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pyproject.toml
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90 lines (81 loc) · 1.93 KB
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[build-system]
requires = ["setuptools>=68", "wheel"]
build-backend = "setuptools.backends.legacy:build"
[project]
name = "spectralquant"
version = "0.1.0"
description = "Exploiting eigenspectral structure for better-than-optimal vector quantization"
readme = "README.md"
license = { text = "MIT" }
authors = [
{ name = "Ashwin Gopinath", email = "ashwin@sentra.ai" },
{ name = "Sambartha Ray Barman" },
]
keywords = [
"KV cache compression",
"quantization",
"large language models",
"eigenspectral analysis",
"vector quantization",
"attention",
]
classifiers = [
"Development Status :: 3 - Alpha",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
]
requires-python = ">=3.10"
dependencies = [
"torch>=2.2.0",
"transformers>=4.40.0",
"datasets",
"scipy",
"scikit-learn",
"matplotlib",
"seaborn",
"pandas",
"numpy",
"tqdm",
"rich",
]
[project.optional-dependencies]
dev = [
"pytest",
"pytest-cov",
"black",
"ruff",
"mypy",
]
benchmarks = [
"rouge-score",
"nltk",
"sacrebleu",
"sentence-transformers",
]
[project.urls]
Repository = "https://github.com/dynamis-labs/spectralquant"
Paper = "https://arxiv.org/abs/XXXX.XXXXX"
[tool.setuptools.packages.find]
where = ["src"]
[tool.pytest.ini_options]
testpaths = ["tests"]
addopts = "-v --tb=short"
[tool.ruff]
line-length = 100
target-version = "py310"
[tool.ruff.lint]
select = ["E", "F", "W", "I", "N", "UP"]
ignore = ["E501"]
[tool.black]
line-length = 100
target-version = ["py310", "py311", "py312"]
[tool.mypy]
python_version = "3.10"
warn_return_any = true
warn_unused_configs = true
ignore_missing_imports = true