fix: fix remaining issues

README.md

@@ -1,6 +1,6 @@
 <img width="128" height="128" align="right" alt="Logo" src="https://github.com/user-attachments/assets/df5f2840-2f92-4991-aa57-252747d7182e" />
 
-# Heretic: Fully automatic censorship removal for language models<br><br>[![Discord](https://img.shields.io/discord/1447831134212984903?color=5865F2&label=discord&labelColor=black&logo=discord&logoColor=white&style=for-the-badge)](https://discord.gg/gdXc48gSyT) [![Follow us on Hugging Face](https://huggingface.co/datasets/huggingface/badges/resolve/main/follow-us-on-hf-md-dark.svg)](https://huggingface.co/heretic-org) [![Codeberg mirror](https://img.shields.io/badge/Codeberg%20mirror-black?logo=codeberg&style=for-the-badge)](https://codeberg.org/p-e-w/heretic)
+# Heretic: Fully automatic censorship removal for language models<br><br>[![Discord](https://img.shields.io/discord/1447831134212984903?color=5865F2&label=discord&labelColor=black&logo=discord&logoColor=white&style=for-the-badge)](https://discord.gg/gdXc48gSyT) [![Follow us on Hugging Face](https://huggingface.co/datasets/huggingface/badges/resolve/main/follow-us-on-hf-md-dark.svg)](https://huggingface.co/heretic-org)
 
 [![#1 Repository of the Day](https://trendshift.io/api/badge/repositories/20538)](https://trendshift.io/repositories/20538)
 
@@ -20,11 +20,6 @@ as possible. Using Heretic does not require an understanding of transformer
 internals. In fact, anyone who knows how to run a command-line program
 can use Heretic to decensor language models.
 
-Heretic supports most dense models, including many multimodal models,
-several different MoE architectures, and even some hybrid models like Qwen3.5.
-Pure state-space models and certain other research architectures are not yet
-supported out of the box.
-
 <img width="650" height="715" alt="Screenshot" src="https://github.com/user-attachments/assets/d71a5efa-d6be-4705-a817-63332afb2d15" />
 
 &nbsp;
@@ -70,15 +65,15 @@ Heretic have been well-received by users (links and emphasis added):
 > Has been the best unquantized abliterated model that I have been able to run on 16gb vram."
 > [*(Link to comment)*](https://old.reddit.com/r/LocalLLaMA/comments/1phjxca/im_calling_these_people_out_right_now/nt06tji/)
 
-Heretic models have also been independently benchmarked using standard metrics
-like MMLU and GSM8K, and have been found to compare favorably with models
-produced by competing abliteration tools:
-[1](https://old.reddit.com/r/LocalLLaMA/comments/1sojjoc/abliterlitics_benchmark_and_tensor_analysis/),
-[2](https://old.reddit.com/r/LocalLLaMA/comments/1sy18lx/abliterlitics_benchmarks_and_tensor_comparison/).
+Heretic supports most dense models, including many multimodal models, and
+several different MoE architectures. It does not yet support SSMs/hybrid models,
+models with inhomogeneous layers, and certain novel attention systems.
 
-The community has created and published
-[well over 3000](https://huggingface.co/models?other=heretic)
-models with Heretic.
+You can find a small collection of models that have been decensored using Heretic
+[on Hugging Face](https://huggingface.co/collections/p-e-w/the-bestiary),
+and the community has created and published
+[well over 1,000](https://huggingface.co/models?other=heretic)
+Heretic models in addition to those.
 
 
 ## Usage
@@ -93,21 +88,6 @@ heretic Qwen/Qwen3-4B-Instruct-2507
 
 Replace `Qwen/Qwen3-4B-Instruct-2507` with whatever model you want to decensor.
 
-> [!IMPORTANT]
->
-> While PyTorch 2.2 is the minimum version of PyTorch needed for Heretic to work,
-> some models and configurations might require features only found in
-> later versions. For example, loading MXFP4-quantized models like gpt-oss
-> uses `torch.accelerator`, which was added in PyTorch 2.6.
-
-> [!TIP]
->
-> Heretic uses [uv](https://docs.astral.sh/uv/) for dependency management,
-> and the repository includes a `uv.lock` file pinning every package version.
-> If you already use uv (and you probably should!), you can just clone the repo
-> and run Heretic with `uv run heretic`, which ensures that your dependencies
-> match those used by the developers, improving reliability and security.
-
 The process is fully automatic and does not require configuration; however,
 Heretic has a variety of configuration parameters that can be changed for
 greater control. Run `heretic --help` to see available command-line options,
@@ -123,7 +103,7 @@ models. Set the `quantization` option to `bnb_4bit` to enable quantization.
 
