build: bump version to 1.4.0

fix: minor cleanups and improvements
feat: automatically reproduce model from reproduce.json (#326 )
2026-09-10 22:28:44 -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 1671 additions and 722 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
@@ -27,6 +27,12 @@ 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

@@ -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,
@@ -91,15 +123,15 @@ n_trials = 200
 # Number of trials that use random sampling for the purpose of exploration.
 n_startup_trials = 60

-# Random seed for reproducible optimization. Set to an integer to enable.
-# Applies to Python's random module, NumPy, PyTorch, and Optuna.
-# seed = 75
-
 # 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",
@@ -137,11 +169,10 @@ 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
+# 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]
@@ -150,7 +181,6 @@ split = "train[:400]"
 column = "text"
 residual_plot_label = '"Harmless" prompts'
 residual_plot_color = "royalblue"
-commit = ""

 # Dataset of prompts that tend to result in refusals (used for calculating refusal directions).
 [bad_prompts]
@@ -159,18 +189,15 @@ split = "train[:400]"
 column = "text"
 residual_plot_label = '"Harmful" prompts'
 residual_plot_color = "darkorange"
-commit = ""

 # Dataset of prompts that tend to not result in refusals (used for evaluating model performance).
 [good_evaluation_prompts]
 dataset = "mlabonne/harmless_alpaca"
 split = "test[:100]"
 column = "text"
-commit = ""

 # Dataset of prompts that tend to result in refusals (used for evaluating model performance).
 [bad_evaluation_prompts]
 dataset = "mlabonne/harmful_behaviors"
 split = "test[:100]"
 column = "text"
-commit = ""
@@ -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"
@@ -25,15 +25,13 @@ 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",
@@ -41,7 +39,7 @@ dependencies = [
    "rich~=14.3",
    "tomli-w~=1.2",
    "tqdm~=4.67",
-    "transformers~=5.3",
+    "transformers[kernels]~=5.6",
 ]

 [project.optional-dependencies]
@@ -60,8 +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"
@@ -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="",
@@ -53,15 +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.",
-    )
-    commit: str | None = Field(
-        default=None,
-        description="Hugging Face commit hash of the dataset.",
+        exclude=True,
    )


@@ -80,12 +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(
@@ -126,9 +171,14 @@ class Settings(BaseSettings):
        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(
@@ -139,6 +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(
@@ -183,36 +236,45 @@ class Settings(BaseSettings):
            "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(
@@ -232,7 +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."
@@ -240,7 +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), '
@@ -291,6 +353,7 @@ class Settings(BaseSettings):
    study_checkpoint_dir: str = Field(
        default="checkpoints",
        description="Directory to save and load study progress to/from.",
+        exclude=True,
    )

    benchmarks: list[BenchmarkSpecification] = Field(
@@ -352,10 +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",
@@ -397,14 +472,6 @@ 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",
@@ -17,11 +17,15 @@ 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
@@ -43,7 +47,7 @@ import questionary
 import torch
 import torch.nn.functional as F
 import transformers
-from huggingface_hub import ModelCard, ModelCardData
+from huggingface_hub import HfApi, ModelCard, ModelCardData
 from lm_eval.models.huggingface import HFLM
 from optuna import Trial, TrialPruned
 from optuna.exceptions import ExperimentalWarning
@@ -51,25 +55,32 @@ from optuna.samplers import TPESampler
 from optuna.storages import JournalStorage
 from optuna.storages.journal import JournalFileBackend, JournalFileOpenLock
 from optuna.study import StudyDirection
-from optuna.trial import TrialState
+from optuna.trial import TrialState, create_trial
 from pydantic import ValidationError
 from questionary import Choice, Style
 from rich.table import Table
 from rich.traceback import install

 from .analyzer import Analyzer
-from .config import QuantizationMethod
+from .config import ExportStrategy, QuantizationMethod
 from .evaluator import Evaluator
 from .model import AbliterationParameters, Model, get_model_class
+from .reproduce import (
+    check_environment,
+    collect_reproducibles,
+    load_reproduction_information,
+)
 from .system import empty_cache, get_accelerator_info
 from .utils import (
    format_duration,
+    format_exception,
+    get_file_sha256,
    get_readme_intro,
    get_trial_parameters,
+    is_hf_path,
    load_prompts,
    print,
    print_memory_usage,
-    prompt_confirm,
    prompt_password,
    prompt_path,
    prompt_select,
@@ -79,17 +90,23 @@ from .utils import (
 )


-def obtain_merge_strategy(settings: Settings) -> str | None:
+def obtain_export_strategy(
+    settings: Settings,
+    model: Model,
+) -> ExportStrategy | None:
    """
-    Prompts the user for how to proceed with saving the model.
+    Gets the export strategy from settings or prompts the user.
    Provides info to the user if the model is quantized on memory use.
-    Returns "merge", "adapter", or None (if cancelled/invalid).
+    Returns an export strategy, or None if cancelled.
    """

+    if settings.export_strategy is not None:
+        return settings.export_strategy
+
    if settings.quantization == QuantizationMethod.BNB_4BIT:
        print()
        print(
-            "Model was loaded with quantization. Merging requires reloading the base model."
+            "The model was loaded with quantization. Merging requires reloading the base model."
        )
        print(
            "[yellow]WARNING: CPU merging requires dequantizing the entire model to system RAM.[/]"
@@ -108,7 +125,10 @@ def obtain_merge_strategy(settings: Settings) -> str | None:
                    settings.model,
                    device_map="meta",
                    torch_dtype=torch.bfloat16,
-                    trust_remote_code=True,
+                    trust_remote_code=True
+                    if settings.model in model.trusted_models
+                    else None,
+                    **model.revision_kwargs,
                )
                footprint_bytes = meta_model.get_memory_footprint()
                footprint_gb = footprint_bytes / (1024**3)
@@ -124,33 +144,29 @@ def obtain_merge_strategy(settings: Settings) -> str | None:
            print(
                "[yellow]Example: A 27B model requires ~80GB RAM. A 70B model requires ~200GB RAM.[/]"
            )
+
        print()

-        strategy = prompt_select(
-            "How do you want to proceed?",
-            choices=[
-                Choice(
-                    title="Merge LoRA into full model"
-                    + (
-                        ""
-                        if settings.quantization == QuantizationMethod.NONE
-                        else " (requires sufficient RAM)"
-                    ),
-                    value="merge",
+    strategy = prompt_select(
+        "How do you want to export the model?",
+        choices=[
+            Choice(
+                title="Merge the abliteration LoRA and export the full model"
+                + (
+                    ""
+                    if settings.quantization == QuantizationMethod.NONE
+                    else " (requires sufficient RAM)"
                ),
-                Choice(
-                    title="Cancel",
-                    value="cancel",
-                ),
-            ],
-        )
+                value=ExportStrategy.MERGE,
+            ),
+            Choice(
+                title="Export the abliteration LoRA only (can be merged later)",
+                value=ExportStrategy.ADAPTER,
+            ),
+        ],
+    )

-        if strategy == "cancel":
-            return None
-
-        return strategy
-    else:
-        return "merge"
+    return strategy


 def run():
@@ -163,7 +179,9 @@ def run():

    # Modified "Pagga" font from https://budavariam.github.io/asciiart-text/
    print(f"[cyan]█░█░█▀▀░█▀▄░█▀▀░▀█▀░█░█▀▀[/]  v{version('heretic-llm')}")
-    print("[cyan]█▀█░█▀▀░█▀▄░█▀▀░░█░░█░█░░[/]")
+    print(
+        "[cyan]█▀█░█▀▀░█▀▄░█▀▀░░█░░█░█░░[/]  [blue underline]https://heretic-project.org[/]"
+    )
    print(
        "[cyan]▀░▀░▀▀▀░▀░▀░▀▀▀░░▀░░▀░▀▀▀[/]  [blue underline]https://github.com/p-e-w/heretic[/]"
    )
@@ -172,6 +190,9 @@ def run():
    if (
        # There is at least one argument (argv[0] is the program name).
        len(sys.argv) > 1
+        # Heretic is being invoked in standard (model processing) mode.
+        and "--collect-reproducibles" not in sys.argv
+        and "--reproduce" not in sys.argv
        # No model has been explicitly provided.
        and "--model" not in sys.argv
        # The last argument is a parameter value rather than a flag (such as "--help").
@@ -180,6 +201,13 @@ def run():
        # Assume the last argument is the model.
        sys.argv.insert(-1, "--model")

