hypersteer/pyproject.toml at master · HyperStuff/hypersteer · GitHub

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# PyTorch is now automatically selected based on platform:
# - CPU builds on macOS/Windows
# - CUDA builds on Linux
#
# For manual control, you can still use: UV_TORCH_BACKEND=auto uv sync

[project]
name = "hypersteer"
version = "0.1.0"
description = "Steering models with hypernetworks."
requires-python = ">=3.10"
dependencies = [
    "einops>=0.8.0",
    "matplotlib>=3.8.0",
    "pandas>=2.0.0",
    "scikit-learn>=1.3.0",
    "seaborn>=0.13.0",
    "tiktoken>=0.5.0",
    "wandb>=0.16.0",
    "together>=1.0.0",
    "hydra-colorlog>=1.0.0",
    "hydra-core>=1.3.0",
    "hydra_colorlog>=1.0.0",
    "omegaconf>=2.3.0",
    "pydantic>=2.0.0",
    "python-dotenv>=1.0.0",
    "pyyaml>=6.0.0",
    "tqdm>=4.65.0",
    "datasets>=2.14.0",
    "httpx>=0.24.0",
    "openai>=1.0.0",
    "pyvene==0.1.7",
    "transformers==4.45.1",
    "adjusttext>=1.0.0",
    "optuna==4.3.0",
]

[project.scripts]
inference = "hypersteer.scripts.inference:main"
generate = "hypersteer.scripts.generate:main"
train = "hypersteer.scripts.train:main"
evaluate = "hypersteer.scripts.evaluate:main"

[project.optional-dependencies]
dev = [
    "ipykernel>=6.25.0",
    "jupyter>=1.0.0",
    "pre-commit>=3.0.0",
    "ai_commit @ git+https://github.com/sidnb13/toolbox.git#subdirectory=src/ai_commit",
]
ray = [
    "hydra-ray-jobs-launcher @ git+https://github.com/sidnb13/hydra#egg=hydra-ray-jobs-launcher&subdirectory=plugins/hydra_ray_jobs_launcher",
]
flash-attn = [
    "flash-attn>=2.0.0",
]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[tool.hatch.metadata]
allow-direct-references = true

[tool.hatch.build.targets.wheel]
packages = ["hypersteer"]