Instructions to use Vivek/checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vivek/checkpoints with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultipleChoice tokenizer = AutoTokenizer.from_pretrained("Vivek/checkpoints") model = AutoModelForMultipleChoice.from_pretrained("Vivek/checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Vivek/checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 498 MB
-
https://huggingface.co/Vivek/checkpoints/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Vivek/checkpoints/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Vivek/checkpoints/resolve/main/flax_model.msgpack
498 MB
- Xet hash:
- 10c7e12b8376098ba9291e2b3fb38e866af6c7576238192b7b9597a269462ad4
- Size of remote file:
- 498 MB
- SHA256:
- 79c0caba83b0aa8746e4b1d6bf1f424c3ea65e6eee895cdadbfc80876407644c
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