+    # Work around the "model" argument being required
+    # when Heretic is invoked in a non-processing mode.
+    if (
+        "--collect-reproducibles" in sys.argv or "--reproduce" in sys.argv
+    ) and "--model" not in sys.argv:
+        sys.argv.extend(["--model", ""])
+
    try:
        # The required argument "model" must be provided by the user,
        # either on the command line or in the configuration file.
@@ -187,8 +215,10 @@ def run():
    except ValidationError as error:
        print(f"[red]Configuration contains [bold]{error.error_count()}[/] errors:[/]")

-        for error in error.errors():
-            print(f"[bold]{error['loc'][0]}[/]: [yellow]{error['msg']}[/]")
+        for error_details in error.errors():
+            print(
+                f"[bold]{error_details['loc'][0]}[/]: [yellow]{error_details['msg']}[/]"
+            )

        print()
        print(
@@ -196,6 +226,35 @@ def run():
        )
        return

+    if settings.collect_reproducibles is not None:
+        collect_reproducibles(settings.collect_reproducibles)
+        return
+
+    reproduction_mode = settings.reproduce is not None
+
+    if settings.reproduce is not None:
+        print(f"Loading reproduction information from [bold]{settings.reproduce}[/]...")
+        # FIXME: "Reproduction"/"reproducibility" name inconsistency!
+        reproduction_information = load_reproduction_information(settings.reproduce)
+
+        if reproduction_information["version"] not in ["1", "2"]:
+            print(
+                (
+                    f"[red]Unsupported file format version: [bold]{reproduction_information['version']}[/].[/] "
+                    "Try loading the file with a newer version of Heretic."
+                )
+            )
+            return
+
+        if not check_environment(reproduction_information):
+            return
+
+        print()
+
+        verify_hashes = reproduction_information["version"] != "1"
+
+        settings = Settings.model_validate(reproduction_information["settings"])
+
    if settings.seed is None:
        settings.seed = random.randint(0, 2**32 - 1)

@@ -245,7 +304,11 @@ def run():
    except IndexError:
        existing_study = None

-    if existing_study is not None and settings.evaluate_model is None:
+    if (
+        existing_study is not None
+        and settings.evaluate_model is None
+        and not reproduction_mode
+    ):
        choices = []

        if existing_study.user_attrs["finished"]:
@@ -350,7 +413,12 @@ def run():
                    # We cannot recover from this.
                    raise

-                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})[/]")
+
                break

            response_lengths = [
@@ -424,9 +492,6 @@ 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)
@@ -464,8 +529,12 @@ 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
@@ -571,7 +640,8 @@ def run():

        trial.set_user_attr("kl_divergence", kl_divergence)
        trial.set_user_attr("refusals", refusals)
-        trial.set_user_attr("total_refusal_prompts", len(evaluator.bad_prompts))
+        trial.set_user_attr("base_refusals", evaluator.base_refusals)
+        trial.set_user_attr("n_bad_prompts", len(evaluator.bad_prompts))

        return score

@@ -583,168 +653,198 @@ def run():
            trial.study.stop()
            raise TrialPruned()

-    study = optuna.create_study(
-        sampler=TPESampler(
-            n_startup_trials=settings.n_startup_trials,
-            n_ei_candidates=128,
-            multivariate=True,
-            seed=settings.seed,
-        ),
-        directions=[StudyDirection.MINIMIZE, StudyDirection.MINIMIZE],
-        storage=storage,
-        study_name="heretic",
-        load_if_exists=True,
-    )
-
-    study.set_user_attr("settings", settings.model_dump_json())
-    study.set_user_attr("finished", False)
-
-    def count_completed_trials() -> int:
-        # Count number of complete trials to compute trials to run.
-        return sum([(1 if t.state == TrialState.COMPLETE else 0) for t in study.trials])
-
-    start_index = trial_index = count_completed_trials()
-    if start_index > 0:
-        print()
-        print("Resuming existing study.")
-
-    try:
-        study.optimize(
-            objective_wrapper,
-            n_trials=settings.n_trials - count_completed_trials(),
+    if not reproduction_mode:
+        study = optuna.create_study(
+            sampler=TPESampler(
+                n_startup_trials=settings.n_startup_trials,
+                n_ei_candidates=128,
+                multivariate=True,
+                seed=settings.seed,
+            ),
+            directions=[StudyDirection.MINIMIZE, StudyDirection.MINIMIZE],
+            storage=storage,
+            study_name="heretic",
+            load_if_exists=True,
        )
-    except KeyboardInterrupt:
-        # This additional handler takes care of the small chance that KeyboardInterrupt
-        # is raised just between trials, which wouldn't be caught by the handler
-        # defined in objective_wrapper above.
-        pass

-    if count_completed_trials() == settings.n_trials:
-        study.set_user_attr("finished", True)
+        study.set_user_attr("settings", settings.model_dump_json())
+        study.set_user_attr("finished", False)
+
+        start_index = trial_index = len(study.trials)
+        if start_index > 0:
+            print()
+            print("Resuming existing study.")
+
+        try:
+            study.optimize(
+                objective_wrapper,
+                n_trials=settings.n_trials - len(study.trials),
+            )
+        except KeyboardInterrupt:
+            # This additional handler takes care of the small chance that KeyboardInterrupt
+            # is raised just between trials, which wouldn't be caught by the handler
+            # defined in objective_wrapper above.
+            pass
+
+        if len(study.trials) == settings.n_trials:
+            study.set_user_attr("finished", True)

    while True:
-        # If no trials at all have been evaluated, the study must have been stopped
-        # by pressing Ctrl+C while the first trial was running. In this case, we just
-        # re-raise the interrupt to invoke the standard handler defined below.
-        completed_trials = [t for t in study.trials if t.state == TrialState.COMPLETE]
-        if not completed_trials:
-            raise KeyboardInterrupt
+        if not reproduction_mode:
+            # If no trials at all have been evaluated, the study must have been stopped
+            # by pressing Ctrl+C while the first trial was running. In this case, we just
+            # re-raise the interrupt to invoke the standard handler defined below.
+            completed_trials = [
+                t for t in study.trials if t.state == TrialState.COMPLETE
+            ]
+            if not completed_trials:
+                raise KeyboardInterrupt

-        # Get the Pareto front of trials. We can't use study.best_trials directly
-        # as get_score() doesn't return the pure KL divergence and refusal count.
-        # Note: Unlike study.best_trials, this does not handle objective constraints.
-        sorted_trials = sorted(
-            completed_trials,
-            key=lambda trial: (
-                trial.user_attrs["refusals"],
-                trial.user_attrs["kl_divergence"],
-            ),
-        )
-        min_divergence = math.inf
-        best_trials = []
-        for trial in sorted_trials:
-            kl_divergence = trial.user_attrs["kl_divergence"]
-            if kl_divergence < min_divergence:
-                min_divergence = kl_divergence
-                best_trials.append(trial)
-
-        choices = [
-            Choice(
-                title=(
-                    f"[Trial {trial.user_attrs['index']:>3}] "
-                    f"Refusals: {trial.user_attrs['refusals']:>2}/{len(evaluator.bad_prompts)}, "
-                    f"KL divergence: {trial.user_attrs['kl_divergence']:.4f}"
+            # Get the Pareto front of trials. We can't use study.best_trials directly
+            # as get_score() doesn't return the pure KL divergence and refusal count.
+            # Note: Unlike study.best_trials, this does not handle objective constraints.
+            sorted_trials = sorted(
+                completed_trials,
+                key=lambda trial: (
+                    trial.user_attrs["refusals"],
+                    trial.user_attrs["kl_divergence"],
                ),
-                value=trial,
            )
-            for trial in best_trials
-        ]
+            min_divergence = math.inf
+            best_trials = []
+            for trial in sorted_trials:
+                kl_divergence = trial.user_attrs["kl_divergence"]
+                if kl_divergence < min_divergence:
+                    min_divergence = kl_divergence
+                    best_trials.append(trial)

