Instructions to use Sangam101/prabigya_partial_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sangam101/prabigya_partial_trained with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Sangam101/prabigya_partial_trained") model = AutoModelForSeq2SeqLM.from_pretrained("Sangam101/prabigya_partial_trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sangam101/prabigya_partial_trained: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/Sangam101/prabigya_partial_trained/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sangam101/prabigya_partial_trained/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sangam101/prabigya_partial_trained/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- ea7263e1b45eb6ec6766e875733a09f91cd93c2060efd4bf84d69dad1c8ce8f3
- Size of remote file:
- 1.63 GB
- SHA256:
- 318fbef9ebec08c52130ff68b6064486ae4b4973b62d06008996e20bd36d85cb
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