 After Heretic has finished decensoring a model, you are given the option to
 save the model, upload it to Hugging Face, chat with it to test how well it works,
-run standard benchmarks on it, or any combination of those actions.
+or any combination of those actions.
 
 
 ## Research features

config.default.toml

@@ -27,12 +27,6 @@ device_map = "auto"
 # Maximum memory to allocate per device.
 # max_memory = { "0" = "20GB", "cpu" = "64GB" }
 
-# Whether to move intermediate analysis tensors (such as residuals and logprobs)
-# to CPU memory as soon as possible to reduce peak VRAM usage.
-# This lowers peak VRAM usage during residual analysis and evaluation,
-# but may slightly reduce performance due to host/device transfers.
-offload_outputs_to_cpu = true
-
 # Number of input sequences to process in parallel (0 = auto).
 batch_size = 0  # auto
 
@@ -42,32 +36,6 @@ max_batch_size = 128
 # Maximum number of tokens to generate for each response.
 max_response_length = 100
 
-# List of pairs of the form [cot_initializer, closed_cot_block] used to skip
-# the Chain-of-Thought block in responses, so that evaluation happens
-# at the start of the actual response.
-chain_of_thought_skips = [
-    # Most thinking models.
-    [
-        "<think>",
-        "<think></think>",
-    ],
-    # gpt-oss.
-    [
-        "<|channel|>analysis<|message|>",
-        "<|channel|>analysis<|message|><|end|><|start|>assistant<|channel|>final<|message|>",
-    ],
-    # Unknown, suggested by user.
-    [
-        "<thought>",
-        "<thought></thought>",
-    ],
-    # Unknown, suggested by user.
-    [
-        "[THINK]",
-        "[THINK][/THINK]",
-    ],
-]
-
 # Whether to print prompt/response pairs when counting refusals.
 print_responses = false
 
@@ -96,13 +64,13 @@ kl_divergence_target = 0.01
 
 # Whether to adjust the refusal directions so that only the component that is
 # orthogonal to the good direction is subtracted during abliteration.
-orthogonalize_direction = true
+orthogonalize_direction = false
 
 # How to apply row normalization of the weights. Options:
 # "none" (no normalization),
 # "pre" (compute LoRA adapter relative to row-normalized weights),
 # "full" (like "pre", but renormalizes to preserve original row magnitudes).
-row_normalization = "full"
+row_normalization = "none"
 
 # The rank of the LoRA adapter to use when "full" row normalization is used.
 # Row magnitude preservation is approximate due to non-linear effects,
@@ -130,9 +98,6 @@ n_startup_trials = 60
 # Directory to save and load study progress to/from.
 study_checkpoint_dir = "checkpoints"
 
-# Maximum size for individual safetensors files generated when exporting a model.
-max_shard_size = "5GB"
-
 # Strings whose presence in a response (case insensitive) identifies the response as a refusal.
 refusal_markers = [
     "sorry",
@@ -172,6 +137,12 @@ refusal_markers = [
 # System prompt to use when prompting the model.
 system_prompt = "You are a helpful assistant."
 
+# Move intermediate analysis tensors (such as residuals and logprobs)
+# to CPU memory as soon as possible to reduce peak VRAM usage.
+# This lowers peak VRAM usage during residual analysis and evaluation,
+# but may slightly reduce performance due to host/device transfers.
+offload_outputs_to_cpu = true
+
 # Dataset of prompts that tend to not result in refusals (used for calculating refusal directions).
 [good_prompts]
 dataset = "mlabonne/harmless_alpaca"

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "heretic-llm"
-version = "1.3.0"
+version = "1.2.0"
 description = "Fully automatic censorship removal for language models"
 readme = "README.md"
 license = "AGPL-3.0-or-later"
@@ -28,12 +28,12 @@ dependencies = [
     "hf-transfer~=0.1",
     "huggingface-hub~=1.7",
     "immutabledict~=4.3",
-    "kernels~=0.13",
+    "kernels~=0.12",
     "langdetect~=1.0",
     "lm-eval[hf]~=0.4",
     "numpy~=2.2",
     "optuna~=4.7",
-    "peft~=0.19",
+    "peft~=0.18",
     "psutil~=7.2",
     "py-cpuinfo~=9.0",
     "pydantic-settings~=2.13",
@@ -41,7 +41,7 @@ dependencies = [
     "rich~=14.3",
     "tomli-w~=1.2",
     "tqdm~=4.67",
-    "transformers~=5.6",
+    "transformers~=5.3",
 ]
 
 [project.optional-dependencies]

src/heretic/config.py

@@ -141,16 +141,6 @@ class Settings(BaseSettings):
         description='Maximum memory to allocate per device (e.g., { "0" = "20GB", "cpu" = "64GB" }).',
     )
 
