Instructions to use Angshul/higher-order-pruner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Angshul/higher-order-pruner with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Angshul/higher-order-pruner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 679 Bytes
cdfcfcf | 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 | [build-system]
requires = ["setuptools>=70", "wheel"]
build-backend = "setuptools.build_meta"
[project]
name = "higher-order-pruner"
version = "0.1.0"
description = "Model-agnostic higher-order pruning for PyTorch and Hugging Face models"
readme = "README.md"
requires-python = ">=3.10"
license = {text = "Apache-2.0"}
authors = [{name = "Angshul Majumdar"}]
dependencies = [
"torch>=2.4,<3",
"transformers>=4.57,<5",
"accelerate>=1.2,<2",
"safetensors>=0.5,<1",
"huggingface_hub>=0.34,<2",
]
[project.optional-dependencies]
test = ["pytest>=8,<10"]
[tool.setuptools.packages.find]
include = ["higher_order_pruner*"]
[tool.pytest.ini_options]
testpaths = ["tests"]
|