Instructions to use ekojs/cscomm-t5-small-la with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ekojs/cscomm-t5-small-la with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ekojs/cscomm-t5-small-la") model = AutoModelForSeq2SeqLM.from_pretrained("ekojs/cscomm-t5-small-la", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from ekojs/cscomm-t5-small-la: direct link, hf CLI and curl.
- Browser
- Download file 308 MB
-
https://huggingface.co/ekojs/cscomm-t5-small-la/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://ekojs/cscomm-t5-small-la/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/ekojs/cscomm-t5-small-la/resolve/main/flax_model.msgpack
308 MB
- Xet hash:
- 4fc146ee30c1f63ea0086db248564408b43d7f474df95778114a4795a001a050
- Size of remote file:
- 308 MB
- SHA256:
- e73ab0783e6ebf87984952ee1297efad6d91d95ad0768fc7cd89824eee5547d9
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