-        choices.append(
-            Choice(
-                title="Run additional trials",
-                value="continue",
-            )
-        )
+            choices = [
+                Choice(
+                    title=(
+                        f"[Trial {trial.user_attrs['index']:>3}] "
+                        f"Refusals: {trial.user_attrs['refusals']:>2}/{len(evaluator.bad_prompts)}, "
+                        f"KL divergence: {trial.user_attrs['kl_divergence']:.4f}"
+                    ),
+                    value=trial,
+                )
+                for trial in best_trials
+            ]

-        choices.append(
-            Choice(
-                title="Exit program",
-                value="",
+            choices.append(
+                Choice(
+                    title="Run additional trials",
+                    value="continue",
+                )
            )
-        )

-        print()
-        print("[bold green]Optimization finished![/]")
-        print()
-        print(
-            (
-                "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, "
-                "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.[/]"
+            choices.append(
+                Choice(
+                    title="Exit program",
+                    value="",
+                )
+            )
+
+            print()
+            print("[bold green]Optimization finished![/]")
+            print()
+            print(
+                (
+                    "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.[/]"
+                )
            )
-        )

        while True:
-            print()
-            trial = prompt_select("Which trial do you want to use?", choices)
+            if reproduction_mode:
+                parameters = reproduction_information["parameters"]
+                metrics = reproduction_information["metrics"]
+
+                trial = create_trial(
+                    values=[],
+                    user_attrs={
+                        "direction_index": parameters["direction_index"],
+                        "parameters": parameters["abliteration_parameters"],
+                        "kl_divergence": metrics["kl_divergence"],
+                        "refusals": metrics["refusals"],
+                        "base_refusals": metrics["base_refusals"],
+                        "n_bad_prompts": metrics["n_bad_prompts"],
+                    },
+                )
+
+                print()
+                print("Restoring model from reproduction information...")
+            else:
+                print()
+                trial = prompt_select("Which trial do you want to use?", choices)
+
+                if trial is None or trial == "":
+                    return
+
+                if trial == "continue":
+                    while True:
+                        try:
+                            n_additional_trials = prompt_text(
+                                "How many additional trials do you want to run?"
+                            )
+                            if n_additional_trials is None or n_additional_trials == "":
+                                n_additional_trials = 0
+                                break
+                            n_additional_trials = int(n_additional_trials)
+                            if n_additional_trials > 0:
+                                break
+                            print("[red]Please enter a number greater than 0.[/]")
+                        except ValueError:
+                            print("[red]Please enter a number.[/]")
+
+                    if n_additional_trials == 0:
+                        continue
+
+                    settings.n_trials += n_additional_trials
+                    study.set_user_attr("settings", settings.model_dump_json())
+                    study.set_user_attr("finished", False)

-            if trial == "continue":
-                while True:
                    try:
-                        n_additional_trials = prompt_text(
-                            "How many additional trials do you want to run?"
+                        study.optimize(
+                            objective_wrapper,
+                            n_trials=settings.n_trials - len(study.trials),
                        )
-                        if n_additional_trials is None or n_additional_trials == "":
-                            n_additional_trials = 0
-                            break
-                        n_additional_trials = int(n_additional_trials)
-                        if n_additional_trials > 0:
-                            break
-                        print("[red]Please enter a number greater than 0.[/]")
-                    except ValueError:
-                        print("[red]Please enter a number.[/]")
+                    except KeyboardInterrupt:
+                        pass

-                if n_additional_trials == 0:
-                    continue
+                    if len(study.trials) == settings.n_trials:
+                        study.set_user_attr("finished", True)

-                settings.n_trials += n_additional_trials
-                study.set_user_attr("settings", settings.model_dump_json())
-                study.set_user_attr("finished", False)
+                    break

-                try:
-                    study.optimize(
-                        objective_wrapper,
-                        n_trials=settings.n_trials - count_completed_trials(),
-                    )
-                except KeyboardInterrupt:
-                    pass
+                print()
+                print(
+                    f"Restoring model from trial [bold]{trial.user_attrs['index']}[/]..."
+                )

-                if count_completed_trials() == settings.n_trials:
-                    study.set_user_attr("finished", True)
-
-                break
-
-            elif trial is None or trial == "":
-                return
-
-            print()
-            print(f"Restoring model from trial [bold]{trial.user_attrs['index']}[/]...")
            print("* Parameters:")
            for name, value in get_trial_parameters(trial).items():
                print(f"  * {name} = [bold]{value}[/]")
-            print("* Resetting model...")
-            model.reset_model()
-            print("* Abliterating...")
-            model.abliterate(
-                refusal_directions,
-                trial.user_attrs["direction_index"],
-                {
-                    k: AbliterationParameters(**v)
-                    for k, v in trial.user_attrs["parameters"].items()
-                },
-            )
+
+            # Per https://github.com/huggingface/peft/issues/868#issuecomment-1820642893
+            # once a LoRA is merged it's expected to be empty. Provide a utility function
+            # to restore the previous LoRA-ified state.
+            def reset_trial_model():
+                print("* Resetting model...")
+                model.reset_model()
+                print("* Abliterating...")
+                model.abliterate(
+                    refusal_directions,
+                    trial.user_attrs["direction_index"],
+                    {
+                        k: AbliterationParameters(**v)
+                        for k, v in trial.user_attrs["parameters"].items()
+                    },
+                )
+
+            reset_trial_model()

            while True:
                print()
@@ -755,12 +855,20 @@ def run():
                        "Upload the model to Hugging Face",
                        "Chat with the model",
                        "Benchmark the model",
-                        "Return to the trial selection menu",
+                        Choice(
+                            title="Exit program"
+                            if reproduction_mode
+                            else "Return to the trial selection menu",
+                            value="",
+                        ),
                    ],
                )

-                if action is None or action == "Return to the trial selection menu":
-                    break
+                if action is None or action == "":
+                    if reproduction_mode:
+                        return
+                    else:
+                        break

                # All actions are wrapped in a try/except block so that if an error occurs,
                # another action can be tried, instead of the program crashing and losing
@@ -772,23 +880,57 @@ def run():
                            if not save_directory:
                                continue

-                            strategy = obtain_merge_strategy(settings)
+                            strategy = obtain_export_strategy(settings, model)
                            if strategy is None:
                                continue

-                            if strategy == "adapter":
+                            if strategy == ExportStrategy.ADAPTER:
                                print("Saving LoRA adapter...")
-                                model.model.save_pretrained(save_directory)
+                                model.model.save_pretrained(
+                                    save_directory,
+                                    max_shard_size=settings.max_shard_size,
+                                )
                            else:
                                print("Saving merged model...")
                                merged_model = model.get_merged_model()
-                                merged_model.save_pretrained(save_directory)
+                                merged_model.save_pretrained(
+                                    save_directory,
+                                    max_shard_size=settings.max_shard_size,
+                                )
                                del merged_model
                                empty_cache()
                                model.tokenizer.save_pretrained(save_directory)
+                                if model.processor is not None:
+                                    model.processor.save_pretrained(save_directory)
+                                reset_trial_model()

                            print(f"Model saved to [bold]{save_directory}[/].")

