Instructions to use prithivida/active_to_passive_styletransfer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivida/active_to_passive_styletransfer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("prithivida/active_to_passive_styletransfer") model = AutoModelForSeq2SeqLM.from_pretrained("prithivida/active_to_passive_styletransfer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from prithivida/active_to_passive_styletransfer: direct link, hf CLI and curl.
- Browser
- Download file 242 MB
-
https://huggingface.co/prithivida/active_to_passive_styletransfer/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://prithivida/active_to_passive_styletransfer/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/prithivida/active_to_passive_styletransfer/resolve/main/flax_model.msgpack
242 MB
- Xet hash:
- 8707567969411968d8a7992a31b718b079dbdd03665f2f0c4b0fe6e3cb8e4ab5
- Size of remote file:
- 242 MB
- SHA256:
- 7770eb75b0092cd0a8f289fe59c4194553cfcfe37d4f4a9072636e6acb179ce7
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