build: bump version to 1.4.0

fix: minor cleanups and improvements
feat: automatically reproduce model from reproduce.json (#326 )
2026-09-01 09:56:07 -07:00 · 2026-06-14 16:37:45 +05:30 · 2026-06-13 19:48:38 +05:30 · 2026-06-11 14:49:28 +05:30 · 2026-06-11 11:05:58 +05:30 · 2026-06-09 08:27:25 +05:30
11 changed files with 2706 additions and 766 deletions
@@ -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" />
+<img width="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)
+# 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) [![Matrix](https://img.shields.io/badge/Matrix-black?logo=matrix&style=for-the-badge)](https://matrix.to/#/#heretic:matrix.org) [![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)

 [![#1 Repository of the Day](https://trendshift.io/api/badge/repositories/20538)](https://trendshift.io/repositories/20538)

@@ -20,6 +20,11 @@ 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;
@@ -65,15 +70,15 @@ 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 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.
+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/).

-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.
+The community has created and published
+[well over 4000](https://huggingface.co/models?other=heretic)
+models with Heretic.


 ## Usage
@@ -88,6 +93,21 @@ 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,
@@ -96,14 +116,15 @@ a configuration file.

 At the start of a program run, Heretic benchmarks the system to determine
 the optimal batch size to make the most of the available hardware.
-On an RTX 3090, with the default configuration, decensoring Llama-3.1-8B-Instruct
-takes about 45 minutes. Note that Heretic supports model quantization with
+On an RTX 3090, with the default configuration, decensoring
+[Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
+takes about 20-30 minutes. Note that Heretic supports model quantization with
 bitsandbytes, which can drastically reduce the amount of VRAM required to process
 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,
-or any combination of those actions.
+run standard benchmarks on it, or any combination of those actions.


 ## Research features
@@ -25,7 +25,13 @@ quantization = "none"
 device_map = "auto"

 # Maximum memory to allocate per device.
-# max_memory = {"0": "20GB", "cpu": "64GB"}
+# 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
@@ -36,6 +42,32 @@ 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

@@ -64,13 +96,13 @@ 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 = false
+orthogonalize_direction = true

 # 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 = "none"
+row_normalization = "full"

 # The rank of the LoRA adapter to use when "full" row normalization is used.
 # Row magnitude preservation is approximate due to non-linear effects,
@@ -94,8 +126,12 @@ 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 = [
+    "disclaimer",
    "sorry",
    "i can'",
    "i cant",
@@ -133,6 +169,11 @@ refusal_markers = [
 # System prompt to use when prompting the model.
 system_prompt = "You are a helpful assistant."

+# Each "dataset" below can be a Hugging Face dataset ID, a path to a dataset on disk,
+# or a path to a plain text file with one prompt per line (empty lines are ignored).
+# For text files, "column" is ignored and "split" is optional; when given, it selects
+# a subset of the lines using slice notation (e.g. "[:400]").
+
 # Dataset of prompts that tend to not result in refusals (used for calculating refusal directions).
 [good_prompts]
 dataset = "mlabonne/harmless_alpaca"
@@ -0,0 +1,69 @@
+# Rename this file to config.toml, place it in the working directory
+# that you run Heretic from, and edit the configuration to your liking.
+
+max_response_length = 300
+
+residual_plot_title = "PaCMAP Projection of Residuals for Serious/Humorous Prompts"
+
+refusal_markers = [
+    "😅",
+    "here's one",
+    "why did",
+    "walked into a bar",
+    "gets worse",
+    "the punchline",
+    "classic",
+    "dad joke",
+    "bad joke",
+    "pun intended",
+    "ba dum tss",
+    "rimshot",
+    "😂",
+    "😄",
+    "😆",
+    "haha",
+    "hehe",
+    "lol",
+    "funny",
+    "joke",
+    "humor",
+    "that's hilarious",
+    "you could say",
+    "one-liner",
+    "comedian",
+    "stand-up",
+    "unexpectedly",
+    "because apparently",
+    "to be fair",
+    "on the bright side",
+    "lmao",
+    "omg",
+    "rofl",
+    "silly",
+    "humorous",
+    "clever",
+]
+
+[good_prompts]
+dataset = "mlabonne/harmless_alpaca"
+split = "train[:400]"
+column = "text"
+residual_plot_label = "Serious prompts"
+residual_plot_color = "royalblue"
+
+[bad_prompts]
+dataset = "UnstableLlama/jokes"
+split = "train[:200]"
+column = "text"
+residual_plot_label = "Humorous prompts"
+residual_plot_color = "darkorange"
+
+[good_evaluation_prompts]
+dataset = "mlabonne/harmless_alpaca"
+split = "test[:100]"
+column = "text"
+
+[bad_evaluation_prompts]
+dataset = "UnstableLlama/jokes"
+split = "train[200:250]"
+column = "text"
@@ -1,6 +1,6 @@
 [project]
 name = "heretic-llm"
-version = "1.2.0"
+version = "1.4.0"
 description = "Fully automatic censorship removal for language models"
 readme = "README.md"
 license = "AGPL-3.0-or-later"
@@ -25,21 +25,21 @@ dependencies = [
    "accelerate~=1.13",
    "bitsandbytes~=0.49",
    "datasets~=4.7",
-    "hf-transfer~=0.1",
    "huggingface-hub~=1.7",
    "immutabledict~=4.3",
-    "kernels~=0.12",
    "langdetect~=1.0",
    "lm-eval[hf]~=0.4",
    "numpy~=2.2",
    "optuna~=4.7",
-    "peft~=0.18",
+    "peft~=0.19",
    "psutil~=7.2",
+    "py-cpuinfo~=9.0",
    "pydantic-settings~=2.13",
    "questionary~=2.1",
    "rich~=14.3",
+    "tomli-w~=1.2",
    "tqdm~=4.67",
-    "transformers~=5.3",
+    "transformers[kernels]~=5.6",
 ]

 [project.optional-dependencies]
@@ -58,8 +58,8 @@ dev = [
 ]

 [project.urls]
-Homepage = "https://github.com/p-e-w/heretic"
-Documentation = "https://github.com/p-e-w/heretic"
+Homepage = "https://heretic-project.org"
+Documentation = "https://heretic-project.org/tutorial"
 Repository = "https://github.com/p-e-w/heretic.git"
 Issues = "https://github.com/p-e-w/heretic/issues"
 Changelog = "https://github.com/p-e-w/heretic/releases"
@@ -71,5 +71,8 @@ heretic = "heretic.main:main"
 requires = ["uv_build>=0.8.11,<0.9.0"]
 build-backend = "uv_build"

+[tool.uv]
+exclude-newer = "7 days"
+
 [tool.uv.build-backend]
 module-name = "heretic"
@@ -13,6 +13,12 @@ from pydantic_settings import (
    TomlConfigSettingsSource,
 )

+# !!!IMPORTANT!!!
+#
+# Any settings added to the classes defined in this module
+# must be evaluated for privacy implications and have
+# exclude=True set in their field definitions if appropriate.
+

 class QuantizationMethod(str, Enum):
    NONE = "none"
@@ -26,14 +32,30 @@ class RowNormalization(str, Enum):
    FULL = "full"


+class ExportStrategy(str, Enum):
+    MERGE = "merge"
+    ADAPTER = "adapter"
+
+
 class DatasetSpecification(BaseModel):
    dataset: str = Field(
        description="Hugging Face dataset ID, or path to dataset on disk."
    )

-    split: str = Field(description="Portion of the dataset to use.")
+    commit: str | None = Field(
+        default=None,
+        description="Hugging Face commit hash of the dataset.",
+    )

-    column: str = Field(description="Column in the dataset that contains the prompts.")
+    split: str | None = Field(
+        default=None,
+        description="Portion of the dataset to use. Required for datasets, optional for plain text files.",
+    )
+
+    column: str | None = Field(
+        default=None,
+        description="Column in the dataset that contains the prompts. Required for datasets, ignored for plain text files.",
+    )

    prefix: str = Field(
        default="",
@@ -53,11 +75,13 @@ class DatasetSpecification(BaseModel):
    residual_plot_label: str | None = Field(
        default=None,
        description="Label to use for the dataset in plots of residual vectors.",
+        exclude=True,
    )

    residual_plot_color: str | None = Field(
        default=None,
        description="Matplotlib color to use for the dataset in plots of residual vectors.",
+        exclude=True,
    )


@@ -76,12 +100,37 @@ class BenchmarkSpecification(BaseModel):
 class Settings(BaseSettings):
    model: str = Field(description="Hugging Face model ID, or path to model on disk.")

