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/networkx/__init__.py from jonl521/modeling_simple: direct link, hf CLI and curl.
- Browser
- Download file 1.63 kB
-
https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/networkx/__init__.py
- Command line
-
hf download hf://jonl521/modeling_simple/.venv/lib/python3.14/site-packages/networkx/__init__.py
-
curl -L -o __init__.py https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/networkx/__init__.py
1.63 kB
| """ | |
| NetworkX | |
| ======== | |
| NetworkX is a Python package for the creation, manipulation, and study of the | |
| structure, dynamics, and functions of complex networks. | |
| See https://networkx.org for complete documentation. | |
| """ | |
| __version__ = "3.6.1" | |
| # These are imported in order as listed | |
| from networkx.lazy_imports import _lazy_import | |
| from networkx.exception import * | |
| from networkx import utils | |
| from networkx.utils import _clear_cache, _dispatchable | |
| # load_and_call entry_points, set configs | |
| config = utils.backends._set_configs_from_environment() | |
| utils.config = utils.configs.config = config # type: ignore[attr-defined] | |
| from networkx import classes | |
| from networkx.classes import filters | |
| from networkx.classes import * | |
| from networkx import convert | |
| from networkx.convert import * | |
| from networkx import convert_matrix | |
| from networkx.convert_matrix import * | |
| from networkx import relabel | |
| from networkx.relabel import * | |
| from networkx import generators | |
| from networkx.generators import * | |
| from networkx import readwrite | |
| from networkx.readwrite import * | |
| # Need to test with SciPy, when available | |
| from networkx import algorithms | |
| from networkx.algorithms import * | |
| from networkx import linalg | |
| from networkx.linalg import * | |
| from networkx import drawing | |
| from networkx.drawing import * | |
| def __getattr__(name): | |
| if name == "random_tree": | |
| raise AttributeError( | |
| "nx.random_tree was removed in version 3.4. Use `nx.random_labeled_tree` instead.\n" | |
| "See: https://networkx.org/documentation/latest/release/release_3.4.html" | |
| ) | |
| raise AttributeError(f"module 'networkx' has no attribute '{name}'") | |