fix: use binary mode for hashes everywhere

* fix: Hash on windows

* trigger ci

* fix: prefer .yaml (used widely than .toml for model configs)

* use removeprefix

* docs: restore commet

* use removeprefix again

* tests: Add windows hash files for all test models

* trigger ci

* fix: minor cleanup

* clean merge mismatch

* remove unnecessary CRLF replace, now that we support more SUMS files

.github/workflows/ci.yml

@@ -40,6 +40,11 @@ jobs:
       - name: Check typing
         run: uv run ty check --output-format=github --error-on-warning .
 
+      - name: Run tests
+        env:
+          PYTHONUNBUFFERED: "1"
+        run: uv run tests/run_tests.py 2>&1
+
       - name: Build package
         run: uv build
 

.gitignore

@@ -15,11 +15,14 @@ wheels/
 # Editors
 /.vscode/
 
-# Configuration files
+# Configuration file (root only, not ignored in test directories)
 /config.toml
 
 # Study checkpoints
-/checkpoints/
+checkpoints/
 
 # Residual plots
-/plots/
+plots/
+
+# Models generated by tests
+/tests/*/model/

README.md

@@ -1,6 +1,6 @@
 <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) [![Codeberg mirror](https://img.shields.io/badge/Codeberg%20mirror-black?logo=codeberg&style=for-the-badge)](https://codeberg.org/p-e-w/heretic)
+# Heretic: Fully automatic censorship removal for language models<br><br>[![Discord](https://img.shields.io/discord/1447831134212984903?color=5865F2&label=discord&labelColor=black&logo=discord&logoColor=white&style=for-the-badge)](https://discord.gg/gdXc48gSyT) [![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)
 
@@ -77,7 +77,7 @@ produced by competing abliteration tools:
 [2](https://old.reddit.com/r/LocalLLaMA/comments/1sy18lx/abliterlitics_benchmarks_and_tensor_comparison/).
 
 The community has created and published
-[well over 3000](https://huggingface.co/models?other=heretic)
+[well over 4000](https://huggingface.co/models?other=heretic)
 models with Heretic.
 
 
@@ -86,7 +86,7 @@ models with Heretic.
 Prepare a Python 3.10+ environment with PyTorch 2.2+ installed as appropriate
 for your hardware. Then run:
 
-```
+```sh
 pip install -U heretic-llm
 heretic Qwen/Qwen3-4B-Instruct-2507
 ```
@@ -134,7 +134,7 @@ provides features designed to support research into the semantics of model inter
 (interpretability). To use those features, you need to install Heretic with the
 optional `research` extra:
 
-```
+```sh
 pip install -U heretic-llm[research]
 ```
 

config.default.toml

@@ -71,6 +71,9 @@ chain_of_thought_skips = [
 # Whether to print prompt/response pairs when counting refusals.
 print_responses = false
 
+# Whether to print additional information that can help with debugging.
+print_debug_information = false
+
 # Whether to print detailed information about residuals and refusal directions.
 print_residual_geometry = false
 
@@ -123,10 +126,6 @@ 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"
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "heretic-llm"
-version = "1.3.0"
+version = "1.4.0"
 description = "Fully automatic censorship removal for language models"
 readme = "README.md"
 license = "AGPL-3.0-or-later"
@@ -38,6 +38,8 @@ dependencies = [
     "questionary~=2.1",
     "rich~=14.3",
     "tomli-w~=1.2",
+    "torch",  # version deliberately unspecified
+    "torchvision",  # version deliberately unspecified
     "tqdm~=4.67",
     "transformers[kernels]~=5.6",
 ]
@@ -58,8 +60,8 @@ dev = [
 ]
 
 [project.urls]
-Homepage = "https://github.com/p-e-w/heretic"
+Homepage = "https://heretic-project.org"
-Documentation = "https://github.com/p-e-w/heretic"
+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"

src/heretic/config.py

@@ -4,7 +4,12 @@
 from enum import Enum
 from typing import Dict
 
-from pydantic import BaseModel, Field
+from pydantic import (
+    BaseModel,
+    Field,
+    NonNegativeInt,
+    PositiveInt,
+)
 from pydantic_settings import (
     BaseSettings,
     CliSettingsSource,
@@ -32,6 +37,11 @@ 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."
@@ -119,6 +129,15 @@ class Settings(BaseSettings):
         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(
         default=[
             # In practice, "auto" almost always means bfloat16.
@@ -167,19 +186,12 @@ class Settings(BaseSettings):
         ),
     )
 
-    trust_remote_code: bool | None = Field(
+    batch_size: NonNegativeInt = Field(
-        default=None,
-        description="Whether to trust remote code when loading the model.",
-        # For security reasons, we don't store this setting.
-        exclude=True,
-    )
-
-    batch_size: int = Field(
         default=0,  # auto
         description="Number of input sequences to process in parallel (0 = auto).",
     )
 
-    max_batch_size: int = Field(
+    max_batch_size: PositiveInt = 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,
@@ -187,7 +199,7 @@ class Settings(BaseSettings):
         exclude=True,
     )
 
-    max_response_length: int = Field(
+    max_response_length: PositiveInt = Field(
         default=100,
         description="Maximum number of tokens to generate for each response.",
     )
@@ -240,6 +252,12 @@ class Settings(BaseSettings):
         exclude=True,
     )
 
+    print_debug_information: bool = Field(
+        default=False,
+        description="Whether to print additional information that can help with debugging.",
+        exclude=True,
+    )
+
     print_residual_geometry: bool = Field(
         default=False,
         description="Whether to print detailed information about residuals and refusal directions.",
@@ -304,7 +322,7 @@ class Settings(BaseSettings):
         ),
     )
 
-    full_normalization_lora_rank: int = Field(
+    full_normalization_lora_rank: PositiveInt = Field(
         default=3,
         description=(
             'The rank of the LoRA adapter to use when "full" row normalization is used. '
@@ -325,12 +343,12 @@ class Settings(BaseSettings):
         ),
     )
 
-    n_trials: int = Field(
+    n_trials: PositiveInt = Field(
         default=200,
         description="Number of abliteration trials to run during optimization.",
     )
 
-    n_startup_trials: int = Field(
+    n_startup_trials: NonNegativeInt = Field(
         default=60,
         description="Number of trials that use random sampling for the purpose of exploration.",
     )
@@ -411,11 +429,63 @@ class Settings(BaseSettings):
         exclude=True,
     )
 
-    max_shard_size: int | str = Field(
+    max_shard_size: PositiveInt | str = Field(
         default="5GB",
         description="Maximum size for individual safetensors files generated when exporting a model.",
     )
 