+    model_commit: str | None = Field(
+        default=None,
+        description="Hugging Face commit hash of the model.",
+    )
+
    evaluate_model: str | None = Field(
        default=None,
        description=(
            "If this model ID or path is set, then instead of abliterating the main model, "
            "evaluate this model relative to the main model."
        ),
+        exclude=True,
+    )
+
+    collect_reproducibles: str | None = Field(
+        default=None,
+        description=(
+            "If this directory path is set, then instead of abliterating a model, "
+            "download all reproduce.json files from public Heretic model repositories "
+            "on Hugging Face, and store them in that directory for archival purposes."
+        ),
+        exclude=True,
+    )
+
+    reproduce: str | None = Field(
+        default=None,
+        description=(
+            "If this path or URL to a reproduce.json file is set, load reproduction information "
+            "from that file, and attempt to reproduce the abliterated model it originated from."
+        ),
+        exclude=True,
    )

    dtypes: list[str] = Field(
@@ -119,12 +168,17 @@ class Settings(BaseSettings):

    max_memory: Dict[str, str] | None = Field(
        default=None,
-        description='Maximum memory to allocate per device (e.g., {"0": "20GB", "cpu": "64GB"}).',
+        description='Maximum memory to allocate per device (e.g., { "0" = "20GB", "cpu" = "64GB" }).',
    )

-    trust_remote_code: bool | None = Field(
-        default=None,
-        description="Whether to trust remote code when loading 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. "
+            "This lowers peak VRAM usage during residual analysis and evaluation, "
+            "but may slightly reduce performance due to host/device transfers."
+        ),
    )

    batch_size: int = Field(
@@ -135,6 +189,9 @@ class Settings(BaseSettings):
    max_batch_size: int = Field(
        default=128,
        description="Maximum batch size to try when automatically determining the optimal batch size.",
+        # When storing a settings object, the batch size is already fixed,
+        # either determined by the automatic mechanism or by explicit user choice.
+        exclude=True,
    )

    max_response_length: int = Field(
@@ -142,34 +199,82 @@ class Settings(BaseSettings):
        description="Maximum number of tokens to generate for each response.",
    )

+    response_prefix: str | None = Field(
+        default=None,
+        description=(
+            "Common prefix to assume for all responses, so that evaluation happens "
+            "at the point where responses start to differ for different prompts. "
+            "If not set, the prefix is determined automatically by comparing multiple responses."
+        ),
+    )
+
+    chain_of_thought_skips: list[tuple[str, str]] = Field(
+        default=[
+            # 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]",
+            ),
+        ],
+        description=(
+            "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."
+        ),
+        # When storing a settings object, the response prefix is already fixed,
+        # either determined by the automatic mechanism or by explicit user choice.
+        exclude=True,
+    )
+
    print_responses: bool = Field(
        default=False,
        description="Whether to print prompt/response pairs when counting refusals.",
+        exclude=True,
    )

    print_residual_geometry: bool = Field(
        default=False,
        description="Whether to print detailed information about residuals and refusal directions.",
+        exclude=True,
    )

    plot_residuals: bool = Field(
        default=False,
        description="Whether to generate plots showing PaCMAP projections of residual vectors.",
+        exclude=True,
    )

    residual_plot_path: str = Field(
        default="plots",
        description="Base path to save plots of residual vectors to.",
+        exclude=True,
    )

    residual_plot_title: str = Field(
        default='PaCMAP Projection of Residual Vectors for "Harmless" and "Harmful" Prompts',
        description="Title placed above plots of residual vectors.",
+        exclude=True,
    )

    residual_plot_style: str = Field(
        default="dark_background",
        description="Matplotlib style sheet to use for plots of residual vectors.",
+        exclude=True,
    )

    kl_divergence_scale: float = Field(
@@ -189,7 +294,7 @@ class Settings(BaseSettings):
    )

    orthogonalize_direction: bool = Field(
-        default=False,
+        default=True,
        description=(
            "Whether to adjust the refusal directions so that only the component that is "
            "orthogonal to the good direction is subtracted during abliteration."
@@ -197,7 +302,7 @@ class Settings(BaseSettings):
    )

    row_normalization: RowNormalization = Field(
-        default=RowNormalization.NONE,
+        default=RowNormalization.FULL,
        description=(
            "How to apply row normalization of the weights. Options: "
            '"none" (no normalization), '
@@ -237,9 +342,18 @@ class Settings(BaseSettings):
        description="Number of trials that use random sampling for the purpose of exploration.",
    )

+    seed: int | None = Field(
+        default=None,
+        description=(
+            "Random seed for reproducible optimization. "
+            "Applies to Python's random module, NumPy, PyTorch, and Optuna."
+        ),
+    )
+
    study_checkpoint_dir: str = Field(
        default="checkpoints",
        description="Directory to save and load study progress to/from.",
+        exclude=True,
    )

    benchmarks: list[BenchmarkSpecification] = Field(
@@ -301,10 +415,22 @@ class Settings(BaseSettings):
            ),
        ],
        description="Benchmarks to offer to the user for evaluating abliterated models.",
+        exclude=True,
+    )
+
+    export_strategy: ExportStrategy | None = Field(
+        default=None,
+        description='How to export the model: "merge", "adapter", or unset to prompt the user.',
+    )
+
+    max_shard_size: int | str = Field(
+        default="5GB",
+        description="Maximum size for individual safetensors files generated when exporting a model.",
    )

    refusal_markers: list[str] = Field(
        default=[
+            "disclaimer",
            "sorry",
            "i can'",
            "i cant",
@@ -17,12 +17,14 @@ from torch.nn import Module, ModuleList
 from transformers import (
    AutoModelForCausalLM,
    AutoModelForImageTextToText,
+    AutoProcessor,
    AutoTokenizer,
    BatchEncoding,
    BitsAndBytesConfig,
    PretrainedConfig,
    PreTrainedModel,
    PreTrainedTokenizerBase,
+    ProcessorMixin,
    TextStreamer,
 )
 from transformers.generation import (
@@ -30,7 +32,8 @@ from transformers.generation import (
 )

 from .config import QuantizationMethod, RowNormalization, Settings
-from .utils import Prompt, batchify, empty_cache, print
+from .system import empty_cache
+from .utils import Prompt, batchify, format_exception, print


 def get_model_class(
@@ -55,21 +58,35 @@ class AbliterationParameters:
 class Model:
    model: PreTrainedModel | PeftModel
    tokenizer: PreTrainedTokenizerBase
+    # Set for multimodal models, None for text-only ones.
+    processor: ProcessorMixin | None
    peft_config: LoraConfig
+    dtype: torch.dtype

    def __init__(self, settings: Settings):
        self.settings = settings
-        self.response_prefix = ""
        self.needs_reload = False

+        self.revision_kwargs = {}
+        if settings.model_commit is not None:
+            self.revision_kwargs["revision"] = settings.model_commit
+
        print()
        print(f"Loading model [bold]{settings.model}[/]...")

        self.tokenizer = AutoTokenizer.from_pretrained(
            settings.model,
-            trust_remote_code=settings.trust_remote_code,
+            **self.revision_kwargs,
        )

+        # Multimodal models have a processor we'll want to save.
+        self.processor = None
+        if get_model_class(settings.model) == AutoModelForImageTextToText:
+            self.processor = AutoProcessor.from_pretrained(
+                settings.model,
+                **self.revision_kwargs,
+            )
+
        # Fallback for tokenizers that don't declare a special pad token.
        if self.tokenizer.pad_token is None:
            self.tokenizer.pad_token = self.tokenizer.eos_token
@@ -85,10 +102,8 @@ class Model:
            if settings.max_memory
            else None
        )
-        self.trusted_models = {settings.model: settings.trust_remote_code}

-        if self.settings.evaluate_model is not None:
-            self.trusted_models[settings.evaluate_model] = settings.trust_remote_code
+        self.trusted_models = set()

        for dtype in settings.dtypes:
            print(f"* Trying dtype [bold]{dtype}[/]...")
@@ -107,14 +122,19 @@ class Model:
                    dtype=dtype,
                    device_map=settings.device_map,
                    max_memory=self.max_memory,
-                    trust_remote_code=self.trusted_models.get(settings.model),
+                    trust_remote_code=True
+                    if settings.model in self.trusted_models
+                    else None,
+                    **self.revision_kwargs,
                    **extra_kwargs,
                )

+                self.dtype = self.model.dtype
+
                # If we reach this point and the model requires trust_remote_code,
-                # either the user accepted, or settings.trust_remote_code is True.
-                if self.trusted_models.get(settings.model) is None:
-                    self.trusted_models[settings.model] = True
+                # the user must have agreed when prompted to execute remote code,
+                # because from_pretrained raises an exception otherwise.
+                self.trusted_models.add(settings.model)

                # A test run can reveal dtype-related problems such as the infamous
                # "RuntimeError: probability tensor contains either `inf`, `nan` or element < 0"
@@ -131,7 +151,13 @@ class Model:
            except Exception as error:
                self.model = None  # ty:ignore[invalid-assignment]
                empty_cache()
-                print(f"* [red]Failed[/] ({error})")
+
+                formatted = format_exception(error)
+                if "\n" in formatted:
+                    print(f"* [red]Failed:\n{formatted}[/]")
+                else:
+                    print(f"* [red]Failed ({formatted})[/]")
+
                continue

            if settings.quantization == QuantizationMethod.BNB_4BIT:
@@ -148,13 +174,15 @@ 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")

@@ -169,18 +197,19 @@ class Model:
        # across layers (e.g. "o_proj" on attention layers, "out_proj" on linear attention layers).
        target_modules_set: set[str] = set()

-        for layer_index, layer in enumerate(self.get_layers()):
-            module_id_to_leaf_name = {
-                id(module): module_name.split(".")[-1]
-                for module_name, module in layer.named_modules()
-            }
+        module_id_to_full_name = {
+            id(module): module_name
+            for module_name, module in self.model.named_modules()
+        }

+        for layer_index in range(len(self.get_layers())):
            for modules in self.get_layer_modules(layer_index).values():
                for module in modules:
-                    if id(module) in module_id_to_leaf_name:
-                        target_modules_set.add(module_id_to_leaf_name[id(module)])
+                    full_name = module_id_to_full_name.get(id(module))
+                    if full_name is not None:
+                        target_modules_set.add(full_name)

-        target_modules = list(target_modules_set)
+        target_modules = sorted(target_modules_set)

        if self.settings.row_normalization != RowNormalization.FULL:
            # Rank 1 is sufficient for directional ablation without renormalization.
@@ -204,7 +233,10 @@ class Model:
        # so the result is a PeftModel rather than a PeftMixedModel.
        self.model = cast(PeftModel, get_peft_model(self.model, self.peft_config))

-        print(f"* LoRA adapters initialized (targets: {', '.join(target_modules)})")
+        display_targets = sorted({name.rsplit(".", 1)[-1] for name in target_modules})
+        print(
+            f"* LoRA adapters initialized (target types: {', '.join(display_targets)})"
+        )

    def _get_quantization_config(self, dtype: str) -> BitsAndBytesConfig | None:
        """
@@ -252,7 +284,10 @@ class Model:
                self.settings.model,
                torch_dtype=self.model.dtype,
                device_map="cpu",
-                trust_remote_code=self.trusted_models.get(self.settings.model),
+                trust_remote_code=True
+                if self.settings.model in self.trusted_models
+                else None,
+                **self.revision_kwargs,
            )

