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
File size: 1,262 Bytes
2216aae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | import subprocess
import sys
import textwrap
import pytest
from numpy.testing import IS_WASM
@pytest.mark.skipif(IS_WASM, reason="can't start subprocess")
def test_lazy_load():
# gh-22045. lazyload doesn't import submodule names into the namespace
# Test within a new process, to ensure that we do not mess with the
# global state during the test run (could lead to cryptic test failures).
# This is generally unsafe, especially, since we also reload the C-modules.
code = textwrap.dedent(r"""
import sys
from importlib.util import LazyLoader, find_spec, module_from_spec
# create lazy load of numpy as np
spec = find_spec("numpy")
module = module_from_spec(spec)
sys.modules["numpy"] = module
loader = LazyLoader(spec.loader)
loader.exec_module(module)
np = module
# test a subpackage import
from numpy.lib import recfunctions # noqa: F401
# test triggering the import of the package
np.ndarray
""")
p = subprocess.run(
(sys.executable, '-c', code),
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
encoding='utf-8',
check=False,
)
assert p.returncode == 0, p.stdout
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