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/frontend/actioncodec2/loading.py from ZibinDong/ActionCodec2: direct link, hf CLI and curl.
- Browser
- Download file 278 Bytes
-
https://huggingface.co/ZibinDong/ActionCodec2/resolve/ef300dff7f6ffa4e4e7eef5fdfef0fd684e7d573/runtime/frontend/actioncodec2/loading.py
- Command line
-
hf download hf://ZibinDong/ActionCodec2@ef300dff7f6ffa4e4e7eef5fdfef0fd684e7d573/runtime/frontend/actioncodec2/loading.py
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curl -L -o loading.py https://huggingface.co/ZibinDong/ActionCodec2/resolve/ef300dff7f6ffa4e4e7eef5fdfef0fd684e7d573/runtime/frontend/actioncodec2/loading.py
278 Bytes
| """Shared release loader bindings.""" | |
| from functools import partial | |
| from ..loading import load_release_router as _load, describe_release as _describe | |
| load_release_router = partial(_load, tokenizer='actioncodec2') | |
| describe_release = partial(_describe, tokenizer='actioncodec2') | |