Text-to-Image
Transformers
PyTorch
English
multi-model-orchestrator
ai
machine-learning
multimodal
image-captioning
orchestration
Instructions to use kunaliitkgp09/multi-model-orchestrator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kunaliitkgp09/multi-model-orchestrator with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kunaliitkgp09/multi-model-orchestrator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download multi_model_env/lib/python3.13/site-packages/setuptools/logging.py from kunaliitkgp09/multi-model-orchestrator: direct link, hf CLI and curl.
- Browser
- Download file 1.26 kB
-
https://huggingface.co/kunaliitkgp09/multi-model-orchestrator/resolve/main/multi_model_env/lib/python3.13/site-packages/setuptools/logging.py
- Command line
-
hf download hf://kunaliitkgp09/multi-model-orchestrator/multi_model_env/lib/python3.13/site-packages/setuptools/logging.py
-
curl -L -o logging.py https://huggingface.co/kunaliitkgp09/multi-model-orchestrator/resolve/main/multi_model_env/lib/python3.13/site-packages/setuptools/logging.py
1.26 kB
| import inspect | |
| import logging | |
| import sys | |
| from . import monkey | |
| import distutils.log | |
| def _not_warning(record): | |
| return record.levelno < logging.WARNING | |
| def configure() -> None: | |
| """ | |
| Configure logging to emit warning and above to stderr | |
| and everything else to stdout. This behavior is provided | |
| for compatibility with distutils.log but may change in | |
| the future. | |
| """ | |
| err_handler = logging.StreamHandler() | |
| err_handler.setLevel(logging.WARNING) | |
| out_handler = logging.StreamHandler(sys.stdout) | |
| out_handler.addFilter(_not_warning) | |
| handlers = err_handler, out_handler | |
| logging.basicConfig( | |
| format="{message}", style='{', handlers=handlers, level=logging.DEBUG | |
| ) | |
| if inspect.ismodule(distutils.dist.log): | |
| monkey.patch_func(set_threshold, distutils.log, 'set_threshold') | |
| # For some reason `distutils.log` module is getting cached in `distutils.dist` | |
| # and then loaded again when patched, | |
| # implying: id(distutils.log) != id(distutils.dist.log). | |
| # Make sure the same module object is used everywhere: | |
| distutils.dist.log = distutils.log | |
| def set_threshold(level: int) -> int: | |
| logging.root.setLevel(level * 10) | |
| return set_threshold.unpatched(level) | |