fix: fix remaining issues

.github/workflows/ci.yml

@@ -40,11 +40,6 @@ 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

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

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" />
+<img width="128" height="128" align="right" alt="Logo" src="https://github.com/user-attachments/assets/df5f2840-2f92-4991-aa57-252747d7182e" />
 
-# Heretic: Fully automatic censorship removal for language models<br><br>[![Discord](https://img.shields.io/discord/1447831134212984903?color=5865F2&label=discord&labelColor=black&logo=discord&logoColor=white&style=for-the-badge)](https://discord.gg/gdXc48gSyT) [![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)
+# Heretic: Fully automatic censorship removal for language models<br><br>[![Discord](https://img.shields.io/discord/1447831134212984903?color=5865F2&label=discord&labelColor=black&logo=discord&logoColor=white&style=for-the-badge)](https://discord.gg/gdXc48gSyT) [![Follow us on Hugging Face](https://huggingface.co/datasets/huggingface/badges/resolve/main/follow-us-on-hf-md-dark.svg)](https://huggingface.co/heretic-org)
 
 [![#1 Repository of the Day](https://trendshift.io/api/badge/repositories/20538)](https://trendshift.io/repositories/20538)
 
@@ -20,11 +20,6 @@ as possible. Using Heretic does not require an understanding of transformer
 internals. In fact, anyone who knows how to run a command-line program
 can use Heretic to decensor language models.
 
-Heretic supports most dense models, including many multimodal models,
-several different MoE architectures, and even some hybrid models like Qwen3.5.
-Pure state-space models and certain other research architectures are not yet
-supported out of the box.
-
 <img width="650" height="715" alt="Screenshot" src="https://github.com/user-attachments/assets/d71a5efa-d6be-4705-a817-63332afb2d15" />
 
 &nbsp;
@@ -70,15 +65,15 @@ Heretic have been well-received by users (links and emphasis added):
 > Has been the best unquantized abliterated model that I have been able to run on 16gb vram."
 > [*(Link to comment)*](https://old.reddit.com/r/LocalLLaMA/comments/1phjxca/im_calling_these_people_out_right_now/nt06tji/)
 
-Heretic models have also been independently benchmarked using standard metrics
-like MMLU and GSM8K, and have been found to compare favorably with models
-produced by competing abliteration tools:
-[1](https://old.reddit.com/r/LocalLLaMA/comments/1sojjoc/abliterlitics_benchmark_and_tensor_analysis/),
-[2](https://old.reddit.com/r/LocalLLaMA/comments/1sy18lx/abliterlitics_benchmarks_and_tensor_comparison/).
+Heretic supports most dense models, including many multimodal models, and
+several different MoE architectures. It does not yet support SSMs/hybrid models,
+models with inhomogeneous layers, and certain novel attention systems.
 
-The community has created and published
-[well over 4000](https://huggingface.co/models?other=heretic)
-models with Heretic.
+You can find a small collection of models that have been decensored using Heretic
+[on Hugging Face](https://huggingface.co/collections/p-e-w/the-bestiary),
+and the community has created and published
+[well over 1,000](https://huggingface.co/models?other=heretic)
+Heretic models in addition to those.
 
 
 ## Usage
@@ -86,28 +81,13 @@ 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
 ```
 
 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,
@@ -116,15 +96,14 @@ 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
-[Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
-takes about 20-30 minutes. Note that Heretic supports model quantization with
+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
 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,
-run standard benchmarks on it, or any combination of those actions.
+or any combination of those actions.
 
 
 ## Research features
@@ -134,7 +113,7 @@ 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

@@ -27,12 +27,6 @@ device_map = "auto"
 # Maximum memory to allocate per device.
 # max_memory = { "0" = "20GB", "cpu" = "64GB" }
 
-# Whether to move intermediate analysis tensors (such as residuals and logprobs)
-# to CPU memory as soon as possible to reduce peak VRAM usage.
-# This lowers peak VRAM usage during residual analysis and evaluation,
-# but may slightly reduce performance due to host/device transfers.