+                            if reproduction_mode and verify_hashes:
+                                print("Verifying hashes of weight files...")
+
+                                for (
+                                    filename,
+                                    original_sha256,
+                                ) in reproduction_information["hashes"].items():
+                                    file_path = Path(save_directory) / filename
+
+                                    if file_path.exists():
+                                        sha256 = get_file_sha256(file_path)
+
+                                        if sha256.lower() == original_sha256.lower():
+                                            print(
+                                                f"[bold]{filename}:[/] [green]Hash matches[/]"
+                                            )
+                                        else:
+                                            print(
+                                                f"[bold]{filename}:[/] [yellow]Hash doesn't match[/]"
+                                            )
+                                    else:
+                                        print(
+                                            f"[bold]{filename}:[/] [red]File not found[/]"
+                                        )
+
                        case "Upload the model to Hugging Face":
                            # We don't use huggingface_hub.login() because that stores the token on disk,
                            # and since this program will often be run on rented or shared GPU servers,
@@ -823,7 +965,7 @@ def run():
                                continue
                            private = visibility == "Private"

-                            strategy = obtain_merge_strategy(settings)
+                            strategy = obtain_export_strategy(settings, model)
                            if strategy is None:
                                continue

@@ -835,27 +977,50 @@ def run():
                                settings.good_evaluation_prompts.dataset,
                                settings.bad_evaluation_prompts.dataset,
                            ]
-                            can_reproduce = not Path(settings.model).exists() and all(
-                                not Path(d).exists() for d in datasets
+                            is_reproducible = (
+                                is_hf_path(settings.model)
+                                and all(is_hf_path(dataset) for dataset in datasets)
+                                and not reproduction_mode
                            )

-                            if can_reproduce:
-                                # Pin the number of trials to the number of actual completed trials
-                                # for the reproduction configuration.
-                                settings.n_trials = count_completed_trials()
-
-                                include_reproduce = prompt_confirm(
-                                    """Include 'reproduce' folder?
-This saves your exact configuration and system information, along with the study checkpoint, to help others verify your results."""
+                            if is_reproducible:
+                                print(
+                                    (
+                                        "Heretic can add information to the repository that allows others to reproduce the model. "
+                                        "This is optional, but valuable to the community as both a learning tool and to preserve computational work already done. "
+                                        "Guaranteeing reproducibility requires basic system information (Python and OS version, CPU and GPU/accelerator info) "
+                                        "as tensor operations can give different results in different system environments. "
+                                        "[bold]The information does not include any file system paths or other private data.[/]"
+                                    )
                                )
+                                reproducibility_information = prompt_select(
+                                    "Which reproducibility information do you want to add?",
+                                    [
+                                        Choice(
+                                            title="Full: Settings, package versions, and system information",
+                                            value="full",
+                                        ),
+                                        Choice(
+                                            title="Basic: Settings and package versions",
+                                            value="basic",
+                                        ),
+                                        Choice(
+                                            title="Don't add any reproducibility information",
+                                            value="none",
+                                        ),
+                                    ],
+                                )
+                                if reproducibility_information is None:
+                                    continue
                            else:
-                                include_reproduce = False
+                                reproducibility_information = "none"

-                            if strategy == "adapter":
+                            if strategy == ExportStrategy.ADAPTER:
                                print("Uploading LoRA adapter...")
                                model.model.push_to_hub(
                                    repo_id,
                                    private=private,
+                                    max_shard_size=settings.max_shard_size,
                                    token=token,
                                )
                            else:
@@ -864,6 +1029,7 @@ This saves your exact configuration and system information, along with the study
                                merged_model.push_to_hub(
                                    repo_id,
                                    private=private,
+                                    max_shard_size=settings.max_shard_size,
                                    token=token,
                                )
                                del merged_model
@@ -873,23 +1039,26 @@ This saves your exact configuration and system information, along with the study
                                    private=private,
                                    token=token,
                                )
+                                if model.processor is not None:
+                                    model.processor.push_to_hub(
+                                        repo_id,
+                                        private=private,
+                                        token=token,
+                                    )
+                                reset_trial_model()

-                            # If the model path exists locally and includes the
-                            # card, use it directly. If the model path doesn't
-                            # exist locally, it can be assumed to be a model
-                            # hosted on the Hugging Face Hub, in which case
-                            # we can retrieve the model card.
-                            model_path = Path(settings.model)
-                            if model_path.exists():
+                            if is_hf_path(settings.model):
+                                card = ModelCard.load(settings.model)
+                            else:
                                card_path = (
-                                    model_path / huggingface_hub.constants.REPOCARD_NAME
+                                    Path(settings.model)
+                                    / huggingface_hub.constants.REPOCARD_NAME
                                )
                                if card_path.exists():
                                    card = ModelCard.load(card_path)
                                else:
                                    card = None
-                            else:
-                                card = ModelCard.load(settings.model)
+
                            if card is not None:
                                if card.data is None:
                                    card.data = ModelCardData()
@@ -899,30 +1068,91 @@ This saves your exact configuration and system information, along with the study
                                card.data.tags.append("uncensored")
                                card.data.tags.append("decensored")
                                card.data.tags.append("abliterated")
+                                if reproducibility_information != "none":
+                                    card.data.tags.append("reproducible")
                                card.text = (
                                    get_readme_intro(
                                        settings,
                                        trial,
-                                        evaluator.base_refusals,
-                                        evaluator.bad_prompts,
+                                        reproducibility_information != "none",
                                    )
                                    + card.text
                                )
                                card.push_to_hub(repo_id, token=token)

-                            if include_reproduce:
-                                upload_reproduce_folder(
+                            if reproducibility_information != "none":
+                                # Set the number of trials to the number of actual completed trials
+                                # for the reproduction configuration.
+                                settings.n_trials = len(study.trials)
+                                current_export_strategy = settings.export_strategy
+                                settings.export_strategy = strategy
+
+                                try:
+                                    upload_reproduce_folder(
+                                        repo_id,
+                                        settings,
+                                        token,
+                                        checkpoint_path=study_checkpoint_file,
+                                        trial=trial,
+                                        include_system_information=(
+                                            reproducibility_information == "full"
+                                        ),
+                                    )
+                                finally:
+                                    settings.export_strategy = current_export_strategy
+
+                            print(f"Model uploaded to [bold]{repo_id}[/].")
+
+                            if reproduction_mode and verify_hashes:
+                                print("Verifying hashes of weight files...")
+
+                                api = HfApi()
+                                model_info = api.model_info(
                                    repo_id,
-                                    settings,
-                                    token,
-                                    checkpoint_path=study_checkpoint_file,
-                                    trial=trial,
+                                    files_metadata=True,
+                                    token=token,
                                )
-                                print(
-                                    f"Model and reproducibility files uploaded to [bold]{repo_id}[/]."
-                                )
-                            else:
-                                print(f"Model uploaded to [bold]{repo_id}[/].")
+
+                                if not model_info.siblings:
+                                    raise RuntimeError(
+                                        "Could not fetch uploaded model hashes."
+                                    )
+
+                                for (
+                                    filename,
+                                    original_sha256,
+                                ) in reproduction_information["hashes"].items():
+                                    file_found = False
+
+                                    for file in model_info.siblings:
+                                        if file.rfilename == filename:
+                                            sha256 = getattr(file, "lfs", {}).get(
+                                                "sha256"
+                                            )
+                                            if not sha256:
+                                                raise RuntimeError(
+                                                    "Could not fetch uploaded model hashes."
+                                                )
+
+                                            if (
+                                                sha256.lower()
+                                                == original_sha256.lower()
+                                            ):
+                                                print(
+                                                    f"[bold]{filename}:[/] [green]Hash matches[/]"
+                                                )
+                                            else:
+                                                print(
+                                                    f"[bold]{filename}:[/] [yellow]Hash doesn't match[/]"
+                                                )
+
+                                            file_found = True
+                                            break
+
+                                    if not file_found:
+                                        print(
+                                            f"[bold]{filename}:[/] [red]File not found[/]"
+                                        )

