Instructions to use indonlp/cendol-mt5-small-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use indonlp/cendol-mt5-small-chat with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("indonlp/cendol-mt5-small-chat") model = AutoModelForSeq2SeqLM.from_pretrained("indonlp/cendol-mt5-small-chat", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from indonlp/cendol-mt5-small-chat: direct link, hf CLI and curl.
- Browser
- Download file 2.23 GB
-
https://huggingface.co/indonlp/cendol-mt5-small-chat/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://indonlp/cendol-mt5-small-chat/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/indonlp/cendol-mt5-small-chat/resolve/main/pytorch_model.bin
2.23 GB
- Xet hash:
- cab84a0244983d6c22c37baeef75cb1d3d7071bc5188ccd35542d8068353b026
- Size of remote file:
- 2.23 GB
- SHA256:
- c54e4ea2e9997cff93f5937dd789114daae4200029690a64418585c1c3efadf3
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