Instructions to use royeis/T5-FlowNLG-Realizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use royeis/T5-FlowNLG-Realizer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("royeis/T5-FlowNLG-Realizer") model = AutoModelForSeq2SeqLM.from_pretrained("royeis/T5-FlowNLG-Realizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from royeis/T5-FlowNLG-Realizer: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/royeis/T5-FlowNLG-Realizer/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://royeis/T5-FlowNLG-Realizer/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/royeis/T5-FlowNLG-Realizer/resolve/main/flax_model.msgpack
892 MB
- Xet hash:
- 5447a47efb576fad9b05ff69499a1c6315397dcb664fc5004010b5de9748d61c
- Size of remote file:
- 892 MB
- SHA256:
- c7c82e2ccd6bce09d682509b8c53525d13009edbabffd6e8ea559b5ac0784b9d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.