-offload_outputs_to_cpu = true
-
 # Number of input sequences to process in parallel (0 = auto).
 batch_size = 0  # auto
 
@@ -42,38 +36,9 @@ 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
 
-# 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
 
@@ -99,13 +64,13 @@ kl_divergence_target = 0.01
 
 # Whether to adjust the refusal directions so that only the component that is
 # orthogonal to the good direction is subtracted during abliteration.
-orthogonalize_direction = true
+orthogonalize_direction = false
 
 # How to apply row normalization of the weights. Options:
 # "none" (no normalization),
 # "pre" (compute LoRA adapter relative to row-normalized weights),
 # "full" (like "pre", but renormalizes to preserve original row magnitudes).
-row_normalization = "full"
+row_normalization = "none"
 
 # The rank of the LoRA adapter to use when "full" row normalization is used.
 # Row magnitude preservation is approximate due to non-linear effects,
@@ -126,15 +91,15 @@ 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",
@@ -172,10 +137,11 @@ refusal_markers = [
 # System prompt to use when prompting the model.
 system_prompt = "You are a helpful assistant."
 
-# Each "dataset" below can be a Hugging Face dataset ID, a path to a dataset on disk,
-# or a path to a plain text file with one prompt per line (empty lines are ignored).
-# For text files, "column" is ignored and "split" is optional; when given, it selects
-# a subset of the lines using slice notation (e.g. "[:400]").
+# Move intermediate analysis tensors (such as residuals and logprobs)
+# to CPU memory as soon as possible to reduce peak VRAM usage.
+# This lowers peak VRAM usage during residual analysis and evaluation,
+# but may slightly reduce performance due to host/device transfers.
+offload_outputs_to_cpu = true
 
 # Dataset of prompts that tend to not result in refusals (used for calculating refusal directions).
 [good_prompts]

config.nohumor.toml

@@ -1,69 +0,0 @@
-# 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"

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "heretic-llm"
-version = "1.4.0"
+version = "1.2.0"
 description = "Fully automatic censorship removal for language models"
 readme = "README.md"
 license = "AGPL-3.0-or-later"
@@ -25,23 +25,23 @@ 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~=0.19",
+    "peft~=0.18",
     "psutil~=7.2",
     "py-cpuinfo~=9.0",
     "pydantic-settings~=2.13",
     "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",
+    "transformers~=5.3",
 ]
 
 [project.optional-dependencies]
@@ -60,8 +60,8 @@ dev = [
 ]
 
 [project.urls]
-Homepage = "https://heretic-project.org"
-Documentation = "https://heretic-project.org/tutorial"
+Homepage = "https://github.com/p-e-w/heretic"
+Documentation = "https://github.com/p-e-w/heretic"
 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

@@ -4,12 +4,7 @@
 from enum import Enum
 from typing import Dict
 
-from pydantic import (
-    BaseModel,
-    Field,
-    NonNegativeInt,
-    PositiveInt,
-)
+from pydantic import BaseModel, Field
 from pydantic_settings import (
     BaseSettings,
     CliSettingsSource,
@@ -37,11 +32,6 @@ 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."
@@ -52,15 +42,9 @@ class DatasetSpecification(BaseModel):
         description="Hugging Face commit hash of the dataset.",
     )
 
-    split: str | None = Field(
-        default=None,
-        description="Portion of the dataset to use. Required for datasets, optional for plain text files.",
-    )
+    split: str = Field(description="Portion of the dataset to use.")
 
-    column: str | None = Field(
-        default=None,
-        description="Column in the dataset that contains the prompts. Required for datasets, ignored for plain text files.",
-    )
+    column: str = Field(description="Column in the dataset that contains the prompts.")
 
     prefix: str = Field(
         default="",
@@ -119,25 +103,6 @@ class Settings(BaseSettings):
         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(
         default=[
             # In practice, "auto" almost always means bfloat16.
@@ -176,22 +141,19 @@ class Settings(BaseSettings):
         description='Maximum memory to allocate per device (e.g., { "0" = "20GB", "cpu" = "64GB" }).',
     )
 
-    offload_outputs_to_cpu: bool = Field(
-        default=True,
-        description=(
-            "Whether to move intermediate analysis tensors (such as residuals and logprobs) "
-            "to CPU memory as soon as possible to reduce peak VRAM usage. "
-            "This lowers peak VRAM usage during residual analysis and evaluation, "
-            "but may slightly reduce performance due to host/device transfers."
