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/requests/hooks.py from kunaliitkgp09/multi-model-orchestrator: direct link, hf CLI and curl.
- Browser
- Download file 733 Bytes
-
https://huggingface.co/kunaliitkgp09/multi-model-orchestrator/resolve/main/multi_model_env/lib/python3.13/site-packages/requests/hooks.py
- Command line
-
hf download hf://kunaliitkgp09/multi-model-orchestrator/multi_model_env/lib/python3.13/site-packages/requests/hooks.py
-
curl -L -o hooks.py https://huggingface.co/kunaliitkgp09/multi-model-orchestrator/resolve/main/multi_model_env/lib/python3.13/site-packages/requests/hooks.py
733 Bytes
| """ | |
| requests.hooks | |
| ~~~~~~~~~~~~~~ | |
| This module provides the capabilities for the Requests hooks system. | |
| Available hooks: | |
| ``response``: | |
| The response generated from a Request. | |
| """ | |
| HOOKS = ["response"] | |
| def default_hooks(): | |
| return {event: [] for event in HOOKS} | |
| # TODO: response is the only one | |
| def dispatch_hook(key, hooks, hook_data, **kwargs): | |
| """Dispatches a hook dictionary on a given piece of data.""" | |
| hooks = hooks or {} | |
| hooks = hooks.get(key) | |
| if hooks: | |
| if hasattr(hooks, "__call__"): | |
| hooks = [hooks] | |
| for hook in hooks: | |
| _hook_data = hook(hook_data, **kwargs) | |
| if _hook_data is not None: | |
| hook_data = _hook_data | |
| return hook_data | |