Instructions to use Fraser/single_latent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fraser/single_latent with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import T5VaeForAutoencoding model = T5VaeForAutoencoding.from_pretrained("Fraser/single_latent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Fraser/single_latent: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/Fraser/single_latent/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Fraser/single_latent/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Fraser/single_latent/resolve/main/flax_model.msgpack
892 MB
- Xet hash:
- d9305654dabb789d9ed5fab706ad3df5ca0f138dadb3887b3dd279af9438c132
- Size of remote file:
- 892 MB
- SHA256:
- 78df6b3743922890773961fedfcd96f0c6af3fb53a81dd02b2ac50da99424ee8
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