-        ),
+    trust_remote_code: bool | None = Field(
+        default=None,
+        description="Whether to trust remote code when loading the model.",
+        # For security reasons, we don't store this setting.
+        exclude=True,
     )
 
-    batch_size: NonNegativeInt = Field(
+    batch_size: int = Field(
         default=0,  # auto
         description="Number of input sequences to process in parallel (0 = auto).",
     )
 
-    max_batch_size: PositiveInt = Field(
+    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,
@@ -199,7 +161,7 @@ class Settings(BaseSettings):
         exclude=True,
     )
 
-    max_response_length: PositiveInt = Field(
+    max_response_length: int = Field(
         default=100,
         description="Maximum number of tokens to generate for each response.",
     )
@@ -252,12 +214,6 @@ 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.",
@@ -305,7 +261,7 @@ class Settings(BaseSettings):
     )
 
     orthogonalize_direction: bool = Field(
-        default=True,
+        default=False,
         description=(
             "Whether to adjust the refusal directions so that only the component that is "
             "orthogonal to the good direction is subtracted during abliteration."
@@ -313,7 +269,7 @@ class Settings(BaseSettings):
     )
 
     row_normalization: RowNormalization = Field(
-        default=RowNormalization.FULL,
+        default=RowNormalization.NONE,
         description=(
             "How to apply row normalization of the weights. Options: "
             '"none" (no normalization), '
@@ -322,7 +278,7 @@ class Settings(BaseSettings):
         ),
     )
 
-    full_normalization_lora_rank: PositiveInt = Field(
+    full_normalization_lora_rank: int = Field(
         default=3,
         description=(
             'The rank of the LoRA adapter to use when "full" row normalization is used. '
@@ -343,12 +299,12 @@ class Settings(BaseSettings):
         ),
     )
 
-    n_trials: PositiveInt = Field(
+    n_trials: int = Field(
         default=200,
         description="Number of abliteration trials to run during optimization.",
     )
 
-    n_startup_trials: NonNegativeInt = Field(
+    n_startup_trials: int = Field(
         default=60,
         description="Number of trials that use random sampling for the purpose of exploration.",
     )
@@ -429,66 +385,13 @@ class Settings(BaseSettings):
         exclude=True,
     )
 
-    max_shard_size: PositiveInt | str = Field(
+    max_shard_size: int | 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",
             "sorry",
             "i can'",
             "i cant",
@@ -530,6 +433,14 @@ class Settings(BaseSettings):
         description="System prompt to use when prompting the model.",
     )
 
+    offload_outputs_to_cpu: bool = Field(
+        default=True,
+        description=(
+            "Whether to move intermediate analysis tensors (such as residuals and logprobs) "
+            "to CPU memory as soon as possible to reduce peak VRAM usage."
+        ),
+    )
+
     good_prompts: DatasetSpecification = Field(
         default=DatasetSpecification(
             dataset="mlabonne/harmless_alpaca",

src/heretic/model.py

@@ -17,14 +17,12 @@ from torch.nn import Module, ModuleList
 from transformers import (
     AutoModelForCausalLM,
     AutoModelForImageTextToText,
-    AutoProcessor,
     AutoTokenizer,
     BatchEncoding,
     BitsAndBytesConfig,
     PretrainedConfig,
     PreTrainedModel,
     PreTrainedTokenizerBase,
-    ProcessorMixin,
     TextStreamer,
 )
 from transformers.generation import (
@@ -33,7 +31,7 @@ from transformers.generation import (
 
 from .config import QuantizationMethod, RowNormalization, Settings
 from .system import empty_cache
-from .utils import Prompt, batchify, format_exception, print
+from .utils import Prompt, batchify, print
 
 
 def get_model_class(
@@ -58,10 +56,7 @@ 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
@@ -76,17 +71,10 @@ class 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
@@ -102,8 +90,10 @@ class Model:
             if settings.max_memory
             else None
         )
+        self.trusted_models = {settings.model: settings.trust_remote_code}
 
-        self.trusted_models = set()
+        if self.settings.evaluate_model is not None:
+            self.trusted_models[settings.evaluate_model] = settings.trust_remote_code
 
         for dtype in settings.dtypes:
             print(f"* Trying dtype [bold]{dtype}[/]...")
