Instructions to use HaadesX/Iconoclast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HaadesX/Iconoclast with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("HaadesX/Iconoclast", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,674 Bytes
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name = "iconoclast-llm"
version = "0.1.0"
description = "Research framework for discriminative representation editing in open-weight language models"
license = "AGPL-3.0-or-later"
authors = [
{ name = "Varesh Patel" }
]
requires-python = ">=3.10"
keywords = ["llm", "transformer", "alignment", "safety", "representation-editing"]
classifiers = [
"Development Status :: 3 - Alpha",
"Environment :: Console",
"Environment :: GPU",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
]
dependencies = [
"accelerate~=1.13",
"datasets~=4.7",
"hf-transfer~=0.1",
"huggingface-hub~=1.7",
"immutabledict~=4.3",
"kernels~=0.12",
"numpy~=2.2",
"optuna~=4.7",
"peft~=0.18",
"psutil~=7.2",
"pydantic-settings~=2.13",
"questionary~=2.1",
"rich~=14.3",
"transformers~=5.3",
]
[project.optional-dependencies]
research = [
"geom-median~=0.1",
"imageio~=2.37",
"matplotlib~=3.10",
"pacmap~=0.8",
"scikit-learn~=1.7",
]
benchmark = [
"lm-eval[hf]~=0.4",
]
quantized = [
"bitsandbytes~=0.49",
]
[dependency-groups]
dev = [
"ruff>=0.14.5",
"ty>=0.0.5",
]
[project.scripts]
iconoclast = "iconoclast.main:main"
[build-system]
requires = ["uv_build>=0.8.11,<0.9.0"]
build-backend = "uv_build"
[tool.uv.build-backend]
module-name = "iconoclast"
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