Instructions to use zharry29/step_benchmark_roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zharry29/step_benchmark_roberta with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultipleChoice tokenizer = AutoTokenizer.from_pretrained("zharry29/step_benchmark_roberta") model = AutoModelForMultipleChoice.from_pretrained("zharry29/step_benchmark_roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from zharry29/step_benchmark_roberta: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/zharry29/step_benchmark_roberta/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://zharry29/step_benchmark_roberta/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/zharry29/step_benchmark_roberta/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- ccda0f1621b6b9d9442d3c91af864aefc4a08a940b499205ad02aba5fdaf49a6
- Size of remote file:
- 499 MB
- SHA256:
- 0f833c3a637a8c9b3df25d9c4b986e76052086d7921d19d6e0f2b7ddde2dad8c
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