@@ -122,19 +112,15 @@ class Model:
                     dtype=dtype,
                     device_map=settings.device_map,
                     max_memory=self.max_memory,
-                    trust_remote_code=True
-                    if settings.model in self.trusted_models
-                    else None,
+                    trust_remote_code=self.trusted_models.get(settings.model),
                     **self.revision_kwargs,
                     **extra_kwargs,
                 )
 
-                self.dtype = self.model.dtype
-
                 # If we reach this point and the model requires trust_remote_code,
-                # the user must have agreed when prompted to execute remote code,
-                # because from_pretrained raises an exception otherwise.
-                self.trusted_models.add(settings.model)
+                # 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
 
                 # A test run can reveal dtype-related problems such as the infamous
                 # "RuntimeError: probability tensor contains either `inf`, `nan` or element < 0"
@@ -151,13 +137,7 @@ 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}[/]")
-                else:
-                    print(f"* [red]Failed ({formatted})[/]")
-
+                print(f"* [red]Failed[/] ({error})")
                 continue
 
             if settings.quantization == QuantizationMethod.BNB_4BIT:
@@ -174,15 +154,13 @@ class Model:
         # so we don't need to do anything manually.
 
         print(f"* Transformer model with [bold]{len(self.get_layers())}[/] layers")
-
+        print("* Abliterable components:")
         all_components = {}
         for layer_index in range(len(self.get_layers())):
             for component, modules in self.get_layer_modules(layer_index).items():
                 if component not in all_components:
                     all_components[component] = 0
                 all_components[component] += len(modules)
-
-        print("* Abliterable components:")
         for component, count in all_components.items():
             print(f"  * [bold]{component}[/]: [bold]{count}[/] modules total")
 
@@ -284,9 +262,7 @@ class Model:
                 self.settings.model,
                 torch_dtype=self.model.dtype,
                 device_map="cpu",
-                trust_remote_code=True
-                if self.settings.model in self.trusted_models
-                else None,
+                trust_remote_code=self.trusted_models.get(self.settings.model),
                 **self.revision_kwargs,
             )
 
@@ -322,40 +298,33 @@ 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:
-            current_model = getattr(self.model.config, "name_or_path", 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(self.dtype).split(".")[-1]
-        )
+        quantization_config = self._get_quantization_config(str(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=self.dtype,
+            dtype=dtype,
             device_map=self.settings.device_map,
             max_memory=self.max_memory,
-            trust_remote_code=True
-            if self.settings.model in self.trusted_models
-            else None,
+            trust_remote_code=self.trusted_models.get(self.settings.model),
             **self.revision_kwargs,
             **extra_kwargs,
         )
@@ -399,8 +368,8 @@ class Model:
         with suppress(Exception):
             try_add("attn.o_proj", layer.self_attn.o_proj)  # ty:ignore[possibly-missing-attribute]
 
-        # Qwen3.5 MoE hybrid layers use GatedDeltaNet (linear attention) instead of
-        # standard self-attention, so self_attn.o_proj doesn't exist on those layers.
+        # Qwen3.5 MoE hybrid layers use GatedDeltaNet (linear attention) instead
+        # of standard self-attention, so self_attn.o_proj doesn't exist on those layers.
         with suppress(Exception):
             try_add("attn.o_proj", layer.linear_attn.out_proj)  # ty:ignore[possibly-missing-attribute]
 
@@ -418,21 +387,6 @@ 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]
@@ -449,13 +403,11 @@ class Model:
         return modules
 
     def get_abliterable_components(self) -> list[str]:
-        components: set[str] = set()
-
         # Scan all layers because hybrid models (e.g. Qwen3.5 MoE) have different
         # components on different layers (some have self_attn, others linear_attn).
+        components: set[str] = set()
         for layer_index in range(len(self.get_layers())):
             components.update(self.get_layer_modules(layer_index).keys())
-
         return sorted(components)
 
     def abliterate(
@@ -499,12 +451,6 @@ 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.
