Instructions to use ZibinDong/ActionCodec2-Extended-5mm-2k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZibinDong/ActionCodec2-Extended-5mm-2k with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ZibinDong/ActionCodec2-Extended-5mm-2k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download runtime/common/_types.py from ZibinDong/ActionCodec2-Extended-5mm-2k: direct link, hf CLI and curl.
- Browser
- Download file 250 Bytes
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https://huggingface.co/ZibinDong/ActionCodec2-Extended-5mm-2k/resolve/main/runtime/common/_types.py
- Command line
-
hf download hf://ZibinDong/ActionCodec2-Extended-5mm-2k/runtime/common/_types.py
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curl -L -o _types.py https://huggingface.co/ZibinDong/ActionCodec2-Extended-5mm-2k/resolve/main/runtime/common/_types.py
250 Bytes
| """Shared lightweight types for the frontend layer.""" | |
| from __future__ import annotations | |
| import numpy as np | |
| import torch | |
| ActionIdxBatch = list[list[int]] | |
| TokenIds = list[int] | np.ndarray | torch.Tensor | |
| __all__ = ["ActionIdxBatch", "TokenIds"] | |