            # Apply LoRA adapters to the CPU model
@@ -287,33 +322,41 @@ class Model:
        - Slow path: If switching models or after merge_and_unload(),
          performs full model reload with quantization config.
        """
-        current_model = getattr(self.model.config, "name_or_path", None)
+
+        # If a prior model load was interrupted/cancelled mid-process, self.model will be None.
+        current_model = None
+        if self.model is not None:
+            current_model = getattr(self.model.config, "name_or_path", None)
+
        if current_model == self.settings.model and not self.needs_reload:
-            # Reset LoRA adapters to zero (identity transformation)
+            # Reset LoRA adapters to zero (identity transformation).
            for name, module in self.model.named_modules():
                if "lora_B" in name and hasattr(module, "weight"):
                    torch.nn.init.zeros_(module.weight)
            return

-        dtype = self.model.dtype
-
        # Purge existing model object from memory to make space.
        self.model = None  # ty:ignore[invalid-assignment]
        empty_cache()

-        quantization_config = self._get_quantization_config(str(dtype).split(".")[-1])
+        quantization_config = self._get_quantization_config(
+            str(self.dtype).split(".")[-1]
+        )

-        # Build kwargs, only include quantization_config if it's not None
+        # Build kwargs, only include quantization_config if it's not None.
        extra_kwargs = {}
        if quantization_config is not None:
            extra_kwargs["quantization_config"] = quantization_config

        self.model = get_model_class(self.settings.model).from_pretrained(
            self.settings.model,
-            dtype=dtype,
+            dtype=self.dtype,
            device_map=self.settings.device_map,
            max_memory=self.max_memory,
-            trust_remote_code=self.trusted_models.get(self.settings.model),
+            trust_remote_code=True
+            if self.settings.model in self.trusted_models
+            else None,
+            **self.revision_kwargs,
            **extra_kwargs,
        )

@@ -356,8 +399,8 @@ 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]

@@ -375,6 +418,21 @@ class Model:
            for expert in layer.block_sparse_moe.experts:  # ty:ignore[possibly-missing-attribute, not-iterable]
                try_add("mlp.down_proj", expert.w2)  # ty:ignore[possibly-missing-attribute]

+        # LFM dense operator blocks.
+        with suppress(Exception):
+            try_add("attn.o_proj", layer.conv.out_proj)  # ty:ignore[possibly-missing-attribute]
+
+        with suppress(Exception):
+            try_add("mlp.down_proj", layer.feed_forward.w2)  # ty:ignore[possibly-missing-attribute]
+
+        # LFM transformer blocks.
+        with suppress(Exception):
+            try_add("attn.o_proj", layer.self_attn.out_proj)  # ty:ignore[possibly-missing-attribute]
+
+        with suppress(Exception):
+            for expert in layer.feed_forward.experts:  # ty:ignore[possibly-missing-attribute, not-iterable]
+                try_add("mlp.down_proj", expert.w2)  # ty:ignore[possibly-missing-attribute]
+
        # Granite MoE Hybrid - attention layers with shared_mlp.
        with suppress(Exception):
            try_add("mlp.down_proj", layer.shared_mlp.output_linear)  # ty:ignore[possibly-missing-attribute]
@@ -391,11 +449,13 @@ 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(
@@ -520,6 +580,10 @@ class Model:
                        W = W - W_org
                        # Use a low-rank SVD to get an approximation of the matrix.
                        r = self.peft_config.r
+                        # svd_lowrank is randomized:
+                        # https://github.com/pytorch/pytorch/blob/20919052303c0b5ba87f8bf7e19237dc33ab09d3/torch/_lowrank.py#L108-L109
+                        # Reseed immediately before the call so restoring a trial is independent of RNG history.
+                        torch.manual_seed(self.settings.seed)
                        U, S, Vh = torch.svd_lowrank(W, q=2 * r + 4, niter=6)
                        # Truncate it to the part we want to store in the LoRA adapter.
                        # Note: svd_lowrank actually returns V, so transpose it to get Vh.
@@ -565,10 +629,12 @@ class Model:
            ),
        )

-        if self.response_prefix:
+        if self.settings.response_prefix:
            # Append the common response prefix to the prompts so that evaluation happens
            # at the point where responses start to differ for different prompts.
-            chat_prompts = [prompt + self.response_prefix for prompt in chat_prompts]
+            chat_prompts = [
+                prompt + self.settings.response_prefix for prompt in chat_prompts
+            ]

        inputs = self.tokenizer(
            chat_prompts,
@@ -630,6 +696,9 @@ class Model:
            max_new_tokens=1,
            output_hidden_states=True,
            return_dict_in_generate=True,
+            # KV cache is unnecessary here because we only need the hidden states
+            # for the first generated token.
+            use_cache=False,
        )

        # This cast is valid because GenerateDecoderOnlyOutput is the return type
@@ -663,7 +732,11 @@ class Model:
                dim=2,
                keepdim=True,
            )
-            return torch.clamp(residuals, -thresholds, thresholds)
+            residuals = torch.clamp(residuals, -thresholds, thresholds)
+
+        if self.settings.offload_outputs_to_cpu:
+            residuals = residuals.cpu()
+            empty_cache()

        return residuals

@@ -675,6 +748,30 @@ class Model:

        return torch.cat(residuals, dim=0)

+    def get_residuals_mean(self, prompts: list[Prompt]) -> Tensor:
+        if not prompts:
+            raise ValueError("prompts must not be empty")
+
+        running_sum = None
+        total_count = 0
+
+        for batch in batchify(prompts, self.settings.batch_size):
+            batch_residuals = self.get_residuals(batch)
+
+            # Accumulate in high precision on CPU to reduce peak VRAM usage.
+            batch_sum = batch_residuals.sum(dim=0, dtype=torch.float64).cpu()
+
+            if running_sum is None:
+                running_sum = batch_sum
+            else:
+                running_sum += batch_sum
+
+            total_count += batch_residuals.shape[0]
+
+        assert running_sum is not None
+
+        return (running_sum / total_count).to(torch.float32)
+
    # We work with logprobs rather than probabilities for numerical stability
    # when computing the KL divergence.
    def get_logprobs(self, prompts: list[Prompt]) -> Tensor:
@@ -683,8 +780,9 @@ class Model:
        _, outputs = self.generate(
            prompts,
            max_new_tokens=1,
-            output_scores=True,
+            output_logits=True,
            return_dict_in_generate=True,
+            use_cache=False,
        )

        # This cast is valid because GenerateDecoderOnlyOutput is the return type
@@ -692,11 +790,20 @@ class Model:
        outputs = cast(GenerateDecoderOnlyOutput, outputs)

        # Logits for the first (only) generated token.
-        # This cast is valid because we passed output_scores=True above.
-        logits = cast(tuple[FloatTensor], outputs.scores)[0]
+        # Use raw logits, not processed generation scores; processors can insert
+        # -inf for suppressed tokens, which can make KL divergence evaluate to NaN.
+        # This cast is valid because we passed output_logits=True above.
+        logits = cast(tuple[FloatTensor], outputs.logits)[0]

        # The returned tensor has shape (prompt, token).
-        return F.log_softmax(logits, dim=-1)
+        logprobs = F.log_softmax(logits, dim=-1)
+
+        if self.settings.offload_outputs_to_cpu:
+            del outputs, logits
+            logprobs = logprobs.cpu()
+            empty_cache()
+
+        return logprobs