@@ -586,16 +532,7 @@ 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]
@@ -791,7 +728,7 @@ class Model:
         _, outputs = self.generate(
             prompts,
             max_new_tokens=1,
-            output_logits=True,
+            output_scores=True,
             return_dict_in_generate=True,
             use_cache=False,
         )
@@ -801,16 +738,15 @@ class Model:
         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]
+        # This cast is valid because we passed output_scores=True above.
+        logits = cast(tuple[FloatTensor], outputs.scores)[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()
 

src/heretic/reproduce.py

@@ -1,391 +0,0 @@
-# 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 (
-    GatedRepoError,
-    disable_progress_bars,
-    enable_progress_bars,
-)
-from questionary import Choice, Style
-from rich.table import Table
-
-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):
-    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(
-    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

@@ -1,30 +1,29 @@
 # SPDX-License-Identifier: AGPL-3.0-or-later
 # Copyright (C) 2025-2026  Philipp Emanuel Weidmann <pew@worldwidemann.com> + contributors
 
-import hashlib
+import getpass
 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 TypeVar
+from typing import Any, 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
 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 Question
+from questionary import Choice, Style
 from rich.console import Console
 
 from .config import DatasetSpecification, Settings
@@ -37,9 +36,6 @@ from .system import (
     is_xpu_available,
 )
 
-T = TypeVar("T")
-
-
 print = Console(highlight=False).print
 
 
@@ -66,6 +62,99 @@ 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)
@@ -79,39 +168,16 @@ 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,
-    # even if the path string is also a valid repository ID.
-    if Path(path).exists():
-        return False
-
-    validate_repo_id(path)
-    return True
+    return (
+        not path.startswith("/")
+        and not path.endswith("/")
+        and path.count("/") == 1
+        and "\\" not in path
+        and not Path(path).exists()
+    )
 
 
 @dataclass
@@ -120,23 +186,6 @@ class Prompt:
     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,
@@ -144,41 +193,29 @@ def load_prompts(
     path = specification.dataset
     split_str = specification.split
 
-    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]
+    if is_hf_path(path):
+        dataset = load_dataset(
+            path,
+            revision=specification.commit,
+            split=split_str,
+        )
     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():
+        if 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 and apply them.
-            start, end = get_split_slice(split_str, len(dataset))
-            dataset = dataset[start:end]
+            # 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]
         else:
             # Path should be a local directory.
             dataset = load_dataset(
@@ -190,7 +227,7 @@ def load_prompts(
                 download_mode=DownloadMode.FORCE_REDOWNLOAD,
             )
 
-        prompts = list(dataset[specification.column])
+    prompts = list(dataset[specification.column])
 
     if specification.prefix:
         prompts = [f"{specification.prefix} {prompt}" for prompt in prompts]
@@ -213,11 +250,14 @@ 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 | FrozenTrial) -> dict[str, str]:
+def get_trial_parameters(trial: Trial) -> dict[str, str]:
     params = {}
 
     direction_index = trial.user_attrs["direction_index"]
@@ -234,7 +274,7 @@ def get_trial_parameters(trial: Trial | FrozenTrial) -> dict[str, str]:
 
 def get_readme_intro(
     settings: Settings,
-    trial: Trial | FrozenTrial,
+    trial: Trial,
     contains_reproducibility_information: bool,
 ) -> str:
     if is_hf_path(settings.model):