+    export_strategy: ExportStrategy | None = Field(
+        default=None,
+        description='How to export the model: "merge", "adapter", or unset to prompt the user.',
+    )
+
+    checkpoint_action: str | None = Field(
+        default=None,
+        description='Action to take in case a checkpoint exists: "continue", "restart", or unset to prompt the user.',
+    )
+
+    trial_index: NonNegativeInt | None = Field(
+        default=None,
+        description="Index (in the sorted Pareto front) of the trial to use, or unset to prompt the user.",
+    )
+
+    n_additional_trials: PositiveInt | None = Field(
+        default=None,
+        description="Number of additional trials to run, or unset to prompt the user.",
+    )
+
+    model_action: str | None = Field(
+        default=None,
+        description='Action to take with the decensored model: "save", "upload", or unset to prompt the user.',
+    )
+
+    save_directory: str | None = Field(
+        default=None,
+        description="Directory to save the model to, or unset to prompt the user.",
+        exclude=True,
+    )
+
+    upload_repo_id: str | None = Field(
+        default=None,
+        description="Name of the Hugging Face repository to upload the model to, or unset to prompt the user.",
+        exclude=True,
+    )
+
+    upload_repo_private: bool | None = Field(
+        default=None,
+        description="Whether the Hugging Face repository to upload the model to should be private, or unset to prompt the user.",
+    )
+
+    upload_reproducibility_information: str | None = Field(
+        default=None,
+        description='Which reproducibility information to add to the Hugging Face repository: "full", "basic", "none", or unset to prompt the user.',
+    )
+
+    ignore_mismatches: bool | None = Field(
+        default=None,
+        description="Whether to attempt to reproduce the model even if there are environment mismatches, or unset to prompt the user.",
+    )
+
     refusal_markers: list[str] = Field(
         default=[
             "disclaimer",

src/heretic/main.py

@@ -5,6 +5,14 @@
 
 import sys
 
+# Ensure standard output/error use UTF-8 instead of system default charmap (e.g. cp1252 on Windows).
+for stream in (sys.stdout, sys.stderr):
+    if (
+        hasattr(stream, "reconfigure")
+        and (getattr(stream, "encoding", "") or "").lower() != "utf-8"
+    ):
+        stream.reconfigure(encoding="utf-8")  # type: ignore
+
 from .config import Settings
 
 
@@ -47,7 +55,7 @@ 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
@@ -55,47 +63,54 @@ 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 collect_reproducibles
+from .reproduce import (
+    check_environment,
+    collect_reproducibles,
+    load_reproduction_information,
+)
 from .system import empty_cache, get_accelerator_info
 from .utils import (
+    ask_if_unset,
     format_duration,
     format_exception,
+    get_file_sha256,
     get_readme_intro,
     get_trial_parameters,
     is_hf_path,
     load_prompts,
     print,
     print_memory_usage,
-    prompt_password,
-    prompt_path,
-    prompt_select,
-    prompt_text,
-    set_seed,
     upload_reproduce_folder,
 )
 
 
-def obtain_merge_strategy(settings: Settings, model: Model) -> 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.quantization == QuantizationMethod.BNB_4BIT:
+    if (
+        settings.quantization == QuantizationMethod.BNB_4BIT
+        and settings.export_strategy is None
+    ):
         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.[/]"
@@ -114,7 +129,9 @@ def obtain_merge_strategy(settings: Settings, model: Model) -> str | None:
                     settings.model,
                     device_map="meta",
                     torch_dtype=torch.bfloat16,
-                    trust_remote_code=model.trusted_models.get(settings.model),
+                    trust_remote_code=True
+                    if settings.model in model.trusted_models
+                    else None,
                     **model.revision_kwargs,
                 )
                 footprint_bytes = meta_model.get_memory_footprint()
@@ -131,29 +148,32 @@ def obtain_merge_strategy(settings: Settings, model: Model) -> str | None:
             print(
                 "[yellow]Example: A 27B model requires ~80GB RAM. A 70B model requires ~200GB RAM.[/]"
             )
+
         print()
 
-    strategy = prompt_select(
+    return ask_if_unset(
-        "How do you want to proceed?",
+        settings.export_strategy,
+        questionary.select(
+            "How do you want to export the model?",
             choices=[
                 Choice(
-                title="Merge LoRA into full model"
+                    title="Merge the abliteration LoRA and export the full model"
                     + (
                         ""
                         if settings.quantization == QuantizationMethod.NONE
                         else " (requires sufficient RAM)"
                     ),
-                value="merge",
+                    value=ExportStrategy.MERGE,
                 ),
                 Choice(
-                title="Save LoRA adapter only (can be merged later)",
+                    title="Export the abliteration LoRA only (can be merged later)",
-                value="adapter",
+                    value=ExportStrategy.ADAPTER,
                 ),
             ],
+            style=Style([("highlighted", "reverse")]),
+        ),
     )
 
-    return strategy
-
 
 def run():
     # Enable expandable segments to reduce memory fragmentation on multi-GPU setups.
@@ -165,7 +185,9 @@ 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[/]"
     )
@@ -176,6 +198,7 @@ def run():
         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").
@@ -186,7 +209,9 @@ def run():
 
     # Work around the "model" argument being required
     # when Heretic is invoked in a non-processing mode.
-    if "--collect-reproducibles" in sys.argv and "--model" not in sys.argv:
+    if (
+        "--collect-reproducibles" in sys.argv or "--reproduce" in sys.argv
+    ) and "--model" not in sys.argv:
         sys.argv.extend(["--model", ""])
 
     try:
@@ -196,9 +221,9 @@ def run():
     except ValidationError as error:
         print(f"[red]Configuration contains [bold]{error.error_count()}[/] errors:[/]")
 
-        for error_detail in error.errors():
+        for error_details in error.errors():
             print(
-                f"[bold]{error_detail['loc'][0]}[/]: [yellow]{error_detail['msg']}[/]"
+                f"[bold]{error_details['loc'][0]}[/]: [yellow]{error_details['msg']}[/]"
             )
 
         print()
@@ -211,13 +236,50 @@ def run():
         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(settings, 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)
 
-    set_seed(settings.seed)
+    transformers.set_seed(settings.seed)
 
     print(get_accelerator_info())
 
+    if settings.print_debug_information:
+        print()
+        print(torch.__config__.show().strip())
+        print()
+        print(
+            f"torch.backends.mkldnn.enabled = [bold]{torch.backends.mkldnn.enabled}[/]"
+        )
+        print(f"torch.get_num_threads() = [bold]{torch.get_num_threads()}[/]")
+        print(
+            f"torch.get_num_interop_threads() = [bold]{torch.get_num_interop_threads()}[/]"
+        )
+
     # We don't need gradients as we only do inference.
     torch.set_grad_enabled(False)
 
@@ -260,10 +322,15 @@ 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"]:
+            if settings.checkpoint_action is None:
                 print()
                 print(
                     (
@@ -273,6 +340,7 @@ def run():
                         "This will delete the checkpoint file and all results from the previous run."
                     )
                 )
+
             choices.append(
                 Choice(
                     title="Show the results from the previous run",
@@ -280,6 +348,7 @@ def run():
                 )
             )
         else:
+            if settings.checkpoint_action is None:
                 print()
                 print(
                     (
@@ -289,6 +358,7 @@ def run():
                         "This will delete the checkpoint file and all results from the previous run."
                     )
                 )
+
             choices.append(
                 Choice(
                     title="Continue the previous run",
@@ -310,19 +380,29 @@ def run():
             )
         )
 
+        if settings.checkpoint_action is None:
             print()
-        choice = prompt_select("How would you like to proceed?", choices)
 