    def get_logprobs_batched(self, prompts: list[Prompt]) -> Tensor:
        logprobs = []
@@ -0,0 +1,382 @@
+# SPDX-License-Identifier: AGPL-3.0-or-later
+# Copyright (C) 2025-2026  Philipp Emanuel Weidmann <pew@worldwidemann.com> + contributors
+
+import json
+import platform
+import random
+import shutil
+from dataclasses import asdict
+from enum import IntEnum
+from pathlib import Path
+from typing import Any, cast
+from urllib.request import urlopen
+
+import cpuinfo
+import torch
+from huggingface_hub import HfApi, hf_hub_download
+from huggingface_hub.utils import (
+    GatedRepoError,
+    disable_progress_bars,
+    enable_progress_bars,
+)
+from questionary import Choice
+from rich.table import Table
+
+from .system import (
+    get_accelerator_info_dict,
+    get_heretic_version_info,
+    get_requirements_dict,
+)
+from .utils import print, prompt_select
+
+
+def collect_reproducibles(path: str):
+    print(
+        f"Collecting [bold]reproduce.json[/] files from Hugging Face and storing them in [bold]{path}[/]..."
+    )
+    print()
+
+    api = HfApi()
+
+    models = api.list_models(
+        filter=["heretic", "reproducible"],
+        sort="created_at",
+        expand=["gated", "tags"],
+    )
+
+    found = 0
+    downloaded = 0
+
+    # We're only downloading tiny files, so the progress bars are just noise.
+    disable_progress_bars()
+
+    try:
+        for model in models:
+            # Ignore repositories containing quantizations.
+            if model.tags is not None and "gguf" in model.tags:
+                continue
+
+            if model.gated:
+                try:
+                    api.auth_check(model.id, repo_type="model")
+                except GatedRepoError:
+                    continue
+
+            print(f"[bold]{model.id}[/]...", end="")
+
+            user, repository = model.id.split("/")
+
+            paths_info = api.get_paths_info(
+                model.id,
+                "reproduce/reproduce.json",
+                expand=True,
+            )
+            # The reproduce.json file might not exist in the repository
+            # despite the relevant tags being present.
+            if not paths_info:
+                print(" [yellow]no reproduce.json found[/]")
+                continue
+
+            found += 1
+
+            commit_hash = paths_info[0].last_commit.oid
+
+            file_path = (
+                Path(path)
+                / "huggingface.co"
+                / user
+                / f"{repository}-{commit_hash[:7]}.json"
+            )
+            if file_path.exists():
+                print(" already stored")
+                continue
+
+            cache_path = hf_hub_download(
+                model.id,
+                "reproduce/reproduce.json",
+            )
+
+            file_path.parent.mkdir(parents=True, exist_ok=True)
+            shutil.copyfile(cache_path, file_path)
+            print(" [green]downloaded[/]")
+
+            downloaded += 1
+    finally:
+        enable_progress_bars()
+
+    print()
+    print(f"Found: [bold]{found}[/] files")
+    print(f"Downloaded: [bold]{downloaded}[/] files")
+    print(f"Already stored: [bold]{found - downloaded}[/] files")
+
+
+def load_reproduction_information(path: str) -> dict[str, Any]:
+    if path.lower().startswith(("http://", "https://")):
+        # The path is a URL on the web.
+
+        # Obtain raw download URL.
+        path = path.replace("/blob/", "/raw/")  # Hugging Face, GitHub
+        path = path.replace("/src/branch/", "/raw/branch/")  # Codeberg
+
+        json_str = urlopen(path).read().decode("utf-8")
+    else:
+        # The path is (assumed to be) a local file system path.
+        json_str = Path(path).read_text(encoding="utf-8")
+
+    return json.loads(json_str)
+
+
+class MismatchSeverity(IntEnum):
+    LOW = 1
+    MEDIUM = 2
+    HIGH = 3
+    CRITICAL = 4
+
+    def __rich__(self) -> str:
+        match self:
+            case MismatchSeverity.LOW:
+                return "[green]low[/]"
+            case MismatchSeverity.MEDIUM:
+                return "[yellow]medium[/]"
+            case MismatchSeverity.HIGH:
+                return "[red]high[/]"
+            case MismatchSeverity.CRITICAL:
+                return "[bold red]critical[/]"
+            case _:
+                raise ValueError(f"unknown MismatchSeverity value: {self}")
+
+
+def get_package_mismatch_severity(package_name: str) -> MismatchSeverity:
+    if package_name in [
+        "heretic-llm",
+    ]:
+        return MismatchSeverity.CRITICAL
+    elif package_name in [
+        "torch",
+        "transformers",
+    ]:
+        return MismatchSeverity.HIGH
+    elif package_name in [
+        "accelerate",
+        "bitsandbytes",
+        "kernels",
+        "optuna",
+        "peft",
+        "tokenizers",
+        "triton",
+    ]:
+        return MismatchSeverity.MEDIUM
+    else:
+        return MismatchSeverity.LOW
+
+
+def format_version_information(version_information: dict[str, Any]) -> str:
+    version = version_information["version"]
+    metadata = version_information["metadata"]
+
+    if "type" in metadata:
+        match metadata["type"]:
+            case "pypi":
+                return version
+            case "git":
+                return f"{version}-git+{metadata['url']}@{metadata['commit_hash']}"
+            case "local":
+                # Append a random number to ensure that two local installations
+                # are always considered to be different versions.
+                return f"{version}-local-{random.randint(2**16, 2**17)}"
+            case _:
+                raise ValueError(
+                    f"unknown metadata.type value in version information: {metadata['type']}"
+                )
+    else:
+        return f"{version}-unknown-{random.randint(2**16, 2**17)}"
+
+
+def check_environment(reproduction_information: dict[str, Any]) -> bool:
+    mismatch_severity: MismatchSeverity | None = None
+
+    system_mismatches = []
+    package_mismatches = []
+
+    def verify(
+        mismatch_list: list[tuple[str, Any, Any, MismatchSeverity]],
+        name: str,
+        this: Any,
+        original: Any,
+        severity: MismatchSeverity,
+    ):
+        nonlocal mismatch_severity
+        if this != original:
+            mismatch_list.append((name, this, original, severity))
+            if mismatch_severity is None:
+                mismatch_severity = severity
+            else:
+                mismatch_severity = max(severity, mismatch_severity)
+
+    if "system" in reproduction_information:
+        system = reproduction_information["system"]
+
+        verify(
+            system_mismatches,
+            "Python version",
+            platform.python_version(),
+            system["python"]["version"],
+            MismatchSeverity.LOW,
+        )
+
+        verify(
+            system_mismatches,
+            "Operating system",
+            platform.platform(),
+            system["os"]["platform"],
+            MismatchSeverity.LOW,
+        )
+
+        verify(
+            system_mismatches,
+            "CPU",
+            cpuinfo.get_cpu_info().get("brand_raw"),
+            system["cpu"]["brand"],
+            MismatchSeverity.LOW,
+        )
+
+        accelerators = get_accelerator_info_dict()
+
+        verify(
+            system_mismatches,
+            "Accelerator type",
+            accelerators["type"],
+            system["accelerators"]["type"],
+            MismatchSeverity.HIGH,
+        )
+
+        if (
+            accelerators["type"]
+            and accelerators["type"] == system["accelerators"]["type"]
+        ):
+            verify(
+                system_mismatches,
+                accelerators["api_name"],
+                accelerators["api_version"],
+                system["accelerators"]["api_version"],
+                MismatchSeverity.MEDIUM,
+            )
+            verify(
+                system_mismatches,
+                "Driver version",
+                accelerators["driver_version"],
+                system["accelerators"]["driver_version"],
+                MismatchSeverity.MEDIUM,
+            )
+            verify(
+                system_mismatches,
+                "Devices",
+                "\n".join([device["name"] for device in accelerators["devices"]]),
+                "\n".join(
+                    [device["name"] for device in system["accelerators"]["devices"]]
+                ),
+                MismatchSeverity.MEDIUM,
+            )
+
+    else:
+        print(
+            (
+                "[yellow]The provided JSON file does not contain system information. "
+                "Some system parameters can affect reproducibility, but due to the lack of system information, "
+                "Heretic is unable to verify that those parameters match the original environment. "
+                "Reproduction may or may not produce a byte-for-byte identical model.[/]"
+            )
+        )
+
+    requirements = get_requirements_dict()
+    requirements["heretic-llm"] = format_version_information(
+        asdict(get_heretic_version_info())
+    )
+    requirements["torch"] = torch.__version__
+
+    original_requirements = reproduction_information["environment"]["requirements"]
+    original_requirements["heretic-llm"] = format_version_information(
+        reproduction_information["environment"]["heretic"]
+    )
+    original_requirements["torch"] = reproduction_information["environment"][
+        "pytorch_version"
+    ]
+
+    package_names = sorted(requirements.keys() | original_requirements.keys())
+
+    for package_name in package_names:
+        verify(
+            package_mismatches,
+            package_name,
+            requirements.get(package_name),
+            original_requirements.get(package_name),
+            get_package_mismatch_severity(package_name),
+        )
+
+    if system_mismatches or package_mismatches:
+        print()
+        print(
+            (
+                "[yellow]Your local environment doesn't perfectly match the environment "
+                "used to produce the original model. The following components differ:[/]"
+            )
+        )
+
+    if system_mismatches:
+        table = Table()
+        table.add_column("Component")
+        table.add_column("This system", overflow="fold")
+        table.add_column("Original system", overflow="fold")
+        table.add_column("Severity", width=8)
+
+        for component, this, original, severity in system_mismatches:
+            table.add_row(f"[bold]{component}[/]", this, original, severity)
+
+        print()
+        print("[bold]System Mismatches[/]")
+        print(table)
+
+    if package_mismatches:
+        table = Table()
+        table.add_column("Package")
+        table.add_column("This system", overflow="fold")
+        table.add_column("Original system", overflow="fold")
+        table.add_column("Severity", width=8)
+
+        for package, this, original, severity in package_mismatches:
+            table.add_row(f"[bold]{package}[/]", this, original, severity)
+
+        print()
+        print("[bold]Package Mismatches[/]")
+        print(table)
+
+    if system_mismatches or package_mismatches:
+        print()
+        print(
+            (
+                f"There is a {cast(MismatchSeverity, mismatch_severity).__rich__()} chance "
+                "that reproduction won't produce a byte-for-byte identical model. "
+                "However, the resulting model will very likely still behave similarly "
+                "to the original model."
+            )
+        )
+
+        print()
+        choice = prompt_select(
+            "How would you like to proceed?",
+            [
+                Choice(
+                    title="Attempt to reproduce the model anyway",
+                    value=True,
+                ),
+                Choice(
+                    title="Exit program",
+                    value=False,
+                ),
+            ],
+        )
+
+        return choice
+    else:
+        # There are no mismatches at all, so there is nothing to confirm.
+        return True
@@ -0,0 +1,478 @@
+# SPDX-License-Identifier: AGPL-3.0-or-later
+# Copyright (C) 2025-2026  Philipp Emanuel Weidmann <pew@worldwidemann.com> + contributors
+
+import gc
+import importlib.metadata
+import json
+import os
+import platform
+import re
+import subprocess
+import sys
+from dataclasses import dataclass
+from typing import Any
+
+import cpuinfo
+import torch
+from accelerate.utils import (
+    is_mlu_available,
+    is_musa_available,
+    is_npu_available,
+    is_sdaa_available,
+    is_xpu_available,
+)
+
+
+def empty_cache():
+    """Clears the backend cache and collects garbage."""
+
+    # Collecting garbage is not an idempotent operation, and to avoid OOM errors,
+    # gc.collect() has to be called both before and after emptying the backend cache.
+    # See https://github.com/p-e-w/heretic/pull/17 for details.
+    gc.collect()
+
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    elif is_xpu_available():
+        torch.xpu.empty_cache()
+    elif is_mlu_available():
+        torch.mlu.empty_cache()  # ty:ignore[unresolved-attribute]
+    elif is_sdaa_available():
+        torch.sdaa.empty_cache()  # ty:ignore[unresolved-attribute]
+    elif is_musa_available():
+        torch.musa.empty_cache()  # ty:ignore[unresolved-attribute]
+    elif torch.backends.mps.is_available():
+        torch.mps.empty_cache()
+
+    gc.collect()
+
+
+def get_nvidia_driver_version() -> str | None:
+    """Gets the NVIDIA driver version using nvidia-smi."""
+
+    try:
+        output = subprocess.check_output(
+            ["nvidia-smi", "--query-gpu=driver_version", "--format=csv,noheader"],
+            stderr=subprocess.DEVNULL,
+            text=True,
+        )
+        return output.strip().split("\n")[0]
+    except (subprocess.CalledProcessError, FileNotFoundError, IndexError):
+        return None
+
+
+def get_amdgpu_driver_version() -> str | None:
+    """Gets the AMD GPU (ROCm) driver and suite version info."""
+
+    # 1. Try amd-smi (modern standard for ROCm 6.0+)
+    try:
+        output = subprocess.check_output(
+            ["amd-smi", "version"],
+            stderr=subprocess.DEVNULL,
