Instructions to use jonl521/modeling_simple with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonl521/modeling_simple with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jonl521/modeling_simple", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download .venv/lib/python3.14/site-packages/numpy/random/_mt19937.pyi from jonl521/modeling_simple: direct link, hf CLI and curl.
- Browser
- Download file 822 Bytes
-
https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/numpy/random/_mt19937.pyi
- Command line
-
hf download hf://jonl521/modeling_simple/.venv/lib/python3.14/site-packages/numpy/random/_mt19937.pyi
-
curl -L -o _mt19937.pyi https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/numpy/random/_mt19937.pyi
822 Bytes
| from typing import TypedDict, type_check_only | |
| from numpy import uint32 | |
| from numpy._typing import _ArrayLikeInt_co | |
| from numpy.random.bit_generator import BitGenerator, SeedSequence | |
| from numpy.typing import NDArray | |
| __all__ = ["MT19937"] | |
| @type_check_only | |
| class _MT19937Internal(TypedDict): | |
| key: NDArray[uint32] | |
| pos: int | |
| @type_check_only | |
| class _MT19937State(TypedDict): | |
| bit_generator: str | |
| state: _MT19937Internal | |
| class MT19937(BitGenerator): | |
| def __init__(self, seed: _ArrayLikeInt_co | SeedSequence | None = ...) -> None: ... | |
| def _legacy_seeding(self, seed: _ArrayLikeInt_co) -> None: ... | |
| def jumped(self, jumps: int = 1) -> MT19937: ... | |
| @property # type: ignore[override] | |
| def state(self) -> _MT19937State: ... | |
| @state.setter | |
| def state(self, value: _MT19937State) -> None: ... | |