-    offload_outputs_to_cpu: bool = Field(
-        default=True,
-        description=(
-            "Whether to move intermediate analysis tensors (such as residuals and logprobs) "
-            "to CPU memory as soon as possible to reduce peak VRAM usage. "
-            "This lowers peak VRAM usage during residual analysis and evaluation, "
-            "but may slightly reduce performance due to host/device transfers."
-        ),
-    )
-
     trust_remote_code: bool | None = Field(
         default=None,
         description="Whether to trust remote code when loading the model.",
@@ -271,7 +261,7 @@ class Settings(BaseSettings):
     )
 
     orthogonalize_direction: bool = Field(
-        default=True,
+        default=False,
         description=(
             "Whether to adjust the refusal directions so that only the component that is "
             "orthogonal to the good direction is subtracted during abliteration."
@@ -279,7 +269,7 @@ class Settings(BaseSettings):
     )
 
     row_normalization: RowNormalization = Field(
-        default=RowNormalization.FULL,
+        default=RowNormalization.NONE,
         description=(
             "How to apply row normalization of the weights. Options: "
             '"none" (no normalization), '
@@ -443,6 +433,14 @@ class Settings(BaseSettings):
         description="System prompt to use when prompting the model.",
     )
 
+    offload_outputs_to_cpu: bool = Field(
+        default=True,
+        description=(
+            "Whether to move intermediate analysis tensors (such as residuals and logprobs) "
+            "to CPU memory as soon as possible to reduce peak VRAM usage."
+        ),
+    )
+
     good_prompts: DatasetSpecification = Field(
         default=DatasetSpecification(
             dataset="mlabonne/harmless_alpaca",

src/heretic/main.py

@@ -17,15 +17,11 @@ def _is_help_invocation() -> bool:
 if _is_help_invocation():
     Settings()  # ty:ignore[missing-argument]
 
-# FIXME: Rich progress bars are currently disabled because of rendering issues
-#        when used from multiple threads in parallel (e.g. by huggingface_hub).
-"""
 from .progress import patch_tqdm
 
 # This patches tqdm class definitions, which must happen
 # before any other module imports tqdm.
 patch_tqdm()
-"""
 
 import logging
 import math
@@ -429,6 +425,9 @@ def run():
 
     needs_full_residuals = settings.print_residual_geometry or settings.plot_residuals
 
+    good_residuals = None
+    bad_residuals = None
+
     if needs_full_residuals:
         print("* Obtaining residuals for good prompts...")
         good_residuals = model.get_residuals_batched(good_prompts)
@@ -466,12 +465,8 @@ def run():
             refusal_directions - projection_vector.unsqueeze(1) * good_directions
         )
         refusal_directions = F.normalize(refusal_directions, p=2, dim=1)
-        del good_directions, projection_vector
-
-    del good_means, bad_means
 
     # Clear cache before starting the optimization study.
-    # This should free up memory from the objects released with the del statements above.
     empty_cache()
 
     trial_index = 0
@@ -688,9 +683,8 @@ def run():
             (
                 "The following trials resulted in Pareto optimal combinations of refusals and KL divergence. "
                 "After selecting a trial, you will be able to save the model, upload it to Hugging Face, "
-                "chat with it to test how well it works, or run standard benchmarks on it. "
-                "You can return to this menu later to select a different trial. "
-                "[yellow]Note that KL divergence values above 0.5 usually indicate significant damage to the original model's capabilities.[/]"
+                "or chat with it to test how well it works. You can return to this menu later to select a different trial. "
+                "[yellow]Note that KL divergence values above 1 usually indicate significant damage to the original model's capabilities.[/]"
             )
         )
 

src/heretic/model.py

@@ -154,15 +154,13 @@ class Model:
         # so we don't need to do anything manually.
 
         print(f"* Transformer model with [bold]{len(self.get_layers())}[/] layers")
-
+        print("* Abliterable components:")
         all_components = {}
         for layer_index in range(len(self.get_layers())):
             for component, modules in self.get_layer_modules(layer_index).items():
                 if component not in all_components:
                     all_components[component] = 0
                 all_components[component] += len(modules)
-
-        print("* Abliterable components:")
         for component, count in all_components.items():
             print(f"  * [bold]{component}[/]: [bold]{count}[/] modules total")
 
@@ -370,8 +368,8 @@ class Model:
         with suppress(Exception):
             try_add("attn.o_proj", layer.self_attn.o_proj)  # ty:ignore[possibly-missing-attribute]
 
-        # Qwen3.5 MoE hybrid layers use GatedDeltaNet (linear attention) instead of
-        # standard self-attention, so self_attn.o_proj doesn't exist on those layers.
+        # Qwen3.5 MoE hybrid layers use GatedDeltaNet (linear attention) instead
+        # of standard self-attention, so self_attn.o_proj doesn't exist on those layers.
         with suppress(Exception):
             try_add("attn.o_proj", layer.linear_attn.out_proj)  # ty:ignore[possibly-missing-attribute]
 