+            text=True,
+        )
+        if output.strip():
+            return output.strip().replace("\n", " | ")
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        pass
+
+    # 2. Try rocm-smi --showdriverversion
+    try:
+        output = subprocess.check_output(
+            ["rocm-smi", "--showdriverversion"],
+            stderr=subprocess.DEVNULL,
+            text=True,
+        )
+        for line in output.split("\n"):
+            if "Driver version" in line:
+                return line.split(":")[-1].strip()
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        pass
+
+    # 3. Try /sys/module/amdgpu/version (Linux kernel driver version)
+    try:
+        if platform.system() == "Linux":
+            version_path = "/sys/module/amdgpu/version"
+            if os.path.exists(version_path):
+                with open(version_path, "r", encoding="utf-8") as f:
+                    return f.read().strip()
+    except Exception:
+        pass
+
+    return None
+
+
+def get_xpu_driver_version() -> str | None:
+    """Gets the Intel XPU driver version."""
+
+    try:
+        output = subprocess.check_output(
+            ["xpu-smi", "discovery"],
+            stderr=subprocess.DEVNULL,
+            text=True,
+        )
+        for line in output.split("\n"):
+            if "Driver Version" in line:
+                return line.split(":")[-1].strip()
+        return None
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        return None
+
+
+def get_npu_driver_version() -> str | None:
+    """Gets the Huawei NPU driver version."""
+
+    try:
+        output = subprocess.check_output(
+            ["npu-smi", "info", "-t", "board", "-i", "0"],
+            stderr=subprocess.DEVNULL,
+            text=True,
+        )
+        for line in output.split("\n"):
+            if "Software Version" in line:
+                return line.split()[-1].strip()
+        return None
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        return None
+
+
+def get_mps_driver_version() -> str | None:
+    """Gets the Apple Silicon (MPS) driver version via macOS version."""
+
+    try:
+        output = subprocess.check_output(
+            ["sw_vers", "-productVersion"],
+            stderr=subprocess.DEVNULL,
+            text=True,
+        )
+        return output.strip()
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        return None
+
+
+@dataclass
+class HereticVersionInfo:
+    """Detailed information about the heretic-llm installation."""
+
+    version: str
+    origin: str | None
+    is_standard_pypi: bool
+    metadata: dict[str, Any]
+
+
+def get_heretic_version_info() -> HereticVersionInfo:
+    """Detects version and installation source (PyPI, Git, Local) of heretic-llm."""
+
+    package_name = "heretic-llm"
+    origin_metadata: dict[str, Any] = {"type": "unknown"}
+    # This package must be installed for this code to run.
+    distribution = importlib.metadata.distribution(package_name)
+
+    base_version = distribution.version.lstrip("v")
+
+    try:
+        direct_url_content = distribution.read_text("direct_url.json")
+    except Exception:
+        direct_url_content = None
+
+    if not direct_url_content:
+        # Standard PyPI installation.
+        origin_metadata["type"] = "pypi"
+
+        return HereticVersionInfo(
+            version=base_version,
+            origin="PyPI",
+            is_standard_pypi=True,
+            metadata=origin_metadata,
+        )
+
+    data = json.loads(direct_url_content)
+
+    # Check for Git source.
+    if "vcs_info" in data and data["vcs_info"].get("vcs") == "git":
+        vcs_info = data["vcs_info"]
+        commit_hash = vcs_info.get("commit_id", "unknown")
+        repo_url = data.get("url", "unknown_repo")
+        requested_revision = vcs_info.get("requested_revision")
+
+        if requested_revision:
+            origin_str = (
+                f"Git ({repo_url}@{requested_revision} - commit: {commit_hash})"
+            )
+        else:
+            origin_str = f"Git ({repo_url} @ {commit_hash})"
+
+        origin_metadata.update(
+            {
+                "type": "git",
+                "url": repo_url,
+                "commit_hash": commit_hash,
+                "requested_revision": requested_revision,
+            }
+        )
+
+        return HereticVersionInfo(
+            version=base_version,
+            origin=origin_str,
+            is_standard_pypi=False,
+            metadata=origin_metadata,
+        )
+
+    # Check for local file/wheel directory.
+    if "url" in data and data["url"].startswith("file://"):
+        origin_metadata["type"] = "local"
+
+        return HereticVersionInfo(
+            version=base_version,
+            origin="Local",
+            is_standard_pypi=False,
+            metadata=origin_metadata,
+        )
+
+    return HereticVersionInfo(
+        version=base_version,
+        origin=None,
+        is_standard_pypi=False,
+        metadata=origin_metadata,
+    )
+
+
+def get_accelerator_info_dict() -> dict[str, Any]:
+    """Retrieves raw accelerator info (CUDA, ROCm, etc) directly into structured keys."""
+
+    if torch.cuda.is_available():
+        count = torch.cuda.device_count()
+        is_rocm = getattr(torch.version, "hip", None) is not None
+
+        # ROCm (AMD) and CUDA (NVIDIA) share the same API in PyTorch.
+        # We distinguish them by checking for the HIP version.
+        info: dict[str, Any] = {
+            "type": "ROCm" if is_rocm else "CUDA",
+            "api_name": "HIP Version" if is_rocm else "CUDA Version",
+            "api_version": torch.version.hip if is_rocm else torch.version.cuda,  # ty:ignore[unresolved-attribute]
+            "driver_version": get_amdgpu_driver_version()
+            if is_rocm
+            else get_nvidia_driver_version(),
+            "devices": [],
+        }
+
+        for i in range(count):
+            name = torch.cuda.get_device_name(i)
+            vram = torch.cuda.mem_get_info(i)[1] / (1024**3)
+            info["devices"].append({"name": name, "vram_gb": round(vram, 2)})
+
+        return info
+
+    if is_xpu_available():
+        count = torch.xpu.device_count()  # ty:ignore[unresolved-attribute]
+        return {
+            "type": "XPU",
+            "api_name": None,
+            "api_version": None,
+            "driver_version": get_xpu_driver_version(),
+            "devices": [{"name": torch.xpu.get_device_name(i)} for i in range(count)],  # ty:ignore[unresolved-attribute]
+        }
+
+    if is_mlu_available():
+        count = torch.mlu.device_count()  # ty:ignore[unresolved-attribute]
+        return {
+            "type": "MLU",
+            "api_name": None,
+            "api_version": None,
+            "driver_version": None,
+            "devices": [{"name": torch.mlu.get_device_name(i)} for i in range(count)],  # ty:ignore[unresolved-attribute]
+        }
+
+    if is_sdaa_available():
+        count = torch.sdaa.device_count()  # ty:ignore[unresolved-attribute]
+        return {
+            "type": "SDAA",
+            "api_name": None,
+            "api_version": None,
+            "driver_version": None,
+            "devices": [{"name": torch.sdaa.get_device_name(i)} for i in range(count)],  # ty:ignore[unresolved-attribute]
+        }
+
+    if is_musa_available():
+        count = torch.musa.device_count()  # ty:ignore[unresolved-attribute]
+        return {
+            "type": "MUSA",
+            "api_name": None,
+            "api_version": None,
+            "driver_version": None,
+            "devices": [{"name": torch.musa.get_device_name(i)} for i in range(count)],  # ty:ignore[unresolved-attribute]
+        }
+
+    if is_npu_available():
+        return {
+            "type": "NPU",
+            "api_name": "CANN Version",
+            "api_version": torch.version.cann,  # ty:ignore[unresolved-attribute]
+            "driver_version": get_npu_driver_version(),
+            "devices": [],  # Multi-NPU is less common.
+        }
+
+    if torch.backends.mps.is_available():
+        return {
+            "type": "MPS",
+            "api_name": None,
+            "api_version": None,
+            "driver_version": get_mps_driver_version(),
+            "devices": [{"name": "Apple Metal"}],
+        }
+
+    return {"type": None}
+
+
+def get_accelerator_info(include_warnings: bool = True) -> str:
+    """Convenience wrapper for hardware detection and console-friendly formatting."""
+
+    info = get_accelerator_info_dict()
+
+    if info["type"] is None:
+        suffix = " Operations will be slow." if include_warnings else ""
+        return (
+            f"[bold yellow]No GPU or other accelerator detected.{suffix}[/]\n".strip()
+        )
+
+    devices = info["devices"]
+    count = len(devices)
+    total_vram = sum(d.get("vram_gb", 0) for d in devices)
+
+    vram_suffix = f" ({total_vram:.2f} GB total VRAM)" if total_vram > 0 else ""
+    report = f"Detected [bold]{count or 1}[/] {info['type']} device(s){vram_suffix}\n"
+
+    if info.get("api_name") and info.get("api_version"):
+        report += f"{info['api_name']}: [bold]{info['api_version']}[/]\n"
+
+    driver = info.get("driver_version") or "Unknown"
+    report += f"Driver Version: [bold]{driver}[/]\n"
+
+    for i, dev in enumerate(devices):
+        vram = f" ({dev['vram_gb']:.2f} GB)" if dev.get("vram_gb") else ""
+        report += f"* {info['type']} {i}: [bold]{dev['name']}[/]{vram}\n"
+
+    return report.strip()
+
+
+def get_cpu_info_dict() -> dict[str, str | int | None]:
+    """Gets granular CPU identifiers using the py-cpuinfo library."""
+
+    info = cpuinfo.get_cpu_info()
+
+    return {
+        "brand": info.get("brand_raw"),
+        "vendor": info.get("vendor_id_raw"),
+        "family": info.get("family"),
+        "model": info.get("model"),
+        "stepping": info.get("stepping"),
+    }
+
+
+def get_cpu_info() -> str:
+    """Gets the CPU brand name."""
+
+    info = get_cpu_info_dict()
+    parts = []
+    parts.append(
+        f"Family {info['family']}, Model {info['model']}, Stepping {info['stepping']}"
+    )
+
+    details = f" ({'; '.join(parts)})" if parts else ""
+    brand = info["brand"] or "Unknown CPU"
+    return f"{brand}{details}"
+
+
+def get_python_env_info_dict() -> dict[str, str]:
+    implementation = platform.python_implementation()
+    compiler = platform.python_compiler()
+
+    # Check for Conda.
+    if "CONDA_PREFIX" in os.environ:
+        env_type = "Conda"
+    # Check for Virtualenv/Venv.
+    elif hasattr(sys, "base_prefix") and sys.base_prefix != sys.prefix:
+        env_type = "Virtualenv/Venv"
+    else:
+        env_type = "System"
+
+    return {
+        "version": platform.python_version(),
+        "implementation": implementation,
+        "compiler": compiler,
+        "environment": env_type,
+    }
+
+
+def get_python_env_info() -> str:
+    """Detects the type of Python environment (Conda, Venv, etc.) and build info."""
+
+    info = get_python_env_info_dict()
+    return f"{info['version']} ({info['implementation']}, {info['compiler']}) [{info['environment']}]"
+
+
+def get_package_version(name: str) -> str:
+    """Gets the installed version of a package, stripping local suffixes like +cu128."""
+
+    # Normalize name: pip considers hyphens and underscores equivalent.
+    normalized_name = name.lower().replace("_", "-")
+    version_str = importlib.metadata.version(normalized_name)
+    return version_str.split("+")[0] if "+" in version_str else version_str
+
+
+def get_requirements_dict() -> dict[str, str]:
+    """Recursively finds all direct and transitive dependencies of heretic-llm and core libraries."""
+
+    # We start with heretic-llm and the core compute libraries.
+    # PyTorch is not listed as a dependency in the heretic-llm package
+    # because installation is hardware-specific and must be done manually.
+    packages_to_check = ["heretic-llm", "torch", "torchaudio", "torchvision"]
+
+    visited = set()
+    required_packages = set()
+
+    while packages_to_check:
+        package = packages_to_check.pop(0)
+        # Normalize name: pip considers hyphens and underscores equivalent.
+        normalized_package = package.lower().replace("_", "-")
+        if normalized_package in visited:
+            continue
+        visited.add(normalized_package)
+
+        try:
+            distribution = importlib.metadata.distribution(normalized_package)
+            required_packages.add(normalized_package)
+            if distribution.requires:
+                for requirement in distribution.requires:
+                    # Requirements can include environment markers like '; extra == "hf"'
+                    # or version constraints. We should ignore optional 'extra' dependencies
+                    # to keep the reproduction environment clean and relevant.
+                    if ";" in requirement and "extra ==" in requirement:
+                        continue
+
+                    # We just want the base package name.
+                    match = re.match(r"^([a-zA-Z0-9_\-]+)", requirement)
+                    if match:
+                        dep_name = match.group(0).lower().replace("_", "-")
+                        if dep_name not in visited:
+                            packages_to_check.append(dep_name)
+        except importlib.metadata.PackageNotFoundError:
+            # If a package is listed as a dependency but not installed, we skip it.
+            continue
+
+    required_packages_sorted = sorted(required_packages)
+
+    # Lookup versions for all discovered packages.
+    dependencies = {}
+    version_info = get_heretic_version_info()
+
+    for package in required_packages_sorted:
+        # If heretic-llm was installed from source (Git/Local), exclude it
+        # from requirements.txt to prevent pip from downloading an unrelated
+        # version from PyPI during reproduction.
+        if package == "heretic-llm" and not version_info.is_standard_pypi:
+            continue
+
+        dependencies[package] = get_package_version(package)
+
+    return dependencies
@@ -1,32 +1,45 @@
 # SPDX-License-Identifier: AGPL-3.0-or-later
 # Copyright (C) 2025-2026  Philipp Emanuel Weidmann <pew@worldwidemann.com> + contributors

