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/torch/jit/_logging.py from jonl521/modeling_simple: direct link, hf CLI and curl.
- Browser
- Download file 257 Bytes
-
https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/torch/jit/_logging.py
- Command line
-
hf download hf://jonl521/modeling_simple/.venv/lib/python3.14/site-packages/torch/jit/_logging.py
-
curl -L -o _logging.py https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/torch/jit/_logging.py
257 Bytes
| import torch | |
| add_stat_value = torch.ops.prim.AddStatValue | |
| set_logger = torch._C._logging_set_logger | |
| LockingLogger = torch._C.LockingLogger | |
| AggregationType = torch._C.AggregationType | |
| NoopLogger = torch._C.NoopLogger | |
| time_point = torch.ops.prim.TimePoint | |