@@ -243,6 +283,8 @@ def get_readme_intro(
         # Hide the path, which may contain private information.
         model_link = "a model"
 
+    version_info = get_heretic_version_info()
+
     if contains_reproducibility_information:
         reproducibility_instructions = """
 > [!TIP]
@@ -255,7 +297,7 @@ def get_readme_intro(
 
     return f"""# This is a decensored version of {
         model_link
-    }, made using [Heretic](https://heretic-project.org) v{version("heretic-llm")}
+    }, made using [Heretic](https://github.com/p-e-w/heretic) v{version_info.version}
 {reproducibility_instructions}
 ## Abliteration parameters
 
@@ -299,6 +341,14 @@ 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,
@@ -318,7 +368,7 @@ def format_hf_link(
 def generate_reproduce_readme(
     settings: Settings,
     checkpoint_filename: str,
-    trial: Trial | FrozenTrial,
+    trial: Trial,
     include_system_information: bool,
 ) -> str:
     """Generates the contents of a README.md for the reproduce/ folder."""
@@ -470,18 +520,13 @@ 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.
@@ -492,7 +537,7 @@ This directory contains the necessary information and assets to reproduce the re
 
 def generate_reproduce_json(
     settings: Settings,
-    trial: Trial | FrozenTrial,
+    trial: Trial,
     timestamp: str,
     uploaded_model_hashes: dict[str, str],
     include_system_information: bool,
@@ -502,7 +547,7 @@ def generate_reproduce_json(
     version_info = get_heretic_version_info()
 
     data = {
-        "version": "2",  # Version number of the reproduce.json file format, to allow for future changes.
+        "version": "1",  # Version number of the reproduce.json file format, to allow for future changes.
         "timestamp": timestamp,
         "system": None,  # Defined here to preserve insertion order.
         "environment": {
@@ -556,23 +601,11 @@ 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 | FrozenTrial,
+    trial: Trial,
     uploaded_model_hashes: dict[str, str],
     include_system_information: bool,
 ):
@@ -646,7 +679,7 @@ def upload_reproduce_folder(
     settings: Settings,
     token: str,
     checkpoint_path: str | Path,
-    trial: Trial | FrozenTrial,
+    trial: Trial,
     include_system_information: bool,
 ):
     api = huggingface_hub.HfApi()

tests/README.md

@@ -1,17 +0,0 @@
-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

@@ -1,7 +0,0 @@
-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

@@ -1,7 +0,0 @@
-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

@@ -1,7 +0,0 @@
-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

@@ -1,7 +0,0 @@
-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

@@ -1,41 +0,0 @@
-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

@@ -1,7 +0,0 @@
-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

@@ -1,7 +0,0 @@
-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

@@ -1,7 +0,0 @@
-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

@@ -1,7 +0,0 @@
-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

@@ -1,41 +0,0 @@
-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

@@ -1,7 +0,0 @@
-a4aee8afcf2e0711942cf848899be66016f8d14a889ff9ede07bca099c28f715 *chat_template.jinja
-749b56d1b1e08081981169db6f2c44ab0be4fd6ebb452d15baafa5e09c21586a *config.json
-4625d1d64d41d1fa9dae7af4ba1e1d7e65a194073d4efa58acb266a916eaaa74 *generation_config.json
-5fb94c65bcd9d736735a45e50c2b0bfafd3bb09a444c49b8cff2e131ed35797e *model.safetensors
-01562eddd6f9e9ec4bc31656a3b7055284cafbf889acc6c4348dca431ae31f68 *processor_config.json
-87a7830d63fcf43bf241c3c5242e96e62dd3fdc29224ca26fed8ea333db72de4 *tokenizer.json
-2e31d1126e81bddf8d15c3f95260fb487b48c5131b24fcbb5bb9d2537e7afac0 *tokenizer_config.json

tests/qwen3.5-moe/SHA256SUMS.linux

@@ -1,7 +0,0 @@
-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

@@ -1,7 +0,0 @@
-a92e1dd97cb1cb175c9b70c0828e146bea4371c2643319b661b777e89811972e *chat_template.jinja
-b75e911805663da79fb9fbbbcc917b8f1a285d2da54d95c2c63ea7c1ffe9a05a *config.json
-2cbd9df0e99570efcced23b8d777bdf1fc692efda54b21eb59ad56ade76c9db6 *generation_config.json
-5f099b32807d0b84ed90765ca0ed53f8771da4738767bc1940486fec954570cf *model.safetensors
-0c29f9491e769aabbc389ad5912127cf6d9d5fceda2db8767f73d48131348c81 *processor_config.json
-87a7830d63fcf43bf241c3c5242e96e62dd3fdc29224ca26fed8ea333db72de4 *tokenizer.json
-4796e48d790a26d65f167bec8fc742beaa71f79f9468a6cd8b3ffa97f6e2a198 *tokenizer_config.json

tests/qwen3.5-moe/config.toml

@@ -1,41 +0,0 @@
-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

@@ -1,87 +0,0 @@
-# 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

@@ -8,7 +8,7 @@ resolution-markers = [
 ]
 
 [options]
-exclude-newer = "0001-01-01T00:00:00Z" # This has no effect and is included for backwards compatibility when using relative exclude-newer values.
