Instructions to use EMBO/sd-panelization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EMBO/sd-panelization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="EMBO/sd-panelization")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("EMBO/sd-panelization") model = AutoModelForTokenClassification.from_pretrained("EMBO/sd-panelization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from EMBO/sd-panelization: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/EMBO/sd-panelization/resolve/refs%2Fpr%2F2/flax_model.msgpack
- Command line
-
hf download hf://EMBO/sd-panelization@refs/pr/2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/EMBO/sd-panelization/resolve/refs%2Fpr%2F2/flax_model.msgpack
496 MB
- Xet hash:
- ca1c11197236c8cf9f3793b9b9226afb7a636a7badb3c8a088cde2c1b1fa1282
- Size of remote file:
- 496 MB
- SHA256:
- c92be69ab7ebeebf6ad7a61296b3c0ec65c40e5490ab299ae1b3160e8d6ecf1f
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