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/_philox.pyi from jonl521/modeling_simple: direct link, hf CLI and curl.
- Browser
- Download file 1.01 kB
-
https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/numpy/random/_philox.pyi
- Command line
-
hf download hf://jonl521/modeling_simple/.venv/lib/python3.14/site-packages/numpy/random/_philox.pyi
-
curl -L -o _philox.pyi https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/numpy/random/_philox.pyi
1.01 kB
| from typing import TypedDict, type_check_only | |
| from numpy import uint64 | |
| from numpy._typing import _ArrayLikeInt_co | |
| from numpy.random.bit_generator import BitGenerator, SeedSequence | |
| from numpy.typing import NDArray | |
| __all__ = ["Philox"] | |
| class _PhiloxInternal(TypedDict): | |
| counter: NDArray[uint64] | |
| key: NDArray[uint64] | |
| class _PhiloxState(TypedDict): | |
| bit_generator: str | |
| state: _PhiloxInternal | |
| buffer: NDArray[uint64] | |
| buffer_pos: int | |
| has_uint32: int | |
| uinteger: int | |
| class Philox(BitGenerator): | |
| def __init__( | |
| self, | |
| seed: _ArrayLikeInt_co | SeedSequence | None = ..., | |
| counter: _ArrayLikeInt_co | None = ..., | |
| key: _ArrayLikeInt_co | None = ..., | |
| ) -> None: ... | |
| # type: ignore[override] | |
| def state(self) -> _PhiloxState: ... | |
| def state(self, value: _PhiloxState) -> None: ... | |
| def jumped(self, jumps: int = 1) -> Philox: ... | |
| def advance(self, delta: int) -> Philox: ... | |