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
File size: 698 Bytes
1bfc3f0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | """ActionCodec2Sparse: ActionCodec2 physics with a zero-free spatio-temporal Set-BPE token layer."""
from .fitting import SparseProfileTrainer, sparse_profile_config
from .grammar import SparseGrammar
from .layout import SparseLayout
from .processor import ActionCodec2SparseProcessor
from .routing import ActionCodec2SparseRouter, RoutedSparseGrammar
from .training import fit_sparse_vocabulary
from .vocabulary import SparseVocabulary
__all__ = [
"ActionCodec2SparseProcessor",
"ActionCodec2SparseRouter",
"RoutedSparseGrammar",
"SparseGrammar",
"SparseLayout",
"SparseProfileTrainer",
"SparseVocabulary",
"fit_sparse_vocabulary",
"sparse_profile_config",
]
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