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/_common.pyi from jonl521/modeling_simple: direct link, hf CLI and curl.
- Browser
- Download file 421 Bytes
-
https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/numpy/random/_common.pyi
- Command line
-
hf download hf://jonl521/modeling_simple/.venv/lib/python3.14/site-packages/numpy/random/_common.pyi
-
curl -L -o _common.pyi https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/numpy/random/_common.pyi
421 Bytes
| from collections.abc import Callable | |
| from typing import Any, NamedTuple, TypeAlias | |
| import numpy as np | |
| __all__: list[str] = ["interface"] | |
| _CDataVoidPointer: TypeAlias = Any | |
| class interface(NamedTuple): | |
| state_address: int | |
| state: _CDataVoidPointer | |
| next_uint64: Callable[..., np.uint64] | |
| next_uint32: Callable[..., np.uint32] | |
| next_double: Callable[..., np.float64] | |
| bit_generator: _CDataVoidPointer | |