Instructions to use Jitin/romanized-malayalam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jitin/romanized-malayalam with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Jitin/romanized-malayalam")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Jitin/romanized-malayalam") model = AutoModelForMaskedLM.from_pretrained("Jitin/romanized-malayalam", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Jitin/romanized-malayalam: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/Jitin/romanized-malayalam/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Jitin/romanized-malayalam/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Jitin/romanized-malayalam/resolve/main/flax_model.msgpack
334 MB
- Xet hash:
- bed51297715340691a85be2488aa0ba03d6c2f335b4f77c5c3ebc124a77d2b7a
- Size of remote file:
- 334 MB
- SHA256:
- ca2b966941d01b0b449547bb4258429835e2c7d1412408311558d756935e20b1
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