Instructions to use djinnn/Bhasa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use djinnn/Bhasa_model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("djinnn/Bhasa_model") model = AutoModelForSeq2SeqLM.from_pretrained("djinnn/Bhasa_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from djinnn/Bhasa_model: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/djinnn/Bhasa_model/resolve/main/training_args.bin
- Command line
-
hf download hf://djinnn/Bhasa_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/djinnn/Bhasa_model/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- db87331c9195e41713e2c8fbe4f511ccca5fb95093fc90f5bb41e7ffdc1f90c2
- Size of remote file:
- 4.09 kB
- SHA256:
- 658299f7d29cdfb8cc6352cccca5cca817ae52bd12a4775f99b0a2cb4a8ce60d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.