                        case "Chat with the model":
                            print()
@@ -1062,7 +1292,11 @@ This saves your exact configuration and system information, along with the study
                                print(table)

                except Exception as error:
-                    print(f"[red]Error: {error}[/]")
+                    formatted = format_exception(error)
+                    if "\n" in formatted:
+                        print(f"[red]Error:\n{formatted}[/]")
+                    else:
+                        print(f"[red]Error: {formatted}[/]")


 def main():
@@ -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 (
@@ -31,7 +33,7 @@ from transformers.generation import (

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


 def get_model_class(
@@ -56,20 +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.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")

@@ -256,7 +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
@@ -291,33 +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,
        )

@@ -360,8 +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]

@@ -379,6 +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]
@@ -395,11 +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(
@@ -524,6 +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.
@@ -720,7 +780,7 @@ class Model:
        _, outputs = self.generate(
            prompts,
            max_new_tokens=1,
-            output_scores=True,
+            output_logits=True,
            return_dict_in_generate=True,
            use_cache=False,
        )
@@ -730,15 +790,16 @@ 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).
        logprobs = F.log_softmax(logits, dim=-1)

-        del outputs
-
        if self.settings.offload_outputs_to_cpu:
+            del outputs, logits
            logprobs = logprobs.cpu()
            empty_cache()

@@ -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
@@ -25,6 +25,7 @@ from accelerate.utils import (

 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.
@@ -48,6 +49,7 @@ def empty_cache():

 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"],
@@ -61,6 +63,7 @@ def get_nvidia_driver_version() -> str | 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(
@@ -101,6 +104,7 @@ def get_amdgpu_driver_version() -> str | None:

 def get_xpu_driver_version() -> str | None:
    """Gets the Intel XPU driver version."""
+
    try:
        output = subprocess.check_output(
            ["xpu-smi", "discovery"],
@@ -117,6 +121,7 @@ def get_xpu_driver_version() -> str | 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"],
@@ -133,6 +138,7 @@ def get_npu_driver_version() -> str | 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"],
@@ -156,6 +162,7 @@ class HereticVersionInfo:

 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.
@@ -171,6 +178,7 @@ def get_heretic_version_info() -> HereticVersionInfo:
    if not direct_url_content:
        # Standard PyPI installation.
        origin_metadata["type"] = "pypi"
+
        return HereticVersionInfo(
            version=base_version,
            origin="PyPI",
@@ -178,51 +186,48 @@ def get_heretic_version_info() -> HereticVersionInfo:
            metadata=origin_metadata,
        )

-    try:
-        data = json.loads(direct_url_content)
+    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")
+    # 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,
-                }
+        if requested_revision:
+            origin_str = (
+                f"Git ({repo_url}@{requested_revision} - commit: {commit_hash})"
            )
+        else:
+            origin_str = f"Git ({repo_url} @ {commit_hash})"

-            return HereticVersionInfo(
-                version=base_version,
-                origin=origin_str,
-                is_standard_pypi=False,
-                metadata=origin_metadata,
-            )
+        origin_metadata.update(
+            {
+                "type": "git",
+                "url": repo_url,
+                "commit_hash": commit_hash,
+                "requested_revision": requested_revision,
+            }
+        )

-        # 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=origin_str,
+            is_standard_pypi=False,
+            metadata=origin_metadata,
+        )

-    except json.JSONDecodeError:
-        pass
+    # 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,
@@ -234,6 +239,7 @@ def get_heretic_version_info() -> HereticVersionInfo:

 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
@@ -320,6 +326,7 @@ def get_accelerator_info_dict() -> dict[str, Any]:

 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:
@@ -350,6 +357,7 @@ def get_accelerator_info(include_warnings: bool = True) -> str:

 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 {
@@ -363,6 +371,7 @@ def get_cpu_info_dict() -> dict[str, str | int | None]:

 def get_cpu_info() -> str:
    """Gets the CPU brand name."""
+
    info = get_cpu_info_dict()
    parts = []
    parts.append(
@@ -397,12 +406,14 @@ def get_python_env_info_dict() -> dict[str, str]:

 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 | None:
+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)
@@ -411,8 +422,12 @@ def get_package_version(name: str) -> str | None:

 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()

@@ -445,18 +460,19 @@ def get_requirements_dict() -> dict[str, str]:
            # 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 name in required_packages:
+
+    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 name == "heretic-llm" and not version_info.is_standard_pypi:
+        if package == "heretic-llm" and not version_info.is_standard_pypi:
            continue

-        version_str = get_package_version(name)
-        if version_str:
-            dependencies[name] = version_str
+        dependencies[package] = get_package_version(package)

    return dependencies
@@ -2,13 +2,16 @@
 # Copyright (C) 2025-2026  Philipp Emanuel Weidmann <pew@worldwidemann.com> + contributors

 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

@@ -21,7 +24,9 @@ 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
@@ -155,18 +160,6 @@ def prompt_password(message: str) -> str:
        return questionary.password(message).ask()


-def prompt_confirm(message: str, default: bool = True) -> bool:
-    if is_notebook():
-        print()
-        choices = "[Y/n]" if default else "[y/N]"
-        result = input(f"{message} {choices} ").strip().lower()
-        if not result:
-            return default
-        return result in ("y", "yes")
-    else:
-        return questionary.confirm(message, default=default).ask()
-
-
 def format_duration(seconds: float) -> str:
    seconds = round(seconds)
    hours, seconds = divmod(seconds, 3600)
@@ -180,12 +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,
@@ -193,25 +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,
@@ -220,11 +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]
@@ -254,7 +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 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"]
@@ -271,21 +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"
-    else:
-        model_link = f"[{settings.model}](https://huggingface.co/{settings.model})"

-    version_info = get_heretic_version_info()
+    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:
+        reproducibility_instructions = ""
+
    return f"""# This is a decensored version of {
        model_link
-    }, made using [Heretic](https://github.com/p-e-w/heretic) v{version_info.version}
-
+    }, made using [Heretic](https://heretic-project.org) v{version("heretic-llm")}
+{reproducibility_instructions}
 ## Abliteration parameters

 | Parameter | Value |
@@ -304,9 +362,9 @@ 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"]} |

 -----

@@ -315,265 +373,312 @@ def get_readme_intro(

 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 = get_requirements_dict()
-    sorted_requirements = sorted(
-        [f"{name}=={version}" for name, version in requirements.items()],
-        key=lambda x: x.lower(),
-    )
-    return "\n".join(sorted_requirements) + "\n"
+
+    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,
-    timestamp: str | None = None,
-    base_model_commit: str | None = None,
+    trial: Trial | FrozenTrial,
+    include_system_information: bool,
 ) -> str:
-    """Generates a README.md for the reproduce/ folder."""
-    torch_version = torch.__version__
-    install_hint = f"pip install torch=={torch_version}"
-    if "+" in torch_version:
-        suffix = torch_version.split("+")[1]
-        if suffix:
-            install_hint += f" --index-url https://download.pytorch.org/whl/{suffix}"
+    """Generates the contents of a README.md for the reproduce/ folder."""

    heterogeneous_warning = ""
-    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 = """
+
+    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 Detected!**
-> This system uses 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.**
+> **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.
 """

-    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].strip(")")
-            origin_warning = f"""
-> [!NOTE]
-> **Git Installation Detected**
-> This system installed `heretic-llm` from source repository: `{repo_info}`.
-> To reproduce these results, you must install Heretic from this exact repository and commit.
-"""
-        elif version_info.origin == "Local":
-            origin_warning = """
-> [!WARNING]
-> **Local Code Detected!**
-> This system installed `heretic-llm` from a local directory or wheel. Uncommitted or experimental code may have been executed. **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:
-            origin_warning = """
-> [!WARNING]
-> **Non-Standard Installation Detected!**
-> This system installed `heretic-llm` from an unknown non-standard source. **Reproducibility ***cannot*** be guaranteed in this environment.**
-"""
+            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}"
+            ]

