Instructions to use ZibinDong/ActionCodec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZibinDong/ActionCodec2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ZibinDong/ActionCodec2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download runtime/backends/actioncodec2/__init__.py from ZibinDong/ActionCodec2: direct link, hf CLI and curl.
- Browser
- Download file 452 Bytes
-
https://huggingface.co/ZibinDong/ActionCodec2/resolve/ef300dff7f6ffa4e4e7eef5fdfef0fd684e7d573/runtime/backends/actioncodec2/__init__.py
- Command line
-
hf download hf://ZibinDong/ActionCodec2@ef300dff7f6ffa4e4e7eef5fdfef0fd684e7d573/runtime/backends/actioncodec2/__init__.py
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curl -L -o __init__.py https://huggingface.co/ZibinDong/ActionCodec2/resolve/ef300dff7f6ffa4e4e7eef5fdfef0fd684e7d573/runtime/backends/actioncodec2/__init__.py
452 Bytes
| """Shared physical models, layouts, and dense and sparse routers.""" | |
| from .configuration import ActionCodec2Config, ConfigError | |
| from .presets import ComponentSpec, PresetConfig | |
| from .processor import ActionCodec2Processor | |
| from .routing import ActionCodec2Router | |
| from .trajectory import ActiveTrajectory | |
| __all__ = ["ActionCodec2Config", "ConfigError", "ComponentSpec", "PresetConfig", "ActionCodec2Processor", "ActionCodec2Router", "ActiveTrajectory"] | |