-import gc
 import getpass
+import hashlib
+import json
 import os
+import platform
+import random
+import tempfile
+import traceback
 from dataclasses import dataclass
+from datetime import datetime, timezone
 from importlib.metadata import version
 from pathlib import Path
 from typing import Any, TypeVar

+import huggingface_hub
+import numpy as np
 import questionary
+import tomli_w
 import torch
-from accelerate.utils import (
-    is_mlu_available,
-    is_musa_available,
-    is_sdaa_available,
-    is_xpu_available,
-)
 from datasets import DatasetDict, ReadInstruction, load_dataset, load_from_disk
 from datasets.config import DATASET_STATE_JSON_FILENAME
 from datasets.download.download_manager import DownloadMode
 from datasets.utils.info_utils import VerificationMode
+from huggingface_hub.utils import validate_repo_id
 from optuna import Trial
+from optuna.trial import FrozenTrial
 from psutil import Process
 from questionary import Choice, Style
 from rich.console import Console

 from .config import DatasetSpecification, Settings
+from .system import (
+    get_accelerator_info_dict,
+    get_cpu_info_dict,
+    get_heretic_version_info,
+    get_python_env_info_dict,
+    get_requirements_dict,
+    is_xpu_available,
+)

 print = Console(highlight=False).print

@@ -160,12 +173,54 @@ def format_duration(seconds: float) -> str:
        return f"{seconds}s"


+def format_exception(error: Exception) -> str:
+    # Walk causal chain to find a non-empty message.
+    current = error
+    while current is not None:
+        message = str(current).strip()
+        if message:
+            return message
+        current = current.__cause__ or current.__context__
+
+    # If there is no message in the entire causal chain, fall back to the complete traceback.
+    return traceback.format_exc().strip()
+
+
+def is_hf_path(path: str) -> bool:
+    """Checks whether a path likely refers to a Hugging Face repository."""
+
+    # Match Transformers: Existing local paths take precedence over Hub lookup,
+    # even if the path string is also a valid repository ID.
+    if Path(path).exists():
+        return False
+
+    validate_repo_id(path)
+    return True
+
+
@dataclass
 class Prompt:
    system: str
    user: str


+def get_split_slice(split_str: str, length: int) -> tuple[int, int]:
+    """Resolves a split specification into absolute (start, end) indices."""
+
+    # The split name is the part before the slice, e.g. "train" in "train[:400]".
+    split_name = split_str.split("[")[0]
+
+    # Associate the split with its number of examples (lines).
+    name_to_length = {split_name: length}
+
+    # Convert the instructions to absolute indices and select the first one.
+    absolute_instruction = ReadInstruction.from_spec(split_str).to_absolute(
+        name_to_length
+    )[0]
+
+    return absolute_instruction.from_, absolute_instruction.to
+
+
 def load_prompts(
    settings: Settings,
    specification: DatasetSpecification,
@@ -173,25 +228,43 @@ def load_prompts(
    path = specification.dataset
    split_str = specification.split

-    if os.path.isdir(path):
-        if Path(path, DATASET_STATE_JSON_FILENAME).exists():
+    if os.path.isfile(path):
+        # Plain text file with one prompt per line. Empty lines are ignored.
+        with open(path, encoding="utf-8") as file:
+            prompts = [line.strip() for line in file if line.strip()]
+
+        # The split is optional for text files. When given, it selects a subset
+        # of the lines using slice notation (e.g. "[:400]"). A synthetic split
+        # name is prepended because ReadInstruction expects a named split.
+        if split_str is not None:
+            start, end = get_split_slice(f"_{split_str}", len(prompts))
+            prompts = prompts[start:end]
+    else:
+        # All dataset sources require an explicit split and column.
+        if split_str is None:
+            raise ValueError(f'The "split" field is required for datasets: {path}')
+
+        if specification.column is None:
+            raise ValueError(f'The "column" field is required for datasets: {path}')
+
+        if is_hf_path(path):
+            dataset = load_dataset(
+                path,
+                revision=specification.commit,
+                split=split_str,
+            )
+        elif Path(path, DATASET_STATE_JSON_FILENAME).exists():
            # Dataset saved with datasets.save_to_disk; needs special handling.
            # Path should be the subdirectory for a particular split.
            dataset = load_from_disk(path)
            assert not isinstance(dataset, DatasetDict), (
                "Loading dataset dicts is not supported"
            )
-            # Parse the split instructions.
-            instruction = ReadInstruction.from_spec(split_str)
-            # Associate the split with its number of examples (lines).
-            split_name = str(dataset.split)
-            name2len = {split_name: len(dataset)}
-            # Convert the instructions to absolute indices and select the first one.
-            abs_instruction = instruction.to_absolute(name2len)[0]
-            # Get the dataset by applying the indices.
-            dataset = dataset[abs_instruction.from_ : abs_instruction.to]
+            # Parse the split instructions and apply them.
+            start, end = get_split_slice(split_str, len(dataset))
+            dataset = dataset[start:end]
        else:
-            # Path is a local directory.
+            # Path should be a local directory.
            dataset = load_dataset(
                path,
                split=split_str,
@@ -200,11 +273,8 @@ def load_prompts(
                # But also don't use cached data, as the dataset may have changed on disk.
                download_mode=DownloadMode.FORCE_REDOWNLOAD,
            )
-    else:
-        # Probably a repository path; let load_dataset figure it out.
-        dataset = load_dataset(path, split=split_str)