-    def format_hf_link(
-        name: str, commit: str | None = None, is_dataset: bool = False
-    ) -> str:
-        if Path(name).exists():
-            return f"`{name}` (Local)"
+            if accelerators.get("api_name") and accelerators.get("api_version"):
+                accelerator_lines.append(
+                    f"  - **{accelerators['api_name']}:** {accelerators['api_version']}"
+                )

-        prefix = "datasets/" if is_dataset else ""
-        base_url = f"https://huggingface.co/{prefix}{name}"
-        link = f"[{name}]({base_url})"
-        if commit:
-            commit_url = f"{base_url}/commit/{commit}"
-            link += f" (Commit: [{commit[:7]}]({commit_url}))"
-        return link
+            if accelerators.get("driver_version"):
+                accelerator_lines.append(
+                    f"  - **Driver Version:** {accelerators['driver_version']}"
+                )

-    model_link = format_hf_link(settings.model, base_model_commit)
-    dataset_info = f"""## Dataset Information
+            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)

- **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)}"""
+        system_report = f"""## System

-    timestamp_str = f"- **Run started at (UTC):** `{timestamp}`" if timestamp else ""
-
-    # System and Accelerator info using structured dictionaries.
-    cpu = get_cpu_info_dict()
-    python_env = get_python_env_info_dict()
-    accelerator = get_accelerator_info_dict()
-
-    # Build System Environment section.
-    system_env_lines = [
-        f"- **OS:** `{platform.platform()}` (`{platform.machine()}`)",
-        f"- **CPU:** `{cpu['brand'] or 'Unknown CPU'}`",
-        f"  - **Information:** Family `{cpu['family']}`, Model `{cpu['model']}`, Stepping `{cpu['stepping']}`",
-    ]
-
-    system_env_lines.extend(
-        [
-            f"- **Python:** `{python_env['version']}` (`{python_env['implementation']}`, `{python_env['compiler']}`) [`{python_env['environment']}`]",
-            f"- **Heretic:** `v{version_info.version}`"
-            + (f" (Origin: `{version_info.origin}`)" if version_info.origin else ""),
-            f"- **PyTorch:** `{torch.__version__}`",
-        ]
-    )
-    system_environment_report = "\n".join(system_env_lines)
-
-    # Build Accelerators section.
-    if accelerator["type"] is None:
-        accelerator_report = "> [!WARNING]\n> **No GPU or other accelerator detected.**"
-    else:
-        devices = accelerator["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 ""
-        accelerator_lines = [
-            f"- **{accelerator['type']}:** Detected `{len(devices)}` device(s){vram_suffix}"
-        ]
-
-        if accelerator.get("api_name") and accelerator.get("api_version"):
-            accelerator_lines.append(
-                f"  - **{accelerator['api_name']}:** `{accelerator['api_version']}`"
-            )
-
-        if accelerator.get("driver_version"):
-            accelerator_lines.append(
-                f"  - **Driver Version:** `{accelerator['driver_version']}`"
-            )
-
-        accelerator_lines.append("- **Devices:**")
-        for i, dev in enumerate(devices):
-            vram = f" (`{dev['vram_gb']:.2f} GB`)" if dev.get("vram_gb") else ""
-            accelerator_lines.append(
-                f"  - **{accelerator['type']} {i}:** `{dev['name']}`{vram}"
-            )
-        accelerator_report = "\n".join(accelerator_lines)
-
-    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}
-
-## Model Information
-
- **Base Model:** {model_link}
-{timestamp_str}
-
-{dataset_info}
-
-## Selected Trial
-
- **Trial Number:** `#{trial.user_attrs["index"]}`
- **Refusal Count:** `{trial.user_attrs.get("refusals")}/{trial.user_attrs.get("total_refusal_prompts")}`
- **KL Divergence:** `{trial.user_attrs.get("kl_divergence", 0):.6f}`
-
-## System Environment
-
-{system_environment_report}
+- **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}

-## Contents
+"""
+        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 = ""

- **config.toml**: The exact configuration used, including the seed `{settings.seed}`.
- **requirements.txt**: The exact versions of all installed Python packages.
- **{checkpoint_filename}**: The Optuna study journal containing the history of all trials.
- **reproduce.json**: A machine-readable version of this report.
- **SHA256SUMS**: Cryptographic hashes for all uploaded weight files (if applicable).
+    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.
+"""

-## How to Reproduce
+    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}"
+            )

-1. Ensure your hardware and environment match the specifications in the **System Environment** section above.
-2. Install the exact package versions listed in `requirements.txt`.
-3. Place the provided `config.toml` in your working directory.
-4. Run `heretic` without any additional arguments.
-5. Verify the integrity of the reproduced files by comparing their SHA256 hashes against the manifest in `SHA256SUMS`.
+    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]
-> To use the included Optuna study journal `{checkpoint_filename}`, place it in a `checkpoints/` directory before running `heretic` on the same model.
+> You can automate this process, including all verification steps, by downloading the `reproduce.json` file and running
+> `heretic --reproduce reproduce.json`.

-> [!IMPORTANT]
-> Make sure to install correct PyTorch version from: `{install_hint}`
+{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,
-    timestamp: str | None = None,
-    base_model_commit: str | None = None,
-    uploaded_model_hashes: dict[str, str] | None = None,
+    trial: Trial | FrozenTrial,
+    timestamp: str,
+    uploaded_model_hashes: dict[str, str],
+    include_system_information: bool,
 ) -> str:
-    """Generates a reproduce.json file for the reproduce/ folder."""
+    """Generates the contents of a reproduce.json file for the reproduce/ folder."""
+
    version_info = get_heretic_version_info()
+
    data = {
-        "base_model": {
-            "id": settings.model,
-            "commit_hash": base_model_commit,
-        },
-        "system": {
-            "os": {"platform": platform.platform(), "machine": platform.machine()},
-            "cpu": get_cpu_info_dict(),
-            "python": get_python_env_info_dict(),
+        "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__,
-            "accelerator": get_accelerator_info_dict(),
+            "requirements": get_requirements_dict(),
        },
-        "requirements": get_requirements_dict(),
-        "settings": settings.model_dump(exclude_none=True),
-        "trial": {
-            "direction_index": trial.user_attrs.get("direction_index"),
-            "parameters": trial.user_attrs.get("parameters"),
-            "metrics": {
-                "refusals": trial.user_attrs.get("refusals"),
-                "total_refusal_prompts": trial.user_attrs.get("total_refusal_prompts"),
-                "kl_divergence": trial.user_attrs.get("kl_divergence"),
-            },
+        "settings": settings.model_dump(),
+        "parameters": {
+            "direction_index": trial.user_attrs["direction_index"],
+            "abliteration_parameters": trial.user_attrs["parameters"],
        },
-        "timestamp": timestamp,
-        "uploaded_model_hashes": uploaded_model_hashes or {},
+        "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 a GNU Coreutils compatible SHA256SUMS file content."""
+    """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,
-    uploaded_model_hashes: dict[str, str] | None = None,
-) -> None:
+    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,
@@ -581,50 +686,46 @@ def create_reproduce_folder(
        settings.good_evaluation_prompts,
        settings.bad_evaluation_prompts,
    ]:
-        if not Path(spec.dataset).exists():
-            # Fail if the dataset is missing or unreachable.
-            spec.commit = huggingface_hub.dataset_info(spec.dataset).sha
-
-    # Fetch commit hash for the base model if it's on HF.
-    base_model_commit = None
-    if not Path(settings.model).exists():
-        try:
-            base_model_commit = huggingface_hub.model_info(settings.model).sha
-        except Exception:
-            pass
+        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 / "config.toml").write_text(
-        generate_config_toml(settings), encoding="utf-8"
-    )
    (reproduce_dir / "requirements.txt").write_text(
-        generate_requirements_txt(), encoding="utf-8"
-    )
-    (reproduce_dir / "README.md").write_text(
-        generate_reproduce_readme(
-            settings,
-            checkpoint_filename,
-            trial,
-            timestamp=timestamp,
-            base_model_commit=base_model_commit,
-        ),
+        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"
+            generate_sha256sums(uploaded_model_hashes),
+            encoding="utf-8",
        )
+
    (reproduce_dir / "reproduce.json").write_text(
        generate_reproduce_json(
            settings,
            trial,
            timestamp=timestamp,
-            base_model_commit=base_model_commit,
            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",
    )
@@ -640,23 +741,26 @@ def upload_reproduce_folder(
    settings: Settings,
    token: str,
    checkpoint_path: str | Path,
-    trial: Trial,
-) -> None:
+    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 = {}
-    try:
-        api = huggingface_hub.HfApi()
-        info = api.model_info(repo_id=repo_id, files_metadata=True, token=token)
-        # For weights, we only care about safetensors.
-        weight_extensions = (".safetensors",)
-        if info.siblings is not None:
-            for file in info.siblings:
-                if file.rfilename.endswith(weight_extensions):
-                    sha256 = getattr(file, "lfs", {}).get("sha256")
-                    if sha256:
-                        uploaded_model_hashes[file.rfilename] = sha256
-    except Exception as e:
-        # Fail if integrity checks cannot be completed.
-        raise RuntimeError(f"Could not fetch uploaded model hashes: {e}") from e
+
+    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)
@@ -666,6 +770,7 @@ def upload_reproduce_folder(
            checkpoint_path=checkpoint_path,
            trial=trial,
            uploaded_model_hashes=uploaded_model_hashes,
+            include_system_information=include_system_information,
        )

