Instructions to use djinnn/Bhasa-Translator-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use djinnn/Bhasa-Translator-Model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("djinnn/Bhasa-Translator-Model") model = AutoModelForSeq2SeqLM.from_pretrained("djinnn/Bhasa-Translator-Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from djinnn/Bhasa-Translator-Model: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/djinnn/Bhasa-Translator-Model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://djinnn/Bhasa-Translator-Model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/djinnn/Bhasa-Translator-Model/resolve/main/pytorch_model.bin
1.2 GB
- Xet hash:
- 07706c9e4956fe57496849214d32c71b71192889c84d9ebaf61f3aac6cf1f6a8
- Size of remote file:
- 1.2 GB
- SHA256:
- c45c51465aa0b5214bf1d459e372a92a5467f13722af4a1b3cdaeadc9d4aad8a
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