-        if choice == "continue":
+        action = ask_if_unset(
+            settings.checkpoint_action,
+            questionary.select(
+                "How would you like to proceed?",
+                choices=choices,
+                style=Style([("highlighted", "reverse")]),
+            ),
+        )
+
+        if action is None or action == "":
+            return
+
+        if action == "continue":
             settings = Settings.model_validate_json(
                 existing_study.user_attrs["settings"]
             )
-        elif choice == "restart":
+        elif action == "restart":
             os.unlink(study_checkpoint_file)
             backend = JournalFileBackend(study_checkpoint_file, lock_obj=lock_obj)
             storage = JournalStorage(backend)
-        elif choice is None or choice == "":
-            return
 
     model = Model(settings)
     print()
@@ -367,9 +447,10 @@ def run():
 
                 formatted = format_exception(error)
                 if "\n" in formatted:
-                    print(f"[red]Failed[/]:\n{formatted}")
+                    print(f"[red]Failed:\n{formatted}[/]")
                 else:
-                    print(f"[red]Failed[/] ({formatted})")
+                    print(f"[red]Failed ({formatted})[/]")
+
                 break
 
             response_lengths = [
@@ -529,10 +610,22 @@ def run():
             # The parameter ranges are based on experiments with various models
             # and much wider ranges. They are not set in stone and might have to be
             # adjusted for future models.
-            max_weight = trial.suggest_float(
+            #
+            # The MLP gets a negative lower bound that is then clamped to 0, so the
+            # optimizer can fully disable its ablation. The clamp puts a positive
+            # probability mass on exactly 0 (the continuous sampler would otherwise
+            # reach 0 with probability zero). Ablating the MLP is often unnecessary for
+            # removing refusals and tends to damage model intelligence more than
+            # ablating the attention output, so on many models the optimum is to leave
+            # it (mostly) untouched. See issue #202.
+            max_weight_lower_bound = -0.25 if component == "mlp.down_proj" else 0.8
+            max_weight = max(
+                0.0,
+                trial.suggest_float(
                     f"{component}.max_weight",
-                0.8,
+                    max_weight_lower_bound,
                     1.5,
+                ),
             )
             max_weight_position = trial.suggest_float(
                 f"{component}.max_weight_position",
@@ -550,7 +643,7 @@ def run():
             min_weight_distance = trial.suggest_float(
                 f"{component}.min_weight_distance",
                 1.0,
-                0.6 * last_layer_index,
+                max(0.6 * last_layer_index, 1.0),
             )
 
             parameters[component] = AbliterationParameters(
@@ -604,6 +697,7 @@ def run():
             trial.study.stop()
             raise TrialPruned()
 
+    if not reproduction_mode:
         study = optuna.create_study(
             sampler=TPESampler(
                 n_startup_trials=settings.n_startup_trials,
@@ -639,11 +733,16 @@ def run():
         if len(study.trials) == settings.n_trials:
             study.set_user_attr("finished", True)
 
-    while True:
+    trial_loop_active = True
+
+    while trial_loop_active:
+        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]
+            completed_trials = [
+                t for t in study.trials if t.state == TrialState.COMPLETE
+            ]
             if not completed_trials:
                 raise KeyboardInterrupt
 
@@ -693,6 +792,8 @@ def run():
 
             print()
             print("[bold green]Optimization finished![/]")
+
+            if settings.trial_index is None:
                 print()
                 print(
                     (
@@ -704,15 +805,55 @@ def run():
                     )
                 )
 
-        while True:
+        while trial_loop_active:
+            # Ensure a predefined trial is only processed once.
+            if settings.trial_index is not None:
+                trial_loop_active = False
+
+            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()
-            trial = prompt_select("Which trial do you want to use?", choices)
+                print("Restoring model from reproduction information...")
+            else:
+                if settings.trial_index is None:
+                    print()
+
+                trial = ask_if_unset(
+                    None
+                    if settings.trial_index is None
+                    else best_trials[settings.trial_index],
+                    questionary.select(
+                        "Which trial do you want to use?",
+                        choices=choices,
+                        style=Style([("highlighted", "reverse")]),
+                    ),
+                )
+
+                if trial is None or trial == "":
+                    return
 
                 if trial == "continue":
                     while True:
                         try:
-                        n_additional_trials = prompt_text(
+                            n_additional_trials = ask_if_unset(
+                                settings.n_additional_trials,
+                                questionary.text(
                                     "How many additional trials do you want to run?"
+                                ),
                             )
                             if n_additional_trials is None or n_additional_trials == "":
                                 n_additional_trials = 0
@@ -727,7 +868,7 @@ def run():
                     if n_additional_trials == 0:
                         continue
 
-                settings.n_trials += n_additional_trials
+                    settings.n_trials = len(study.trials) + n_additional_trials
                     study.set_user_attr("settings", settings.model_dump_json())
                     study.set_user_attr("finished", False)
 
@@ -744,18 +885,19 @@ def run():
 
                     break
 
-            elif trial is None or trial == "":
-                return
-
                 print()
-            print(f"Restoring model from trial [bold]{trial.user_attrs['index']}[/]...")
+                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}[/]")
 
-            # Per https://github.com/huggingface/peft/issues/868#issuecomment-1820642893 once a LoRA is merged it's
+            # Per https://github.com/huggingface/peft/issues/868#issuecomment-1820642893
-            # expected to be empty. Provide a utility function to restore the previous LoRA-ified state.
+            # once a LoRA is merged it's expected to be empty. Provide a utility function
-            def reset_trial_model() -> None:
+            # to restore the previous LoRA-ified state.
+            def reset_trial_model():
                 print("* Resetting model...")
                 model.reset_model()
                 print("* Abliterating...")
@@ -770,20 +912,52 @@ def run():
 
             reset_trial_model()
 
-            while True:
+            action_loop_active = True
+
+            while action_loop_active:
+                # Ensure a predefined action is only executed once.
+                if settings.model_action is not None:
+                    action_loop_active = False
+
+                if settings.model_action is None:
                     print()
-                action = prompt_select(
+
+                action = ask_if_unset(
+                    settings.model_action,
+                    questionary.select(
                         "What do you want to do with the decensored model?",
-                    [
+                        choices=[
-                        "Save the model to a local folder",
+                            Choice(
-                        "Upload the model to Hugging Face",
+                                title="Save the model to a local folder",
-                        "Chat with the model",
+                                value="save",
-                        "Benchmark the model",
+                            ),
-                        "Return to the trial selection menu",
+                            Choice(
+                                title="Upload the model to Hugging Face",
+                                value="upload",
+                            ),
+                            Choice(
+                                title="Chat with the model",
+                                value="chat",
+                            ),
+                            Choice(
+                                title="Benchmark the model",
+                                value="benchmark",
+                            ),
+                            Choice(
+                                title="Exit program"
+                                if reproduction_mode
+                                else "Return to the trial selection menu",
+                                value="",
+                            ),
                         ],
+                        style=Style([("highlighted", "reverse")]),
+                    ),
                 )
 
-                if action is None or action == "Return to the trial selection menu":
+                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,
@@ -791,16 +965,22 @@ def run():
                 # the optimized model.
                 try:
                     match action:
-                        case "Save the model to a local folder":
+                        case "save":
-                            save_directory = prompt_path("Path to the folder:")
+                            save_directory = ask_if_unset(
+                                settings.save_directory,
+                                questionary.path(
+                                    "Path to the folder:",
+                                    only_directories=True,
+                                ),
+                            )
                             if not save_directory:
                                 continue
 
-                            strategy = obtain_merge_strategy(settings, model)
+                            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,
@@ -822,13 +1002,45 @@ def run():
 
                             print(f"Model saved to [bold]{save_directory}[/].")
 
