cache@refs/pr/2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/transformers-community/sink_cache/resolve/refs%2Fpr%2F2/tokenizer.json
11.4 MB
- Xet hash:
- 6aec39639a0a2d1ca966356b8c2b8426a484f80ff80731f44fa8482040713bdf
- Size of remote file:
- 11.4 MB
- SHA256:
- aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.