mirror of https://github.com/razor-ai/soup.git
105 lines
3.7 KiB
TOML
105 lines
3.7 KiB
TOML
[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[project]
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name = "soup-cli"
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version = "0.53.10"
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description = "Fine-tune LLMs in one command. No SSH, no config hell."
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readme = "README.md"
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license = "Apache-2.0"
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requires-python = ">=3.9"
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authors = [
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{ name = "Soup Team" },
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]
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keywords = ["llm", "fine-tuning", "lora", "qlora", "machine-learning"]
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classifiers = [
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"Development Status :: 3 - Alpha",
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"Intended Audience :: Developers",
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"Intended Audience :: Science/Research",
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"License :: OSI Approved :: Apache Software License",
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"Programming Language :: Python :: 3",
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"Topic :: Scientific/Engineering :: Artificial Intelligence",
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]
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dependencies = [
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"typer>=0.9.0,<0.21.0",
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"rich>=13.0.0",
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"pydantic>=2.0.0",
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"pyyaml>=6.0",
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"torch>=2.0.0",
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"transformers>=4.36.0,<5.0.0",
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"peft>=0.7.0",
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"trl>=0.7.0",
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"datasets>=2.14.0",
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"bitsandbytes>=0.41.0",
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"accelerate>=0.25.0",
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"huggingface-hub>=0.16.0",
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"plotext>=5.2.0",
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]
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[project.optional-dependencies]
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eval = ["lm-eval>=0.4.0"]
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data = ["datasketch>=1.6.0"]
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wandb = ["wandb>=0.15.0,<0.18.0"]
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dev = ["pytest>=7.0", "ruff>=0.1.0", "pytest-cov>=4.0", "httpx>=0.24.0"]
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ui = ["fastapi>=0.104.0", "uvicorn>=0.24.0"]
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serve = ["fastapi>=0.104.0", "uvicorn>=0.24.0"]
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serve-fast = ["vllm>=0.4.0", "fastapi>=0.104.0", "uvicorn>=0.24.0"]
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generate = ["httpx>=0.24.0"]
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deepspeed = ["deepspeed>=0.12.0"]
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fast = ["unsloth>=2024.8"]
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vision = ["Pillow>=9.0.0"]
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qat = ["torchao>=0.4.0"]
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liger = ["liger-kernel>=0.3.0"]
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ring-attn = ["ring-flash-attn>=0.1.0"]
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onnx = ["optimum[onnxruntime]>=1.16.0"]
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tensorrt = ["tensorrt_llm>=0.9.0"]
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audio = ["librosa>=0.10.0", "soundfile>=0.12.0"]
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awq = ["autoawq>=0.2.0"]
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gptq = ["auto-gptq>=0.7.0"]
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sglang = ["sglang>=0.2.0", "fastapi>=0.104.0", "uvicorn>=0.24.0"]
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mlx = ["mlx>=0.20.0", "mlx-lm>=0.20.0"]
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cce = ["cut-cross-entropy>=24.10.0"]
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tui = ["textual>=0.50.0"]
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# v0.53.8 #89 — bundle MLflow / SwanLab / Trackio for `--tracker` users.
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trackers = ["mlflow>=2.0.0", "swanlab>=0.3.0", "trackio>=0.0.1"]
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# v0.53.8 #85 — fsspec backends for remote dataset loading (s3 / gs / az / oci).
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remote = ["fsspec>=2024.1.0", "s3fs>=2024.1.0", "gcsfs>=2024.1.0", "adlfs>=2024.1.0"]
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# v0.53.10 #150 — bundle scikit-optimize so `soup data mix --optimize` runs the
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# Bayesian-style loop instead of falling back to the v0.48.0 Dirichlet sampler.
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mix = ["scikit-optimize>=0.9.0"]
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# v0.53.10 #113 — production-grade data quality: langdetect (language) +
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# presidio-analyzer (PII). Llama-Guard-3-1B is documented as a manual recipe
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# (license + ~600 MB weight blob too large to bundle by default).
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data-pro = ["langdetect>=1.0.9", "presidio-analyzer>=2.2.0"]
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[project.scripts]
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soup = "soup_cli.cli:run"
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[project.urls]
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Homepage = "https://github.com/MakazhanAlpamys/Soup"
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Repository = "https://github.com/MakazhanAlpamys/Soup"
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Issues = "https://github.com/MakazhanAlpamys/Soup/issues"
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[tool.hatch.build.targets.wheel]
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packages = ["soup_cli"]
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# v0.53.8 #93 — include bundled fixture JSONLs as package data so
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# `soup data demo` works in zipapp / namespace-package installs.
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# Hatchling's ``packages = ["soup_cli"]`` already recurses into the
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# package directory, so we use the artifacts directive (NOT
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# force-include, which double-shipped the files in v0.53.8 and produced
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# a "duplicate filename in local headers" 400 from PyPI upload).
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artifacts = ["soup_cli/data/_fixtures/*.jsonl"]
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[tool.ruff]
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target-version = "py39"
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line-length = 100
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[tool.ruff.lint]
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select = ["E", "F", "I", "N", "W"]
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[tool.pytest.ini_options]
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testpaths = ["tests"]
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markers = ["smoke: slow smoke tests that download models and run training (run with: pytest -m smoke)"]
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addopts = "-m 'not smoke' --cov=soup_cli --cov-fail-under=50 --cov-report=term-missing:skip-covered"
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