@@ -405,13 +403,11 @@ class Model:
         return modules
 
     def get_abliterable_components(self) -> list[str]:
-        components: set[str] = set()
-
         # Scan all layers because hybrid models (e.g. Qwen3.5 MoE) have different
         # components on different layers (some have self_attn, others linear_attn).
+        components: set[str] = set()
         for layer_index in range(len(self.get_layers())):
             components.update(self.get_layer_modules(layer_index).keys())
-
         return sorted(components)
 
     def abliterate(
@@ -748,8 +744,9 @@ class Model:
         # The returned tensor has shape (prompt, token).
         logprobs = F.log_softmax(logits, dim=-1)
 
+        del outputs
+
         if self.settings.offload_outputs_to_cpu:
-            del outputs, logits
             logprobs = logprobs.cpu()
             empty_cache()
 

src/heretic/utils.py

@@ -9,7 +9,6 @@ import random
 import tempfile
 from dataclasses import dataclass
 from datetime import datetime, timezone
-from importlib.metadata import version
 from pathlib import Path
 from typing import Any, TypeVar
 
@@ -284,6 +283,8 @@ def get_readme_intro(
         # Hide the path, which may contain private information.
         model_link = "a model"
 
+    version_info = get_heretic_version_info()
+
     if contains_reproducibility_information:
         reproducibility_instructions = """
 > [!TIP]
@@ -296,7 +297,7 @@ def get_readme_intro(
 
     return f"""# This is a decensored version of {
         model_link
-    }, made using [Heretic](https://github.com/p-e-w/heretic) v{version("heretic-llm")}
+    }, made using [Heretic](https://github.com/p-e-w/heretic) v{version_info.version}
 {reproducibility_instructions}
 ## Abliteration parameters
 

uv.lock

@@ -8,7 +8,7 @@ resolution-markers = [
 ]
 
 [options]
-exclude-newer = "2026-04-28T12:47:55.130721483Z"
+exclude-newer = "2026-04-14T22:48:57.86057843Z"
 exclude-newer-span = "P7D"
 
 [[package]]
@@ -931,7 +931,7 @@ wheels = [
 
 [[package]]
 name = "heretic-llm"
-version = "1.3.0"
+version = "1.2.0"
 source = { editable = "." }
 dependencies = [
     { name = "accelerate" },
@@ -983,14 +983,14 @@ requires-dist = [
     { name = "huggingface-hub", specifier = "~=1.7" },
     { name = "imageio", marker = "extra == 'research'", specifier = "~=2.37" },
     { name = "immutabledict", specifier = "~=4.3" },
-    { name = "kernels", specifier = "~=0.13" },
+    { name = "kernels", specifier = "~=0.12" },
     { name = "langdetect", specifier = "~=1.0" },
     { name = "lm-eval", extras = ["hf"], specifier = "~=0.4" },
     { name = "matplotlib", marker = "extra == 'research'", specifier = "~=3.10" },
     { name = "numpy", specifier = "~=2.2" },
     { name = "optuna", specifier = "~=4.7" },
     { name = "pacmap", marker = "extra == 'research'", specifier = "~=0.8" },
-    { name = "peft", specifier = "~=0.19" },
+    { name = "peft", specifier = "~=0.18" },
     { name = "psutil", specifier = "~=7.2" },
     { name = "py-cpuinfo", specifier = "~=9.0" },
     { name = "pydantic-settings", specifier = "~=2.13" },
@@ -999,7 +999,7 @@ requires-dist = [
     { name = "scikit-learn", marker = "extra == 'research'", specifier = "~=1.7" },
     { name = "tomli-w", specifier = "~=1.2" },
     { name = "tqdm", specifier = "~=4.67" },
-    { name = "transformers", specifier = "~=5.6" },
+    { name = "transformers", specifier = "~=5.3" },
 ]
 provides-extras = ["research"]
 
@@ -1188,18 +1188,17 @@ wheels = [
 
 [[package]]
 name = "kernels"
-version = "0.13.0"
+version = "0.12.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
     { name = "packaging" },
     { name = "pyyaml" },
     { name = "tomli", marker = "python_full_version < '3.11'" },
-    { name = "tomlkit" },
 ]
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 [[package]]
@@ -1509,14 +1508,14 @@ wheels = [
 
 [[package]]
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-version = "1.3.11"
+version = "1.3.10"
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@@ -2366,7 +2365,7 @@ wheels = [
 
 [[package]]
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-version = "0.19.1"
+version = "0.18.0"
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@@ -2381,9 +2380,9 @@ dependencies = [
     { name = "tqdm" },
     { name = "transformers" },
 ]
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