Instructions to use rail-berkeley/octo-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rail-berkeley/octo-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rail-berkeley/octo-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download example_batch.msgpack from rail-berkeley/octo-base: direct link, hf CLI and curl.
- Browser
- Download file 738 kB
-
https://huggingface.co/rail-berkeley/octo-base/resolve/main/example_batch.msgpack
- Command line
-
hf download hf://rail-berkeley/octo-base/example_batch.msgpack
-
curl -L -o example_batch.msgpack https://huggingface.co/rail-berkeley/octo-base/resolve/main/example_batch.msgpack
738 kB
- Xet hash:
- 4b7b542f07ab90abd3486dea94c8c4567468458363f4a5f48432c662bdc94702
- Size of remote file:
- 738 kB
- SHA256:
- 0ce74dd8e433ce4a8a1534c4ab9687d9fc3e444b3eece750047dcb22125d73ff
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