-    prompts = list(dataset[specification.column])
+        prompts = list(dataset[specification.column])

    if specification.prefix:
        prompts = [f"{specification.prefix} {prompt}" for prompt in prompts]
@@ -234,29 +304,7 @@ def batchify(items: list[T], batch_size: int) -> list[list[T]]:
    return [items[i : i + batch_size] for i in range(0, len(items), batch_size)]


-def empty_cache():
-    # Collecting garbage is not an idempotent operation, and to avoid OOM errors,
-    # gc.collect() has to be called both before and after emptying the backend cache.
-    # See https://github.com/p-e-w/heretic/pull/17 for details.
-    gc.collect()
-
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-    elif is_xpu_available():
-        torch.xpu.empty_cache()
-    elif is_mlu_available():
-        torch.mlu.empty_cache()  # ty:ignore[unresolved-attribute]
-    elif is_sdaa_available():
-        torch.sdaa.empty_cache()  # ty:ignore[unresolved-attribute]
-    elif is_musa_available():
-        torch.musa.empty_cache()  # ty:ignore[unresolved-attribute]
-    elif torch.backends.mps.is_available():
-        torch.mps.empty_cache()
-
-    gc.collect()
-
-
-def get_trial_parameters(trial: Trial) -> dict[str, str]:
+def get_trial_parameters(trial: Trial | FrozenTrial) -> dict[str, str]:
    params = {}

    direction_index = trial.user_attrs["direction_index"]
@@ -273,20 +321,29 @@ def get_trial_parameters(trial: Trial) -> dict[str, str]:

 def get_readme_intro(
    settings: Settings,
-    trial: Trial,
-    base_refusals: int,
-    bad_prompts: list[Prompt],
+    trial: Trial | FrozenTrial,
+    contains_reproducibility_information: bool,
 ) -> str:
-    if Path(settings.model).exists():
+    if is_hf_path(settings.model):
+        model_link = f"[{settings.model}](https://huggingface.co/{settings.model})"
+    else:
        # Hide the path, which may contain private information.
        model_link = "a model"
+
+    if contains_reproducibility_information:
+        reproducibility_instructions = """
+> [!TIP]
+> **This model is reproducible!**
+>
+> See the [README](reproduce/README.md) in the `reproduce` directory for more information.
+"""
    else:
-        model_link = f"[{settings.model}](https://huggingface.co/{settings.model})"
+        reproducibility_instructions = ""

    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://heretic-project.org) v{version("heretic-llm")}
+{reproducibility_instructions}
 ## Abliteration parameters

 | Parameter | Value |
@@ -305,10 +362,423 @@ def get_readme_intro(
 | Metric | This model | Original model ({model_link}) |
 | :----- | :--------: | :---------------------------: |
 | **KL divergence** | {trial.user_attrs["kl_divergence"]:.4f} | 0 *(by definition)* |
-| **Refusals** | {trial.user_attrs["refusals"]}/{len(bad_prompts)} | {base_refusals}/{
-        len(bad_prompts)
-    } |
+| **Refusals** | {trial.user_attrs["refusals"]}/{trial.user_attrs["n_bad_prompts"]} | {
+        trial.user_attrs["base_refusals"]
+    }/{trial.user_attrs["n_bad_prompts"]} |