-                        case "Upload the model to Hugging Face":
+                            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":
                             # 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,
                             # it's better to not persist credentials.
                             token = huggingface_hub.get_token()
                             if not token:
-                                token = prompt_password("Hugging Face access token:")
+                                # NOTE: Unlike for most other values obtained from interactive inputs, it is
+                                #       not possible to set the token via the settings. This is a security
+                                #       precaution to prevent exporting the token under all circumstances.
+                                #       For scripting, the correct way to set the token is through the HF_TOKEN
+                                #       environment variable, or through the HF token file.
+                                token = questionary.password(
+                                    "Hugging Face access token:"
+                                ).ask()
                             if not token:
                                 continue
 
@@ -840,23 +1052,38 @@ def run():
                             email = user.get("email", "no email found")
                             print(f"Logged in as [bold]{fullname} ({email})[/]")
 
-                            repo_id = prompt_text(
+                            repo_id = ask_if_unset(
+                                settings.upload_repo_id,
+                                questionary.text(
                                     "Name of repository:",
                                     default=f"{user['name']}/{Path(settings.model).name}-heretic",
+                                ),
                             )
+                            if not repo_id:
+                                continue
 
-                            visibility = prompt_select(
+                            visibility = ask_if_unset(
+                                None
+                                if settings.upload_repo_private is None
+                                else (
+                                    "Private"
+                                    if settings.upload_repo_private
+                                    else "Public"
+                                ),
+                                questionary.select(
                                     "Should the repository be public or private?",
-                                [
+                                    choices=[
                                         "Public",
                                         "Private",
                                     ],
+                                    style=Style([("highlighted", "reverse")]),
+                                ),
                             )
                             if visibility is None:
                                 continue
                             private = visibility == "Private"
 
-                            strategy = obtain_merge_strategy(settings, model)
+                            strategy = obtain_export_strategy(settings, model)
                             if strategy is None:
                                 continue
 
@@ -868,11 +1095,14 @@ def run():
                                 settings.good_evaluation_prompts.dataset,
                                 settings.bad_evaluation_prompts.dataset,
                             ]
-                            is_reproducible = is_hf_path(settings.model) and all(
+                            is_reproducible = (
-                                is_hf_path(dataset) for dataset in datasets
+                                is_hf_path(settings.model)
+                                and all(is_hf_path(dataset) for dataset in datasets)
+                                and not reproduction_mode
                             )
 
                             if is_reproducible:
+                                if settings.upload_reproducibility_information is None:
                                     print(
                                         (
                                             "Heretic can add information to the repository that allows others to reproduce the model. "
@@ -882,9 +1112,12 @@ def run():
                                             "[bold]The information does not include any file system paths or other private data.[/]"
                                         )
                                     )
-                                reproducibility_information = prompt_select(
+
+                                reproducibility_information = ask_if_unset(
+                                    settings.upload_reproducibility_information,
+                                    questionary.select(
                                         "Which reproducibility information do you want to add?",
-                                    [
+                                        choices=[
                                             Choice(
                                                 title="Full: Settings, package versions, and system information",
                                                 value="full",
@@ -898,13 +1131,15 @@ def run():
                                                 value="none",
                                             ),
                                         ],
+                                        style=Style([("highlighted", "reverse")]),
+                                    ),
                                 )
                                 if reproducibility_information is None:
                                     continue
                             else:
                                 reproducibility_information = "none"
 
-                            if strategy == "adapter":
+                            if strategy == ExportStrategy.ADAPTER:
                                 print("Uploading LoRA adapter...")
                                 model.model.push_to_hub(
                                     repo_id,
@@ -973,7 +1208,10 @@ def run():
                                 # 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,
@@ -984,10 +1222,63 @@ def run():
                                             reproducibility_information == "full"
                                         ),
                                     )
+                                finally:
+                                    settings.export_strategy = current_export_strategy
 
                             print(f"Model uploaded to [bold]{repo_id}[/].")
 
-                        case "Chat with the model":
+                            if reproduction_mode and verify_hashes:
+                                print("Verifying hashes of weight files...")
+
+                                api = HfApi()
+                                model_info = api.model_info(
+                                    repo_id,
+                                    files_metadata=True,
+                                    token=token,
+                                )
+
+                                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":
                             print()
                             print(
                                 "[cyan]Press Ctrl+C at any time to return to the menu.[/]"
@@ -999,11 +1290,10 @@ def run():
 
                             while True:
                                 try:
-                                    message = prompt_text(
+                                    message = questionary.text(
                                         "User:",
                                         qmark=">",
-                                        unsafe=True,
+                                    ).unsafe_ask()
-                                    )
                                     if not message:
                                         break
                                     chat.append({"role": "user", "content": message})
@@ -1017,7 +1307,7 @@ def run():
                                     # Ctrl+C/Ctrl+D
                                     break
 
-                        case "Benchmark the model":
+                        case "benchmark":
                             benchmarks = questionary.checkbox(
                                 "Which benchmarks do you want to run?",
                                 [
@@ -1032,16 +1322,17 @@ def run():
                             if not benchmarks:
                                 continue
 
-                            scope = prompt_select(
+                            scope = questionary.select(
                                 (
                                     "Do you want to benchmark the original model along with the decensored model? "
                                     "Benchmarking both models allows you to compare the scores, but it takes twice as much time."
                                 ),
-                                [
+                                choices=[
                                     "Benchmark only the decensored model",
                                     "Benchmark both models",
                                 ],
-                            )
+                                style=Style([("highlighted", "reverse")]),
+                            ).ask()
                             if scope is None:
                                 continue
                             benchmark_original_model = scope == "Benchmark both models"
@@ -1127,7 +1418,7 @@ def run():
                 except Exception as error:
                     formatted = format_exception(error)
                     if "\n" in formatted:
-                        print(f"[red]Error:[/]\n{formatted}")
+                        print(f"[red]Error:\n{formatted}[/]")
                     else:
                         print(f"[red]Error: {formatted}[/]")
 

src/heretic/model.py

@@ -76,7 +76,6 @@ class Model:
 
         self.tokenizer = AutoTokenizer.from_pretrained(
             settings.model,
-            trust_remote_code=settings.trust_remote_code,
             **self.revision_kwargs,
         )
 
@@ -85,7 +84,6 @@ class Model:
         if get_model_class(settings.model) == AutoModelForImageTextToText:
             self.processor = AutoProcessor.from_pretrained(
                 settings.model,
-                trust_remote_code=settings.trust_remote_code,
                 **self.revision_kwargs,
             )
 
