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
File size: 733 Bytes
b4740c6 | 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 | """
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
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