 -----

 """
+
+
+def generate_config_toml(settings: Settings) -> str:
+    """Serializes the full Settings object to TOML."""
+
+    return tomli_w.dumps(settings.model_dump(exclude_none=True))
+
+
+def generate_requirements_txt() -> str:
+    """Collects direct project dependencies as a formatted string."""
+
+    requirements = [
+        f"{package}=={version}" for package, version in get_requirements_dict().items()
+    ]
+    return "\n".join(requirements) + "\n"
+
+
+def set_seed(seed: int):
+    """Sets the seed for all RNGs."""
+
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+
+
+def format_hf_link(
+    path: str,
+    commit: str | None = None,
+    is_dataset: bool = False,
+) -> str:
+    prefix = "datasets/" if is_dataset else ""
+    base_url = f"https://huggingface.co/{prefix}{path}"
+    link = f"[{path}]({base_url})"
+
+    if commit:
+        commit_url = f"{base_url}/commit/{commit}"
+        link += f" (Commit: [`{commit[:7]}`]({commit_url}))"
+
+    return link
+
+
+def generate_reproduce_readme(
+    settings: Settings,
+    checkpoint_filename: str,
+    trial: Trial | FrozenTrial,
+    include_system_information: bool,
+) -> str:
+    """Generates the contents of a README.md for the reproduce/ folder."""
+
+    heterogeneous_warning = ""
+
+    if include_system_information:
+        if torch.cuda.is_available():
+            count = torch.cuda.device_count()
+            if count > 1:
+                device_names = {torch.cuda.get_device_name(i) for i in range(count)}
+                if len(device_names) > 1:
+                    heterogeneous_warning = """
+> [!WARNING]
+> **Heterogeneous GPUs**
+>
+> This model was generated using multiple non-identical GPUs. When operations are distributed across different GPUs
+> (e.g. via `device_map='auto'`), non-deterministic behavior can occur.
+>
+> Reproducibility *cannot* be guaranteed in this environment.
+"""
+
+        cpu = get_cpu_info_dict()
+        python_env = get_python_env_info_dict()
+
+        accelerators = get_accelerator_info_dict()
+        if accelerators["type"] is None:
+            accelerator_report = "**No GPU or other accelerator detected.**"
+        else:
+            devices = accelerators["devices"]
+            total_vram = sum(device.get("vram_gb", 0) for device in devices)
+            vram_suffix = f" ({total_vram:.2f} GB total VRAM)" if total_vram > 0 else ""
+            accelerator_lines = [
+                f"- **{accelerators['type']}:** Detected {len(devices)} device(s){vram_suffix}"
+            ]
+
+            if accelerators.get("api_name") and accelerators.get("api_version"):
+                accelerator_lines.append(
+                    f"  - **{accelerators['api_name']}:** {accelerators['api_version']}"
+                )
+
+            if accelerators.get("driver_version"):
+                accelerator_lines.append(
+                    f"  - **Driver Version:** {accelerators['driver_version']}"
+                )
+
+            accelerator_lines.append("- **Devices:**")
+            for i, device in enumerate(devices):
+                vram = f" ({device['vram_gb']:.2f} GB)" if device.get("vram_gb") else ""
+                accelerator_lines.append(
+                    f"  - **{accelerators['type']} {i}:** {device['name']}{vram}"
+                )
+            accelerator_report = "\n".join(accelerator_lines)
+
+        system_report = f"""## System
+
+- **Python:** {python_env["version"]} ({python_env["implementation"]}, {python_env["compiler"]}) [{python_env["environment"]}]
+- **Operating system:** {platform.platform()} ({platform.machine()})
+- **CPU:** {cpu["brand"] or "Unknown"}
+
+### Accelerators
+
+{accelerator_report}
+
+"""
+        system_instructions = (
+            "1. Ensure your system matches the specifications in the **System** section above. "
+            "Exact reproducibility is only guaranteed if all aspects of your system are identical to the one the model was originally generated on.\n"
+        )
+    else:
+        system_report = ""
+        system_instructions = ""
+
+    version_info = get_heretic_version_info()
+    origin_warning = ""
+    if not version_info.is_standard_pypi:
+        if version_info.origin and version_info.origin.startswith("Git"):
+            repo_info = version_info.origin.split("Git (")[1].rstrip(")")
+            origin_warning = f"""
+> [!IMPORTANT]
+> **Git installation**
+>
+> This system installed Heretic from a Git repository: {repo_info}
+>
+> To reproduce the model, you must install Heretic from this exact repository and commit.
+"""
+        elif version_info.origin == "Local":
+            origin_warning = """
+> [!WARNING]
+> **Local code**
+>
+> This system installed Heretic from a local directory or wheel. Uncommitted or experimental code may have been executed.
+>
+> Reproducibility *cannot* be guaranteed in this environment.
+"""
+        else:
+            origin_warning = """
+> [!WARNING]
+> **Non-standard installation**
+>
+> This system installed Heretic from an unknown non-standard source.
+>
+> Reproducibility *cannot* be guaranteed in this environment.
+"""
+
+    pytorch_version = torch.__version__
+    pytorch_install_command = f"pip install torch=={pytorch_version}"
+    if "+" in pytorch_version:
+        suffix = pytorch_version.split("+")[1]
+        if suffix:
+            pytorch_install_command += (
+                f" --index-url https://download.pytorch.org/whl/{suffix}"
+            )
+
+    return f"""# Reproduction guide
+
+This directory contains the necessary information and assets to reproduce the results obtained during this Heretic run.{heterogeneous_warning}{origin_warning}
+
+## Models
+
+- **Base model:** {format_hf_link(settings.model, settings.model_commit)}
+
+## Datasets
+
+- **Good prompts:** {format_hf_link(settings.good_prompts.dataset, settings.good_prompts.commit, is_dataset=True)}
+- **Bad prompts:** {format_hf_link(settings.bad_prompts.dataset, settings.bad_prompts.commit, is_dataset=True)}
+- **Good evaluation prompts:** {format_hf_link(settings.good_evaluation_prompts.dataset, settings.good_evaluation_prompts.commit, is_dataset=True)}
+- **Bad evaluation prompts:** {format_hf_link(settings.bad_evaluation_prompts.dataset, settings.bad_evaluation_prompts.commit, is_dataset=True)}
+
+## Selected trial
+
+- **Trial number:** {trial.user_attrs["index"]}
+- **KL divergence:** {trial.user_attrs["kl_divergence"]:.6f}
+- **Refusals:** {trial.user_attrs["refusals"]}/{trial.user_attrs["n_bad_prompts"]}
+
+{system_report}## Environment
+
+- **Heretic:** v{version_info.version}{f" (Origin: {version_info.origin})" if version_info.origin else ""}
+- **PyTorch:** {pytorch_version}
+- **Other dependencies:** See [`requirements.txt`](requirements.txt).
+
+## Contents of this directory
+
+- [`requirements.txt`](requirements.txt): The exact versions of all Python packages.
+- [`config.toml`](config.toml): The exact configuration used, including the RNG seed.
+- [`{checkpoint_filename}`]({checkpoint_filename}): The Optuna study journal containing the history of all trials.
+- [`SHA256SUMS`](SHA256SUMS): Cryptographic hashes for all weight files.
+- [`reproduce.json`](reproduce.json): A machine-readable file containing all reproducibility information.
+
+## How to reproduce
+
+> [!TIP]
+> You can automate this process, including all verification steps, by downloading the `reproduce.json` file and running
+> `heretic --reproduce reproduce.json`.
+
+{system_instructions}1. Install the exact version of Heretic indicated in the **Environment** section above, from its original source.
+1. Install the packages listed in `requirements.txt`: `pip install -r requirements.txt`
+1. Install the correct version of PyTorch: `{pytorch_install_command}`
+1. Place the provided `config.toml` in your working directory.
+1. Run Heretic without any additional arguments: `heretic`
+1. Wait for the run to finish, then select trial **{trial.user_attrs["index"]}** and export the model.
+1. Verify that the weight files have been exactly reproduced by comparing their SHA-256 hashes against those in `SHA256SUMS`:
+   `sha256sum -c SHA256SUMS` (or look at the hashes online if you uploaded to Hugging Face)
+
+> [!TIP]
+> To use the included Optuna study journal `{checkpoint_filename}`, place it in the checkpoints directory (usually `checkpoints/`) before running Heretic.
+>
+> This allows you to export other models from the Pareto front, or to run additional trials without having to re-run the stored trials.
+"""
+
+
+def generate_reproduce_json(
+    settings: Settings,
+    trial: Trial | FrozenTrial,
+    timestamp: str,
+    uploaded_model_hashes: dict[str, str],
+    include_system_information: bool,
+) -> str:
+    """Generates the contents of a reproduce.json file for the reproduce/ folder."""
+
+    version_info = get_heretic_version_info()
+
+    data = {
+        "version": "2",  # Version number of the reproduce.json file format, to allow for future changes.
+        "timestamp": timestamp,
+        "system": None,  # Defined here to preserve insertion order.
+        "environment": {
+            "heretic": {
+                "version": version_info.version,
+                "is_standard_pypi": version_info.is_standard_pypi,
+                "metadata": version_info.metadata,
+            },
+            "pytorch_version": torch.__version__,
+            "requirements": get_requirements_dict(),
+        },
+        "settings": settings.model_dump(),
+        "parameters": {
+            "direction_index": trial.user_attrs["direction_index"],
+            "abliteration_parameters": trial.user_attrs["parameters"],
+        },
+        "metrics": {
+            "kl_divergence": trial.user_attrs["kl_divergence"],
+            "refusals": trial.user_attrs["refusals"],
+            "base_refusals": trial.user_attrs["base_refusals"],
+            "n_bad_prompts": trial.user_attrs["n_bad_prompts"],
+        },
+        "hashes": uploaded_model_hashes,
+    }
+
+    if include_system_information:
+        data["system"] = {
+            "python": get_python_env_info_dict(),
+            "os": {
+                "platform": platform.platform(),
+                "machine": platform.machine(),
+            },
+            "cpu": get_cpu_info_dict(),
+            "accelerators": get_accelerator_info_dict(),
+        }
+    else:
+        del data["system"]
+
+    return json.dumps(data, indent=4)
+
+
+def generate_sha256sums(hashes: dict[str, str]) -> str:
+    """Generates GNU Coreutils compatible SHA256SUMS file content."""
+
+    lines = []
+
+    for filename, sha256 in sorted(hashes.items()):
+        # Use '*' to indicate binary mode for model weights.
+        lines.append(f"{sha256} *{filename}")
+
+    return "\n".join(lines) + "\n"
+
+
+# TODO: Replace this with hashlib.file_digest when we drop support for Python 3.10.
+def get_file_sha256(file_path: str | Path) -> str:
+    hash = hashlib.sha256()
+
+    with open(file_path, "rb") as file:
+        # Read the file in 64 kB blocks.
+        for block in iter(lambda: file.read(65536), b""):
+            hash.update(block)
+
+    return hash.hexdigest()
+
+
+def create_reproduce_folder(
+    path: Path,
+    settings: Settings,
+    checkpoint_path: str | Path,
+    trial: Trial | FrozenTrial,
+    uploaded_model_hashes: dict[str, str],
+    include_system_information: bool,
+):
+    reproduce_dir = path / "reproduce"
+    reproduce_dir.mkdir(parents=True, exist_ok=True)
+
+    checkpoint_filename = Path(checkpoint_path).name
+
+    # Fetch commit hash for the base model.
+    settings.model_commit = huggingface_hub.model_info(settings.model).sha
+
+    # Fetch commit hashes for all HF datasets to ensure reproducibility.
+    for spec in [
+        settings.good_prompts,
+        settings.bad_prompts,
+        settings.good_evaluation_prompts,
+        settings.bad_evaluation_prompts,
+    ]:
+        spec.commit = huggingface_hub.dataset_info(spec.dataset).sha
+
+    # Strip microseconds and timezone for a clean format.
+    timestamp = (
+        datetime.now(timezone.utc).replace(microsecond=0, tzinfo=None).isoformat()
+    )
+
+    (reproduce_dir / "requirements.txt").write_text(
+        generate_requirements_txt(),
+        encoding="utf-8",
+    )
+
+    (reproduce_dir / "config.toml").write_text(
+        generate_config_toml(settings),
+        encoding="utf-8",
+    )
+
+    if uploaded_model_hashes:
+        (reproduce_dir / "SHA256SUMS").write_text(
+            generate_sha256sums(uploaded_model_hashes),
+            encoding="utf-8",
+        )
+
+    (reproduce_dir / "reproduce.json").write_text(
+        generate_reproduce_json(
+            settings,
+            trial,
+            timestamp=timestamp,
+            uploaded_model_hashes=uploaded_model_hashes,
+            include_system_information=include_system_information,
+        ),
+        encoding="utf-8",
+    )
+
+    (reproduce_dir / "README.md").write_text(
+        generate_reproduce_readme(
+            settings,
+            checkpoint_filename,
+            trial,
+            include_system_information=include_system_information,
+        ),
+        encoding="utf-8",
+    )
+
+    # Copy Optuna study journal.
+    checkpoint_file = Path(checkpoint_path)
+    if checkpoint_file.exists():
+        (reproduce_dir / checkpoint_file.name).write_bytes(checkpoint_file.read_bytes())
+
+
+def upload_reproduce_folder(
+    repo_id: str,
+    settings: Settings,
+    token: str,
+    checkpoint_path: str | Path,
+    trial: Trial | FrozenTrial,
+    include_system_information: bool,
+):
+    api = huggingface_hub.HfApi()
+    info = api.model_info(repo_id=repo_id, files_metadata=True, token=token)
+
+    if not info.siblings:
+        raise RuntimeError("Could not fetch uploaded model hashes.")
+
+    # For weights, we only care about safetensors.
+    weight_extensions = (".safetensors",)
+
+    uploaded_model_hashes = {}
+
+    for file in info.siblings:
+        if file.rfilename.endswith(weight_extensions):
+            sha256 = getattr(file, "lfs", {}).get("sha256")
+            if not sha256:
+                raise RuntimeError("Could not fetch uploaded model hashes.")
+            uploaded_model_hashes[file.rfilename] = sha256
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        tmp_path = Path(tmpdir)
+        create_reproduce_folder(
+            tmp_path,
+            settings,
+            checkpoint_path=checkpoint_path,
+            trial=trial,
+            uploaded_model_hashes=uploaded_model_hashes,
+            include_system_information=include_system_information,
+        )
+
+        reproduce_dir = tmp_path / "reproduce"
+        for file_path in reproduce_dir.iterdir():
+            if file_path.is_file():
+                huggingface_hub.upload_file(
+                    path_or_fileobj=str(file_path),
+                    path_in_repo=f"reproduce/{file_path.name}",
+                    repo_id=repo_id,
+                    token=token,
+                )
@@ -7,6 +7,10 @@ resolution-markers = [
    "python_full_version < '3.11'",
 ]

+[options]
+exclude-newer = "0001-01-01T00:00:00Z" # This has no effect and is included for backwards compatibility when using relative exclude-newer values.
+exclude-newer-span = "P7D"
+
 [[package]]
 name = "absl-py"
 version = "2.4.0"
@@ -46,7 +50,7 @@ wheels = [

 [[package]]
 name = "aiohttp"
-version = "3.13.2"
+version = "3.13.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "aiohappyeyeballs" },
@@ -58,110 +62,110 @@ dependencies = [
    { name = "propcache" },
    { name = "yarl" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/45/4a/064321452809dae953c1ed6e017504e72551a26b6f5708a5a80e4bf556ff/aiohttp-3.13.4.tar.gz", hash = "sha256:d97a6d09c66087890c2ab5d49069e1e570583f7ac0314ecf98294c1b6aaebd38", size = 7859748, upload-time = "2026-03-28T17:19:40.6Z" }
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