@@ -104,10 +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 = set()
-            self.trusted_models[settings.evaluate_model] = settings.trust_remote_code
 
         for dtype in settings.dtypes:
             print(f"* Trying dtype [bold]{dtype}[/]...")
@@ -126,16 +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.
+                # the user must have agreed when prompted to execute remote code,
-                if self.trusted_models.get(settings.model) is None:
+                # because from_pretrained raises an exception otherwise.
-                    self.trusted_models[settings.model] = True
+                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"
@@ -152,11 +151,13 @@ class Model:
             except Exception as error:
                 self.model = None  # ty:ignore[invalid-assignment]
                 empty_cache()
+
                 formatted = format_exception(error)
                 if "\n" in formatted:
-                    print(f"* [red]Failed[/]:\n{formatted}")
+                    print(f"* [red]Failed:\n{formatted}[/]")
                 else:
-                    print(f"* [red]Failed[/] ({formatted})")
+                    print(f"* [red]Failed ({formatted})[/]")
+
                 continue
 
             if settings.quantization == QuantizationMethod.BNB_4BIT:
@@ -283,7 +284,9 @@ 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,
             )
 
@@ -319,6 +322,7 @@ class Model:
         - Slow path: If switching models or after merge_and_unload(),
           performs full model reload with quantization config.
         """
+
         # If a prior model load was interrupted/cancelled mid-process, self.model will be None.
         current_model = None
         if self.model is not None:
@@ -349,7 +353,9 @@ class Model:
             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,
         )
@@ -493,6 +499,12 @@ class Model:
                     params.min_weight - params.max_weight
                 )
 
+                # A weight of 0 disables this component's ablation. reset_model() has
+                # already left the adapter at identity, so abort before the otherwise
+                # wasteful decomposition (which would also be operating on a zero matrix).
+                if weight == 0:
+                    continue
+
                 if refusal_direction is None:
                     # The index must be shifted by 1 because the first element
                     # of refusal_directions is the direction for the embeddings.
@@ -574,7 +586,16 @@ 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)
+                        # "It's safe to call this function if CUDA is not available;
+                        # in that case, it is silently ignored."
+                        torch.cuda.manual_seed_all(self.settings.seed)  # ty:ignore[invalid-argument-type]
                         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.
                         U = U[:, :r]
@@ -779,8 +800,10 @@ class Model:
         # of model.generate with return_dict_in_generate=True.
         outputs = cast(GenerateDecoderOnlyOutput, outputs)
 
+        # Logits for the first (only) generated token.
         # 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).

src/heretic/reproduce.py

@@ -1,13 +1,35 @@
 # 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 questionary
+import torch
 from huggingface_hub import HfApi, hf_hub_download
-from huggingface_hub.utils import disable_progress_bars, enable_progress_bars
+from huggingface_hub.utils import (
+    GatedRepoError,
+    disable_progress_bars,
+    enable_progress_bars,
+)
+from questionary import Choice, Style
+from rich.table import Table
 
-from .utils import print
+from .config import Settings
+from .system import (
+    get_accelerator_info_dict,
+    get_heretic_version_info,
+    get_requirements_dict,
+)
+from .utils import ask_if_unset, print
 
 
 def collect_reproducibles(path: str):
@@ -21,6 +43,7 @@ def collect_reproducibles(path: str):
     models = api.list_models(
         filter=["heretic", "reproducible"],
         sort="created_at",
+        expand=["gated", "tags"],
     )
 
     found = 0
@@ -35,6 +58,12 @@ def collect_reproducibles(path: str):
             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("/")
@@ -81,3 +110,282 @@ def collect_reproducibles(path: str):
     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(
+    settings: Settings,
+    reproduction_information: dict[str, Any],
+) -> bool | None:
+    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."
+            )
+        )
+
+        if settings.ignore_mismatches is None:
+            print()
+
+        return ask_if_unset(
+            settings.ignore_mismatches,
+            questionary.select(
+                "How would you like to proceed?",
+                choices=[
+                    Choice(
+                        title="Attempt to reproduce the model anyway",
+                        value=True,
+                    ),
+                    Choice(
+                        title="Exit program",
+                        value=False,
+                    ),
+                ],
+                style=Style([("highlighted", "reverse")]),
+            ),
+        )
+    else:
+        # There are no mismatches at all, so there is nothing to confirm.
+        return True

src/heretic/utils.py

@@ -1,22 +1,19 @@
 # SPDX-License-Identifier: AGPL-3.0-or-later
 # 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
+from typing import TypeVar
 
 import huggingface_hub
-import numpy as np
-import questionary
 import tomli_w
 import torch
 from datasets import DatasetDict, ReadInstruction, load_dataset, load_from_disk
@@ -25,8 +22,9 @@ 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 questionary import Question
 from rich.console import Console
 
 from .config import DatasetSpecification, Settings
@@ -39,6 +37,9 @@ from .system import (
     is_xpu_available,
 )
 
+T = TypeVar("T")
+
+
 print = Console(highlight=False).print
 
 
@@ -65,99 +66,6 @@ def print_memory_usage():
         p("Driver (reserved) MPS memory", torch.mps.driver_allocated_memory())
 
 
-def is_notebook() -> bool:
-    # Check for specific environment variables (Colab, Kaggle).
-    # This is necessary because when running as a subprocess (e.g. !heretic),
-    # get_ipython() might not be available or might not reflect the notebook environment.
-    if os.getenv("COLAB_GPU") or os.getenv("KAGGLE_KERNEL_RUN_TYPE"):
-        return True
-
-    # Check IPython shell type (for library usage).
-    try:
-        from IPython import get_ipython  # ty:ignore[unresolved-import]
-
-        shell = get_ipython()
-        if shell is None:
-            return False
-
-        shell_name = shell.__class__.__name__
-        if shell_name in ["ZMQInteractiveShell", "Shell"]:
-            return True
-
-        if "google.colab" in str(shell.__class__):
-            return True
-
-        return False
-    except (ImportError, NameError, AttributeError):
-        return False
-
-
-def prompt_select(message: str, choices: list[Any]) -> Any:
-    if is_notebook():
-        print()
-        print(message)
-        real_choices = []
-
-        for i, choice in enumerate(choices, 1):
-            if isinstance(choice, Choice):
-                print(f"[{i}] {choice.title}")
-                real_choices.append(choice.value)
-            else:
-                print(f"[{i}] {choice}")
-                real_choices.append(choice)
-
-        while True:
-            try:
-                selection = input("Enter number: ")
-                index = int(selection) - 1
-                if 0 <= index < len(real_choices):
-                    return real_choices[index]
-                print(
-                    f"[red]Please enter a number between 1 and {len(real_choices)}[/]"
-                )
-            except ValueError:
-                print("[red]Invalid input. Please enter a number.[/]")
-    else:
-        return questionary.select(
-            message,
-            choices=choices,
-            style=Style([("highlighted", "reverse")]),
-        ).ask()
-
-
-def prompt_text(
-    message: str,
-    default: str = "",
-    qmark: str = "?",
-    unsafe: bool = False,
-) -> str:
-    if is_notebook():
-        print()
-        result = input(f"{message} [{default}]: " if default else f"{message}: ")
-        return result if result else default
-    else:
-        question = questionary.text(message, default=default, qmark=qmark)
-        if unsafe:
-            return question.unsafe_ask()
-        else:
-            return question.ask()
-
-
-def prompt_path(message: str) -> str:
-    if is_notebook():
-        return prompt_text(message)
-    else:
-        return questionary.path(message, only_directories=True).ask()
-
-
-def prompt_password(message: str) -> str:
-    if is_notebook():
-        print()
-        return getpass.getpass(message)
-    else:
-        return questionary.password(message).ask()
-
-
 def format_duration(seconds: float) -> str:
     seconds = round(seconds)
     hours, seconds = divmod(seconds, 3600)
@@ -171,10 +79,33 @@ 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 ask_if_unset(value: T, question: Question, unsafe: bool = False) -> T:
+    if value is None:
+        if unsafe:
+            return question.unsafe_ask()
+        else:
+            return question.ask()
+    else:
+        return value
+
+
 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,
+    # 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
@@ -194,12 +125,15 @@ def get_split_slice(split_str: str, length: int) -> tuple[int, int]:
 
