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
modeling_simple / .venv /lib /python3.14 /site-packages /numpy /random /_common.cpython-314-darwin.so
Download .venv/lib/python3.14/site-packages/numpy/random/_common.cpython-314-darwin.so from jonl521/modeling_simple: direct link, hf CLI and curl.
- Browser
- Download file 232 kB
-
https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/numpy/random/_common.cpython-314-darwin.so
- Command line
-
hf download hf://jonl521/modeling_simple/.venv/lib/python3.14/site-packages/numpy/random/_common.cpython-314-darwin.so
-
curl -L -o _common.cpython-314-darwin.so https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/numpy/random/_common.cpython-314-darwin.so
232 kB
- Xet hash:
- 36d1f179fb20c11d409ca3a83ccb986e261efc86b715c5c6d590f07fd9e88a4d
- Size of remote file:
- 232 kB
- SHA256:
- 7048111631dbd8157fd6b4bbef0faaf006ed4e32283320bfa651d3b6d4a7bf56
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.