        reproduce_dir = tmp_path / "reproduce"
@@ -8,7 +8,7 @@ resolution-markers = [
 ]

 [options]
-exclude-newer = "2026-04-14T22:48:57.86057843Z"
+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]]
@@ -931,16 +931,14 @@ wheels = [

 [[package]]
 name = "heretic-llm"
-version = "1.2.0"
+version = "1.4.0"
 source = { editable = "." }
 dependencies = [
    { name = "accelerate" },
    { name = "bitsandbytes" },
    { name = "datasets" },
-    { name = "hf-transfer" },
    { name = "huggingface-hub" },
    { name = "immutabledict" },
-    { name = "kernels" },
    { name = "langdetect" },
    { name = "lm-eval", extra = ["hf"] },
    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
@@ -954,7 +952,7 @@ dependencies = [
    { name = "rich" },
    { name = "tomli-w" },
    { name = "tqdm" },
-    { name = "transformers" },
+    { name = "transformers", extra = ["kernels"] },
 ]

 [package.optional-dependencies]
@@ -979,18 +977,16 @@ requires-dist = [
    { name = "bitsandbytes", specifier = "~=0.49" },
    { name = "datasets", specifier = "~=4.7" },
    { name = "geom-median", marker = "extra == 'research'", specifier = "~=0.1" },
-    { name = "hf-transfer", specifier = "~=0.1" },
    { name = "huggingface-hub", specifier = "~=1.7" },
    { name = "imageio", marker = "extra == 'research'", specifier = "~=2.37" },
    { name = "immutabledict", specifier = "~=4.3" },
-    { 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 = "~=0.18" },
+    { name = "peft", specifier = "~=0.19" },
    { name = "psutil", specifier = "~=7.2" },
    { name = "py-cpuinfo", specifier = "~=9.0" },
    { name = "pydantic-settings", specifier = "~=2.13" },
@@ -999,7 +995,7 @@ 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 = "~=5.3" },
+    { name = "transformers", extras = ["kernels"], specifier = "~=5.6" },
 ]
 provides-extras = ["research"]

@@ -1009,38 +1005,6 @@ dev = [
    { name = "ty", specifier = ">=0.0.5" },
 ]

