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: 1,651 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 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | """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
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