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/__init__.pxd from jonl521/modeling_simple: direct link, hf CLI and curl.
- Browser
- Download file 431 Bytes
-
https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/numpy/random/__init__.pxd
- Command line
-
hf download hf://jonl521/modeling_simple/.venv/lib/python3.14/site-packages/numpy/random/__init__.pxd
-
curl -L -o __init__.pxd https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/numpy/random/__init__.pxd
431 Bytes
| cimport numpy as np | |
| from libc.stdint cimport uint32_t, uint64_t | |
| cdef extern from "numpy/random/bitgen.h": | |
| struct bitgen: | |
| void *state | |
| uint64_t (*next_uint64)(void *st) nogil | |
| uint32_t (*next_uint32)(void *st) nogil | |
| double (*next_double)(void *st) nogil | |
| uint64_t (*next_raw)(void *st) nogil | |
| ctypedef bitgen bitgen_t | |
| from numpy.random.bit_generator cimport BitGenerator, SeedSequence | |