-[[package]]
-name = "hf-transfer"
-version = "0.1.9"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/1a/eb/8fc64f40388c29ce8ce3b2b180a089d4d6b25b1d0d232d016704cb852104/hf_transfer-0.1.9.tar.gz", hash = "sha256:035572865dab29d17e783fbf1e84cf1cb24f3fcf8f1b17db1cfc7fdf139f02bf", size = 25201, upload-time = "2025-01-07T10:05:12.947Z" }
-wheels = [
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Author	SHA1	Message	Date
Philipp Emanuel Weidmann	6ea3b8d778	build: bump version to 1.4.0	2026-06-14 16:37:45 +05:30
Philipp Emanuel Weidmann	6757ada999	fix: minor cleanups and improvements	2026-06-13 19:48:38 +05:30
Philipp Emanuel Weidmann	2fd163f5e4	feat: automatically reproduce model from reproduce.json (#326 ) * feat: load reproduction information * feat: check reproduction environment against original environment * fix: remove `trust_remote_code` setting This improves security when running Heretic with an untrusted config file. The prompt is now always shown. This is NOT a breaking change, because we currently ignore values for unknown settings, so existing configs continue to work. * feat: reproduce model from JSON file * feat: verify hashes of uploaded weight files * fix: fix issues in automatic reproduction system (#352) * fix: Check if a model is gated / accessible * fix: handle unknown gated models * feat: Auto install requirements * simplify * Revert "simplify" This reverts commit `10287926e9`. * Revert "feat: Auto install requirements" This reverts commit `f4be1abd04`. * fix: Seed pytorch method * reference, style * simplify token * feat: Export strategy in reproduce.json, v2 * style: Name * simplify export strategy * style: Rename * enumeration * maybe remove seed as well * fix: don't lock settings with permanent strategy * simplify no choice, use try/finally block * feat: verify hashes of locally saved weight files * fix: remove obsolete code from merge * docs: add automatic reproduction instructions to reproduce README --------- Co-authored-by: Vinay-Umrethe <vinayumrethe99@gmail.com>	2026-06-11 14:49:28 +05:30
UmranPros	e735203d56	fix: make reset_model null-safe to handle study cancellations (#77 ) (#367 ) * fix: make reset_model null-safe to handle study cancellations (#77) * fix: address bot review, use nested getattr and fallback to settings dtypes * fix: address maintainer review comments in model.py * fix: address maintainer review feedback on reset_model * fix: update Model.dtype type annotation to torch.dtype * chore: revert pyproject.toml and uv.lock changes	2026-06-11 11:05:58 +05:30
UmranPros	ed14dd14ca	fix: improve exception formatting (#146 ) (#363 ) * fix: fall back to exception class name when string representation is empty (#146) * fix: walk stacktrace and causal chain to extract exception details in format_exception * fix: fall back to complete stacktrace when exception has no message, as suggested by maintainer * fix: address maintainer review, push newline control to printing boundaries	2026-06-09 08:27:25 +05:30
UmranPros	1a9d01c002	fix: count all trials, not just completed trials (#357 )	2026-06-07 09:15:14 +05:30
Vinay-Umrethe	c9ce36ddde	style: remove annoying gray bg from logo (#359 )	2026-06-07 08:33:40 +05:30
dependabot[bot]	d68a41fb54	build(deps): bump pyarrow from 22.0.0 to 23.0.1 (#358 ) Bumps [pyarrow](https://github.com/apache/arrow) from 22.0.0 to 23.0.1. - [Release notes](https://github.com/apache/arrow/releases) - [Commits](https://github.com/apache/arrow/compare/apache-arrow-22.0.0...apache-arrow-23.0.1) --- updated-dependencies: - dependency-name: pyarrow dependency-version: 23.0.1 dependency-type: indirect ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	2026-06-06 18:18:04 +05:30
UmranPros	a3dbfd21e6	fix: resolve variable shadowing of error in ValidationError handler (#356 )	2026-06-05 20:16:26 +05:30
zaakir	61c59f7227	feat: save processor for multimodal models (#353 ) * feat: save processor for multimodal models VL models load via AutoModelForImageTextToText, but only the tokenizer was saved/pushed, dropping the processor's image/audio preprocessing config. Save/push it alongside the tokenizer so multimodal models stay complete. * Update src/heretic/model.py Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> * Adjusted processor type to use ProcessorMixin --------- Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2026-06-05 19:41:45 +05:30
MoonRide303	46b5ced274	feat: add support for gemma-4-12B-it (#350 )	2026-06-04 18:20:46 +05:30
Philipp Emanuel Weidmann	c62e10d570	fix: install `kernels` as a Transformers extra Fixes #343	2026-06-04 12:17:35 +05:30
Ashar	906d96f78a	feat: add support for LiquidAI/LFM2.5 models (#344 ) * feat: add support for LiquidAI/LFM2.5 models * add lint supress and obey gemini Signed-off-by: coder3101 <ashar786khan@gmail.com> * ci: format code Signed-off-by: Ashar <ashar786khan@gmail.com> --------- Signed-off-by: coder3101 <ashar786khan@gmail.com> Signed-off-by: Ashar <ashar786khan@gmail.com>	2026-06-03 17:58:05 +05:30
UnstableLlama	b79aa717c6	feat: add config.nohumor.toml (#340 ) * feat: add config.nohumor * Update config.nohumor.toml Following style guide Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> * Update config.nohumor.toml Reduced initial comments --------- Co-authored-by: UnstableLlama <randomnotrealemail@gmail.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2026-05-31 15:26:40 +05:30
Rocker Zhang	db07814a97	build(deps): remove unused hf-transfer dependency (#338 ) hf-transfer is declared in pyproject.toml but never activated: nothing in the codebase sets HF_HUB_ENABLE_HF_TRANSFER, and downloads go through from_pretrained / hf_hub_download with no transfer toggle. huggingface-hub is pinned ~=1.7, where Xet is the default transfer backend, so hf-transfer is dead weight and only surfaces a deprecation warning.	2026-05-31 15:16:31 +05:30
Rocker Zhang	b790094193	feat: support plain text files as prompt datasets (#337 ) A dataset path that points to a plain file is now read as one prompt per line, with empty lines ignored. For text files, "column" is ignored and "split" is optional; when given, it selects a subset of lines using slice notation (e.g. "[:400]"). Detection uses os.path.isfile so files without an extension also work. The split-parsing logic is factored into a shared get_split_slice helper, which derives the split name from the specification, and split/column are now optional in DatasetSpecification, with the dataset branches raising a clear error when either is missing. An invalid split raises instead of being silently ignored. A bare slice does not parse with the pinned datasets version, since ReadInstruction.from_spec expects a named split, so the text branch prepends a synthetic split name. Revives the approach from #103. Closes #98. Co-authored-by: Ric <ricyoung@gmail.com>	2026-05-31 15:06:47 +05:30
kabachuha	6338e2c99b	feat: add "disclaimer" to the prohibited strings list (#334 ) * add "disclaimer" to the prohibited strings list The favorite Gemma's word. * add "disclaimer" to config.py refusal markers	2026-05-28 17:36:30 +05:30
dependabot[bot]	4dcacb5eba	build(deps): bump urllib3 from 2.6.3 to 2.7.0 (#328 ) Bumps [urllib3](https://github.com/urllib3/urllib3) from 2.6.3 to 2.7.0. - [Release notes](https://github.com/urllib3/urllib3/releases) - [Changelog](https://github.com/urllib3/urllib3/blob/main/CHANGES.rst) - [Commits](https://github.com/urllib3/urllib3/compare/2.6.3...2.7.0) --- updated-dependencies: - dependency-name: urllib3 dependency-version: 2.7.0 dependency-type: indirect ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	2026-05-22 15:00:08 +05:30
dependabot[bot]	b8d2c5a7e9	build(deps): bump idna from 3.11 to 3.15 (#327 ) Bumps [idna](https://github.com/kjd/idna) from 3.11 to 3.15. - [Release notes](https://github.com/kjd/idna/releases) - [Changelog](https://github.com/kjd/idna/blob/master/HISTORY.md) - [Commits](https://github.com/kjd/idna/compare/v3.11...v3.15) --- updated-dependencies: - dependency-name: idna dependency-version: '3.15' dependency-type: indirect ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	2026-05-22 14:56:21 +05:30
Philipp Emanuel Weidmann	4e3a3a78a3	docs: update README	2026-05-22 14:51:24 +05:30
iuyua9	551db26bb7	fix: recognize root Hugging Face repo IDs (#325 ) * fix: recognize root Hugging Face repo IDs * fix: propagate invalid HF repo ids * fix: match transformers local path precedence	2026-05-16 09:19:15 +05:30
dependabot[bot]	8b5b85bec9	build(deps): bump mako from 1.3.11 to 1.3.12 (#323 ) Bumps [mako](https://github.com/sqlalchemy/mako) from 1.3.11 to 1.3.12. - [Release notes](https://github.com/sqlalchemy/mako/releases) - [Changelog](https://github.com/sqlalchemy/mako/blob/main/CHANGES) - [Commits](https://github.com/sqlalchemy/mako/commits) --- updated-dependencies: - dependency-name: mako dependency-version: 1.3.12 dependency-type: indirect ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	2026-05-09 15:19:28 +05:30
anrp	1b4851536d	fix: Reset model after saving merged model (#321 ) * fix: Reset model after saving merged model The adapter is lost and writes 0-byte adapters if you save an adapter after saving the merged model. * Revert "Revert "Revert "fix: disable LoRA export for now" (#308)" (#319)" This reverts commit `216c089974`. * Add comment as to why resetting model is needed	2026-05-09 15:16:26 +05:30
Philipp Emanuel Weidmann	b2bdc1f9d6	feat: add functionality for collecting reproduce.json files from Hugging Face	2026-05-07 18:33:50 +05:30
Philipp Emanuel Weidmann	9b7624ddfa	build: bump version to 1.3.0	2026-05-05 18:22:02 +05:30
Philipp Emanuel Weidmann	0e7c14d94a	fix: minor cleanups and improvements	2026-05-04 22:11:14 +05:30
Philipp Emanuel Weidmann	02ce8ad079	chore: update dependencies	2026-05-03 19:25:36 +05:30
Philipp Emanuel Weidmann	79ea9ce905	docs: update README	2026-05-03 09:08:57 +05:30
Philipp Emanuel Weidmann	216c089974	Revert "Revert "fix: disable LoRA export for now" (#308 )" (#319 ) This reverts commit `da92f745de`.	2026-05-03 07:25:00 +05:30
Philipp Emanuel Weidmann	43f8e86a84	fix: minor cleanups and improvements	2026-05-02 06:35:31 +05:30
anrp	da92f745de	Revert "fix: disable LoRA export for now" (#308 ) This reverts commit `025ab3a881`. Co-authored-by: Andrew Patrikalakis <anrp@tri.global>	2026-05-02 06:07:47 +05:30
dependabot[bot]	ebb5e651df	build(deps): bump mako from 1.3.10 to 1.3.11 (#309 ) Bumps [mako](https://github.com/sqlalchemy/mako) from 1.3.10 to 1.3.11. - [Release notes](https://github.com/sqlalchemy/mako/releases) - [Changelog](https://github.com/sqlalchemy/mako/blob/main/CHANGES) - [Commits](https://github.com/sqlalchemy/mako/commits) --- updated-dependencies: - dependency-name: mako dependency-version: 1.3.11 dependency-type: indirect ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	2026-04-25 08:14:38 +05:30
Philipp Emanuel Weidmann	513e3acc72	fix: improve the reproducibility system (#303 ) * fix: various cleanups and improvements for the reproducibility system * fix: save only essential settings * fix: improve model commit handling * feat: make including system information optional * fix: improve formatting of reproducibility README * fix: fix remaining issues	2026-04-23 19:08:18 +05:30