+exclude-newer = "2026-04-14T22:48:57.86057843Z"
 exclude-newer-span = "P7D"
 
 [[package]]
@@ -50,7 +50,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.14.1"
+version = "3.13.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -60,129 +60,112 @@ dependencies = [
     { name = "frozenlist" },
     { name = "multidict" },
     { name = "propcache" },
-    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
     { name = "yarl" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/45/4a/064321452809dae953c1ed6e017504e72551a26b6f5708a5a80e4bf556ff/aiohttp-3.13.4.tar.gz", hash = "sha256:d97a6d09c66087890c2ab5d49069e1e570583f7ac0314ecf98294c1b6aaebd38", size = 7859748, upload-time = "2026-03-28T17:19:40.6Z" }
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+version = "1.2.0"
 source = { editable = "." }
 dependencies = [
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     { name = "bitsandbytes" },
     { name = "datasets" },
+    { name = "hf-transfer" },
     { name = "huggingface-hub" },
     { name = "immutabledict" },
+    { name = "kernels" },
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-    { name = "torch" },
-    { name = "torchvision" },
     { name = "tqdm" },
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     { name = "datasets", specifier = "~=4.7" },
     { name = "geom-median", marker = "extra == 'research'", specifier = "~=0.1" },
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     { name = "imageio", marker = "extra == 'research'", specifier = "~=2.37" },
     { name = "immutabledict", specifier = "~=4.3" },
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     { name = "pacmap", marker = "extra == 'research'", specifier = "~=0.8" },
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     { name = "pydantic-settings", specifier = "~=2.13" },
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-    { name = "torchvision" },
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-    { name = "transformers", extras = ["kernels"], specifier = "~=5.6" },
+    { name = "transformers", specifier = "~=5.3" },
 ]
 provides-extras = ["research"]
 
@@ -1026,6 +1009,38 @@ dev = [
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@@ -3926,11 +3895,11 @@ wheels = [
 
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 name = "urllib3"
-version = "2.7.0"
+version = "2.6.3"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/53/0c/06f8b233b8fd13b9e5ee11424ef85419ba0d8ba0b3138bf360be2ff56953/urllib3-2.7.0.tar.gz", hash = "sha256:231e0ec3b63ceb14667c67be60f2f2c40a518cb38b03af60abc813da26505f4c", size = 433602, upload-time = "2026-05-07T16:13:18.596Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/c7/24/5f1b3bdffd70275f6661c76461e25f024d5a38a46f04aaca912426a2b1d3/urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed", size = 435556, upload-time = "2026-01-07T16:24:43.925Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/7f/3e/5db95bcf282c52709639744ca2a8b149baccf648e39c8cc87553df9eae0c/urllib3-2.7.0-py3-none-any.whl", hash = "sha256:9fb4c81ebbb1ce9531cce37674bbc6f1360472bc18ca9a553ede278ef7276897", size = 131087, upload-time = "2026-05-07T16:13:17.151Z" },
+    { url = "https://files.pythonhosted.org/packages/39/08/aaaad47bc4e9dc8c725e68f9d04865dbcb2052843ff09c97b08904852d84/urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4", size = 131584, upload-time = "2026-01-07T16:24:42.685Z" },
 ]
 
 [[package]]