     # 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
 
 
@@ -279,14 +213,11 @@ def load_prompts(
     ]
 
 
-T = TypeVar("T")
-
-
 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"]
@@ -303,7 +234,7 @@ def get_trial_parameters(trial: Trial) -> dict[str, str]:
 
 def get_readme_intro(
     settings: Settings,
-    trial: Trial,
+    trial: Trial | FrozenTrial,
     contains_reproducibility_information: bool,
 ) -> str:
     if is_hf_path(settings.model):
@@ -324,7 +255,7 @@ def get_readme_intro(
 
     return f"""# This is a decensored version of {
         model_link
-    }, made using [Heretic](https://github.com/p-e-w/heretic) v{version("heretic-llm")}
+    }, made using [Heretic](https://heretic-project.org) v{version("heretic-llm")}
 {reproducibility_instructions}
 ## Abliteration parameters
 
@@ -368,14 +299,6 @@ def generate_requirements_txt() -> str:
     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,
@@ -395,7 +318,7 @@ def format_hf_link(
 def generate_reproduce_readme(
     settings: Settings,
     checkpoint_filename: str,
-    trial: Trial,
+    trial: Trial | FrozenTrial,
     include_system_information: bool,
 ) -> str:
     """Generates the contents of a README.md for the reproduce/ folder."""
@@ -547,13 +470,18 @@ This directory contains the necessary information and assets to reproduce the re
 
 ## How to reproduce
 
+> [!TIP]
+> You can automate this process, including all verification steps, by downloading the `reproduce.json` file and running
+> `heretic --reproduce reproduce.json`.
+
 {system_instructions}1. Install the exact version of Heretic indicated in the **Environment** section above, from its original source.
 1. Install the packages listed in `requirements.txt`: `pip install -r requirements.txt`
 1. Install the correct version of PyTorch: `{pytorch_install_command}`
 1. Place the provided `config.toml` in your working directory.
 1. Run Heretic without any additional arguments: `heretic`
 1. Wait for the run to finish, then select trial **{trial.user_attrs["index"]}** and export the model.
-1. Verify that the weight files have been exactly reproduced by comparing their SHA-256 hashes against those in `SHA256SUMS`: `sha256sum -c SHA256SUMS` (or look at the hashes online if you uploaded to Hugging Face)
+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.
@@ -564,7 +492,7 @@ This directory contains the necessary information and assets to reproduce the re
 
 def generate_reproduce_json(
     settings: Settings,
-    trial: Trial,
+    trial: Trial | FrozenTrial,
     timestamp: str,
     uploaded_model_hashes: dict[str, str],
     include_system_information: bool,
@@ -574,7 +502,7 @@ def generate_reproduce_json(
     version_info = get_heretic_version_info()
 
     data = {
-        "version": "1",  # Version number of the reproduce.json file format, to allow for future changes.
+        "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": {
@@ -628,11 +556,23 @@ def generate_sha256sums(hashes: dict[str, str]) -> str:
     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,
+    trial: Trial | FrozenTrial,
     uploaded_model_hashes: dict[str, str],
     include_system_information: bool,
 ):
@@ -706,7 +646,7 @@ def upload_reproduce_folder(
     settings: Settings,
     token: str,
     checkpoint_path: str | Path,
-    trial: Trial,
+    trial: Trial | FrozenTrial,
     include_system_information: bool,
 ):
     api = huggingface_hub.HfApi()
@@ -747,16 +687,3 @@ def upload_reproduce_folder(
                     repo_id=repo_id,
                     token=token,
                 )
-
-
-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()

tests/README.md

@@ -0,0 +1,17 @@
+Run the tests with
+
+```sh
+uv run run_tests.py
+```
+
+To update the hashes after a logic change, run the tests, then execute
+
+```sh
+cd TEST_DIR/model
+sha256sum -b * > ../SHA256SUMS.LABEL
+```
+
+where `LABEL` describes the type of system you are running the tests on.
+Since PyTorch does not guarantee exact cross-system reproducibility regardless of configuration,
+multiple valid hashes can be provided for each output file. The above update must be performed
+for each `TEST_DIR` and on each type of system.

tests/gemma-4e/SHA256SUMS.ci

@@ -0,0 +1,7 @@
+2f1b4d75d067bae3fe44e676721c7f077d243bc007156cb9c2f8b5836613d082 *chat_template.jinja
+ca80080dfa4ec6ba87152fa2b9afe70b90c400e5c4b1d6bdc3aa3114467ca68f *config.json
+70070bac883cf9c39b5992450d6b23cd160eaf33099e24c654e0359d2f87c760 *generation_config.json
+f3f4ec19504f182486459cf4e255ece265c25f827840d63b6a9d4058b8e4877a *model.safetensors
+32bdf45d2ad4cc29a0822ddd157a182de76644f0419a6228d151495256e9813c *processor_config.json
+cc8d3a0ce36466ccc1278bf987df5f71db1719b9ca6b4118264f45cb627bfe0f *tokenizer.json
+a1bab8c81ed15fa6ce912ec993c66cb49392e0487fb1ea5f5f11ea3618683627 *tokenizer_config.json

tests/gemma-4e/SHA256SUMS.ci2

@@ -0,0 +1,7 @@
+2f1b4d75d067bae3fe44e676721c7f077d243bc007156cb9c2f8b5836613d082 *chat_template.jinja
+ca80080dfa4ec6ba87152fa2b9afe70b90c400e5c4b1d6bdc3aa3114467ca68f *config.json
+70070bac883cf9c39b5992450d6b23cd160eaf33099e24c654e0359d2f87c760 *generation_config.json
+53c4ee891dce23c0ac85bebc2c4d48301469750fafbb3e6e024c15786d94db8b *model.safetensors
+32bdf45d2ad4cc29a0822ddd157a182de76644f0419a6228d151495256e9813c *processor_config.json
+cc8d3a0ce36466ccc1278bf987df5f71db1719b9ca6b4118264f45cb627bfe0f *tokenizer.json
+a1bab8c81ed15fa6ce912ec993c66cb49392e0487fb1ea5f5f11ea3618683627 *tokenizer_config.json

