Instructions to use indonlp/cendol-mt5-base-inst with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use indonlp/cendol-mt5-base-inst with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("indonlp/cendol-mt5-base-inst") model = AutoModelForSeq2SeqLM.from_pretrained("indonlp/cendol-mt5-base-inst", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from indonlp/cendol-mt5-base-inst: direct link, hf CLI and curl.
- Browser
- Download file 3.87 GB
-
https://huggingface.co/indonlp/cendol-mt5-base-inst/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://indonlp/cendol-mt5-base-inst/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/indonlp/cendol-mt5-base-inst/resolve/main/pytorch_model.bin
3.87 GB
- Xet hash:
- 6f90884989d688f4444705c9e1a494a5bd8a4ca36aa843137fc09eabcb621d57
- Size of remote file:
- 3.87 GB
- SHA256:
- 73e5413420201604246b5ca34e8f8ac835dd0a88499f4ad3e24c6abd40f269c1
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