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/jinja2/optimizer.py from kunaliitkgp09/multi-model-orchestrator: direct link, hf CLI and curl.
- Browser
- Download file 1.65 kB
-
https://huggingface.co/kunaliitkgp09/multi-model-orchestrator/resolve/main/multi_model_env/lib/python3.13/site-packages/jinja2/optimizer.py
- Command line
-
hf download hf://kunaliitkgp09/multi-model-orchestrator/multi_model_env/lib/python3.13/site-packages/jinja2/optimizer.py
-
curl -L -o optimizer.py https://huggingface.co/kunaliitkgp09/multi-model-orchestrator/resolve/main/multi_model_env/lib/python3.13/site-packages/jinja2/optimizer.py
1.65 kB
| """The optimizer tries to constant fold expressions and modify the AST | |
| in place so that it should be faster to evaluate. | |
| Because the AST does not contain all the scoping information and the | |
| compiler has to find that out, we cannot do all the optimizations we | |
| want. For example, loop unrolling doesn't work because unrolled loops | |
| would have a different scope. The solution would be a second syntax tree | |
| that stored the scoping rules. | |
| """ | |
| import typing as t | |
| from . import nodes | |
| from .visitor import NodeTransformer | |
| if t.TYPE_CHECKING: | |
| from .environment import Environment | |
| def optimize(node: nodes.Node, environment: "Environment") -> nodes.Node: | |
| """The context hint can be used to perform an static optimization | |
| based on the context given.""" | |
| optimizer = Optimizer(environment) | |
| return t.cast(nodes.Node, optimizer.visit(node)) | |
| class Optimizer(NodeTransformer): | |
| def __init__(self, environment: "t.Optional[Environment]") -> None: | |
| self.environment = environment | |
| def generic_visit( | |
| self, node: nodes.Node, *args: t.Any, **kwargs: t.Any | |
| ) -> nodes.Node: | |
| node = super().generic_visit(node, *args, **kwargs) | |
| # Do constant folding. Some other nodes besides Expr have | |
| # as_const, but folding them causes errors later on. | |
| if isinstance(node, nodes.Expr): | |
| try: | |
| return nodes.Const.from_untrusted( | |
| node.as_const(args[0] if args else None), | |
| lineno=node.lineno, | |
| environment=self.environment, | |
| ) | |
| except nodes.Impossible: | |
| pass | |
| return node | |