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/_sfc64.pyi from jonl521/modeling_simple: direct link, hf CLI and curl.
- Browser
- Download file 691 Bytes
-
https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/numpy/random/_sfc64.pyi
- Command line
-
hf download hf://jonl521/modeling_simple/.venv/lib/python3.14/site-packages/numpy/random/_sfc64.pyi
-
curl -L -o _sfc64.pyi https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/numpy/random/_sfc64.pyi
691 Bytes
| from typing import TypedDict, type_check_only | |
| from numpy import uint64 | |
| from numpy._typing import NDArray, _ArrayLikeInt_co | |
| from numpy.random.bit_generator import BitGenerator, SeedSequence | |
| __all__ = ["SFC64"] | |
| @type_check_only | |
| class _SFC64Internal(TypedDict): | |
| state: NDArray[uint64] | |
| @type_check_only | |
| class _SFC64State(TypedDict): | |
| bit_generator: str | |
| state: _SFC64Internal | |
| has_uint32: int | |
| uinteger: int | |
| class SFC64(BitGenerator): | |
| def __init__(self, seed: _ArrayLikeInt_co | SeedSequence | None = ...) -> None: ... | |
| @property # type: ignore[override] | |
| def state(self) -> _SFC64State: ... | |
| @state.setter | |
| def state(self, value: _SFC64State) -> None: ... | |