Instructions to use indonlp/cendol-mt5-small-inst with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use indonlp/cendol-mt5-small-inst with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("indonlp/cendol-mt5-small-inst") model = AutoModelForSeq2SeqLM.from_pretrained("indonlp/cendol-mt5-small-inst", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from indonlp/cendol-mt5-small-inst: direct link, hf CLI and curl.
- Browser
- Download file 2.23 GB
-
https://huggingface.co/indonlp/cendol-mt5-small-inst/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://indonlp/cendol-mt5-small-inst/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/indonlp/cendol-mt5-small-inst/resolve/main/pytorch_model.bin
2.23 GB
- Xet hash:
- b6b9c3e58c02c564adee993ca98ed965d29ec25a7611d4a93d54aea5673f4a32
- Size of remote file:
- 2.23 GB
- SHA256:
- 4ce67f4ab5fc024f2111f739dbc07bf40539dd800dcb55dd37678959e845bf46
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