tests/gemma-4e/SHA256SUMS.linux

@@ -0,0 +1,7 @@
+2f1b4d75d067bae3fe44e676721c7f077d243bc007156cb9c2f8b5836613d082 *chat_template.jinja
+ca80080dfa4ec6ba87152fa2b9afe70b90c400e5c4b1d6bdc3aa3114467ca68f *config.json
+70070bac883cf9c39b5992450d6b23cd160eaf33099e24c654e0359d2f87c760 *generation_config.json
+effe36925f85ecb1e29bba84501a456bb49df21e4047be8b7ea3f6f88181fb65 *model.safetensors
+32bdf45d2ad4cc29a0822ddd157a182de76644f0419a6228d151495256e9813c *processor_config.json
+cc8d3a0ce36466ccc1278bf987df5f71db1719b9ca6b4118264f45cb627bfe0f *tokenizer.json
+a1bab8c81ed15fa6ce912ec993c66cb49392e0487fb1ea5f5f11ea3618683627 *tokenizer_config.json

tests/gemma-4e/SHA256SUMS.windows

@@ -0,0 +1,7 @@
+b16d3228a775c549ba97af41233a54e9de8dd2b65250f78346661d18b936a8b5 *chat_template.jinja
+0094ad598a8043f84d82ad5c886547bca1d1d7f302d82f1491f83d388e89acd4 *config.json
+1a019c5d688d54cf01318eab88cb4345dfa52135eb1d83c2f54125469eb88d5c *generation_config.json
+effe36925f85ecb1e29bba84501a456bb49df21e4047be8b7ea3f6f88181fb65 *model.safetensors
+24d00232e58cfa179fe8b3911c788d4aad9a6279d778ebe4c72e82623b6197f9 *processor_config.json
+cc8d3a0ce36466ccc1278bf987df5f71db1719b9ca6b4118264f45cb627bfe0f *tokenizer.json
+8044bbbddaee8dc47e6b5660e013ba92224d4a5392b2939c59699aa0105f5c8b *tokenizer_config.json

tests/gemma-4e/config.toml

@@ -0,0 +1,41 @@
+model = "tiny-random/gemma-4e"
+model_commit = "3a207ada2c2cd95e9671942e84cf47ea58f0f6af"
+
+seed = 12345
+print_debug_information = true
+
+batch_size = 2
+max_response_length = 10
+kl_divergence_target = 0
+n_trials = 2
+n_startup_trials = 1
+
+export_strategy = "merge"
+checkpoint_action = "restart"
+trial_index = 0
+model_action = "save"
+save_directory = "model"
+
+[good_prompts]
+dataset = "mlabonne/harmless_alpaca"
+commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
+split = "train[:5]"
+column = "text"
+
+[bad_prompts]
+dataset = "mlabonne/harmful_behaviors"
+commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
+split = "train[:5]"
+column = "text"
+
+[good_evaluation_prompts]
+dataset = "mlabonne/harmless_alpaca"
+commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
+split = "test[:5]"
+column = "text"
+
+[bad_evaluation_prompts]
+dataset = "mlabonne/harmful_behaviors"
+commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
+split = "test[:5]"
+column = "text"

tests/mistral-3/SHA256SUMS.ci

@@ -0,0 +1,7 @@
+39f03c383413f531fd302c06c7e982ad98c83f0657a8339ae25478ccb81fdcda *chat_template.jinja
+f69f84977a47c8fea9ce9fc26b7de379216cb01146ea726a87996d3554cfcd19 *config.json
+34dfa6012ca9ac5f57e5521d8dbaecbc7ab7f7ab0fd96ec020b543aab5f265d9 *generation_config.json
+876c6691eb85e3e5e11771e589529830fb454ab26344e1271ae550661e312b50 *model.safetensors
+84be30b124b50749c56d25fdbec5ccedf564446f6b3b035e88e1e07b986d2491 *processor_config.json
+c3a8d92e371b92a2cd6e678e31ebc27d0235e929a51fbf290f74742b341fa96f *tokenizer.json
+7b29c843c0043622d28fd4638451cbb0a609d99a0762ffbff3b92b4b2fee4d94 *tokenizer_config.json

tests/mistral-3/SHA256SUMS.ci2

@@ -0,0 +1,7 @@
+39f03c383413f531fd302c06c7e982ad98c83f0657a8339ae25478ccb81fdcda *chat_template.jinja
+f69f84977a47c8fea9ce9fc26b7de379216cb01146ea726a87996d3554cfcd19 *config.json
+34dfa6012ca9ac5f57e5521d8dbaecbc7ab7f7ab0fd96ec020b543aab5f265d9 *generation_config.json
+6febb813086f253e5ec0fcda02fdfc849c551a7dba54681b37ac5bc402e4eed6 *model.safetensors
+84be30b124b50749c56d25fdbec5ccedf564446f6b3b035e88e1e07b986d2491 *processor_config.json
+c3a8d92e371b92a2cd6e678e31ebc27d0235e929a51fbf290f74742b341fa96f *tokenizer.json
+7b29c843c0043622d28fd4638451cbb0a609d99a0762ffbff3b92b4b2fee4d94 *tokenizer_config.json

tests/mistral-3/SHA256SUMS.linux

@@ -0,0 +1,7 @@
+39f03c383413f531fd302c06c7e982ad98c83f0657a8339ae25478ccb81fdcda *chat_template.jinja
+f69f84977a47c8fea9ce9fc26b7de379216cb01146ea726a87996d3554cfcd19 *config.json
+34dfa6012ca9ac5f57e5521d8dbaecbc7ab7f7ab0fd96ec020b543aab5f265d9 *generation_config.json
+29aff97d5633dead9e1ccd29a2cc153b4b7431d22f63c8d6cf60bc6547681cc9 *model.safetensors
+84be30b124b50749c56d25fdbec5ccedf564446f6b3b035e88e1e07b986d2491 *processor_config.json
+c3a8d92e371b92a2cd6e678e31ebc27d0235e929a51fbf290f74742b341fa96f *tokenizer.json
+7b29c843c0043622d28fd4638451cbb0a609d99a0762ffbff3b92b4b2fee4d94 *tokenizer_config.json

tests/mistral-3/SHA256SUMS.windows

@@ -0,0 +1,7 @@
+72f84af4ea36b82409c35e31b584361534305ef7c0d90fce20d0dc38a7efead8 *chat_template.jinja
+e4c5278b361c57621253c27a2c3db358e1580aec8a14be8e19d4420a224137cf *config.json
+8dde85c000ae807be907421465826c7c63a39f6acf6d04a5a84efaf116ed4ef7 *generation_config.json
+29aff97d5633dead9e1ccd29a2cc153b4b7431d22f63c8d6cf60bc6547681cc9 *model.safetensors
+20e7a6dcde0a6f60ea3b4fb08f6f7afa62532dda93a3111e28384ba5150575f9 *processor_config.json
+c3a8d92e371b92a2cd6e678e31ebc27d0235e929a51fbf290f74742b341fa96f *tokenizer.json
+60a8042e29b4b20e884e48375aa1b9ac0025547371d50e60f6d55e6a9675e868 *tokenizer_config.json

tests/mistral-3/config.toml

@@ -0,0 +1,41 @@
+model = "tiny-random/mistral-3"
+model_commit = "931aa2e5c9668fc3679e56aa44972fe18597d55d"
+
+seed = 12345
+print_debug_information = true
+
+batch_size = 2
+max_response_length = 10
+kl_divergence_target = 0
+n_trials = 2
+n_startup_trials = 1
+
+export_strategy = "merge"
+checkpoint_action = "restart"
+trial_index = 0
+model_action = "save"
+save_directory = "model"
+
+[good_prompts]
+dataset = "mlabonne/harmless_alpaca"
+commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
+split = "train[:5]"
+column = "text"
+
+[bad_prompts]
+dataset = "mlabonne/harmful_behaviors"
+commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
+split = "train[:5]"
+column = "text"
+
+[good_evaluation_prompts]
+dataset = "mlabonne/harmless_alpaca"
+commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
+split = "test[:5]"
+column = "text"
+
+[bad_evaluation_prompts]
+dataset = "mlabonne/harmful_behaviors"
+commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
+split = "test[:5]"
+column = "text"

tests/qwen3.5-moe/SHA256SUMS.ci

@@ -0,0 +1,7 @@
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+4625d1d64d41d1fa9dae7af4ba1e1d7e65a194073d4efa58acb266a916eaaa74 *generation_config.json
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+01562eddd6f9e9ec4bc31656a3b7055284cafbf889acc6c4348dca431ae31f68 *processor_config.json
+87a7830d63fcf43bf241c3c5242e96e62dd3fdc29224ca26fed8ea333db72de4 *tokenizer.json
+2e31d1126e81bddf8d15c3f95260fb487b48c5131b24fcbb5bb9d2537e7afac0 *tokenizer_config.json

tests/qwen3.5-moe/SHA256SUMS.linux

@@ -0,0 +1,7 @@
+a4aee8afcf2e0711942cf848899be66016f8d14a889ff9ede07bca099c28f715 *chat_template.jinja
+749b56d1b1e08081981169db6f2c44ab0be4fd6ebb452d15baafa5e09c21586a *config.json
+4625d1d64d41d1fa9dae7af4ba1e1d7e65a194073d4efa58acb266a916eaaa74 *generation_config.json
+5e0fb0ac724cf079b693fc76a515e60bc16de72c32b36c107b9f078061c4f2ef *model.safetensors
+01562eddd6f9e9ec4bc31656a3b7055284cafbf889acc6c4348dca431ae31f68 *processor_config.json
+87a7830d63fcf43bf241c3c5242e96e62dd3fdc29224ca26fed8ea333db72de4 *tokenizer.json
+2e31d1126e81bddf8d15c3f95260fb487b48c5131b24fcbb5bb9d2537e7afac0 *tokenizer_config.json

tests/qwen3.5-moe/SHA256SUMS.windows

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tests/qwen3.5-moe/config.toml

@@ -0,0 +1,41 @@
+model = "tiny-random/qwen3.5-moe"
+model_commit = "2ebfa8d9717238c5dda927008104fa172a149050"
+
+seed = 12345
+print_debug_information = true
+
+batch_size = 2
+max_response_length = 10
+kl_divergence_target = 0
+n_trials = 2
+n_startup_trials = 1
+
+export_strategy = "merge"
+checkpoint_action = "restart"
+trial_index = 0
+model_action = "save"
+save_directory = "model"
+
+[good_prompts]
+dataset = "mlabonne/harmless_alpaca"
+commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
+split = "train[:5]"
+column = "text"
+
+[bad_prompts]
+dataset = "mlabonne/harmful_behaviors"
+commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
+split = "train[:5]"
+column = "text"
+
+[good_evaluation_prompts]
+dataset = "mlabonne/harmless_alpaca"
+commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
+split = "test[:5]"
+column = "text"
+
+[bad_evaluation_prompts]
+dataset = "mlabonne/harmful_behaviors"
+commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
+split = "test[:5]"
+column = "text"

tests/run_tests.py

@@ -0,0 +1,87 @@
+# SPDX-License-Identifier: AGPL-3.0-or-later
+# Copyright (C) 2025-2026  Philipp Emanuel Weidmann <pew@worldwidemann.com> + contributors
+
+import hashlib
+import subprocess
+import sys
+from pathlib import Path
+
+
+# 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()
+
+
+script_directory = Path(__file__).resolve().parent
+
+project_directory = script_directory.parent
+
+tests_failed = False
+
+for test_directory in script_directory.iterdir():
+    if test_directory.is_dir():
+        config_file = test_directory / "config.toml"
+        hash_files = list(test_directory.glob("SHA256SUMS.*"))
+
+        if config_file.is_file() and hash_files:
+            print("#" * 50)
+            print(f"Running test {test_directory.name}")
+            print("#" * 50)
+            print()
+
+            subprocess.run(
+                [
+                    "uv",
+                    "run",
+                    "--project",
+                    project_directory,
+                    "--directory",
+                    test_directory,
+                    "heretic",
+                ],
+                check=True,
+            )
+
+            print()
+
+            valid_hashes: dict[str, list[str]] = {}
+
+            for hash_file in hash_files:
+                with open(hash_file, "r", encoding="utf-8") as file:
+                    for line in file:
+                        if line.strip():
+                            sha256, filename = line.split()
+                            filename = filename.removeprefix("*")
+
+                            if filename not in valid_hashes:
+                                valid_hashes[filename] = []
+
+                            valid_hashes[filename].append(sha256.lower())
+
+            for filename in valid_hashes:
+                sha256 = get_file_sha256(test_directory / "model" / filename)
+
+                if sha256.lower() not in valid_hashes[filename]:
+                    print(
+                        (
+                            f"Test {test_directory.name} has FAILED!\n"
+                            f"Output file {filename} doesn't match any valid hash.\n\n"
+                            f"Valid hashes:\n"
+                            f"{chr(10).join(valid_hashes[filename])}\n\n"
+                            f"Actual hash:\n"
+                            f"{sha256}\n"
+                        )
+                    )
+                    tests_failed = True
+
+if tests_failed:
+    sys.exit("Tests failed.")
+else:
+    print("All tests passed.")

uv.lock

@@ -50,7 +50,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.13.4"
+version = "3.14.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -60,112 +60,129 @@ dependencies = [
     { name = "frozenlist" },
     { name = "multidict" },
     { name = "propcache" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
     { name = "yarl" },
 ]
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 source = { editable = "." }
 dependencies = [
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@@ -951,6 +968,8 @@ dependencies = [
     { name = "questionary" },
     { name = "rich" },
     { name = "tomli-w" },
+    { name = "torch" },
+    { name = "torchvision" },
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     { name = "scikit-learn", marker = "extra == 'research'", specifier = "~=1.7" },
     { name = "tomli-w", specifier = "~=1.2" },
+    { name = "torch" },
+    { name = "torchvision" },
     { name = "tqdm", specifier = "~=4.67" },
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