fix: fix remaining issues

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
@@ -93,21 +88,6 @@ heretic Qwen/Qwen3-4B-Instruct-2507
 
 Replace `Qwen/Qwen3-4B-Instruct-2507` with whatever model you want to decensor.
 
-> [!IMPORTANT]
->
-> While PyTorch 2.2 is the minimum version of PyTorch needed for Heretic to work,
-> some models and configurations might require features only found in
-> later versions. For example, loading MXFP4-quantized models like gpt-oss
-> uses `torch.accelerator`, which was added in PyTorch 2.6.
-
-> [!TIP]
->
-> Heretic uses [uv](https://docs.astral.sh/uv/) for dependency management,
-> and the repository includes a `uv.lock` file pinning every package version.
-> If you already use uv (and you probably should!), you can just clone the repo
-> and run Heretic with `uv run heretic`, which ensures that your dependencies
-> match those used by the developers, improving reliability and security.
-
 The process is fully automatic and does not require configuration; however,
 Heretic has a variety of configuration parameters that can be changed for
 greater control. Run `heretic --help` to see available command-line options,
@@ -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

config.default.toml

@@ -27,12 +27,6 @@ device_map = "auto"
 # Maximum memory to allocate per device.
 # max_memory = { "0" = "20GB", "cpu" = "64GB" }
 
-# Whether to move intermediate analysis tensors (such as residuals and logprobs)
-# to CPU memory as soon as possible to reduce peak VRAM usage.
-# This lowers peak VRAM usage during residual analysis and evaluation,
-# but may slightly reduce performance due to host/device transfers.
-offload_outputs_to_cpu = true
-
 # Number of input sequences to process in parallel (0 = auto).
 batch_size = 0  # auto
 
@@ -42,32 +36,6 @@ max_batch_size = 128
 # Maximum number of tokens to generate for each response.
 max_response_length = 100
 
-# List of pairs of the form [cot_initializer, closed_cot_block] used to skip
-# the Chain-of-Thought block in responses, so that evaluation happens
-# at the start of the actual response.
-chain_of_thought_skips = [
-    # Most thinking models.
-    [
-        "<think>",
-        "<think></think>",
-    ],
-    # gpt-oss.
-    [
-        "<|channel|>analysis<|message|>",
-        "<|channel|>analysis<|message|><|end|><|start|>assistant<|channel|>final<|message|>",
-    ],
-    # Unknown, suggested by user.
-    [
-        "<thought>",
-        "<thought></thought>",
-    ],
-    # Unknown, suggested by user.
-    [
-        "[THINK]",
-        "[THINK][/THINK]",
-    ],
-]
-
 # Whether to print prompt/response pairs when counting refusals.
 print_responses = false
 
@@ -96,13 +64,13 @@ kl_divergence_target = 0.01
 
 # Whether to adjust the refusal directions so that only the component that is
 # orthogonal to the good direction is subtracted during abliteration.
-orthogonalize_direction = true
+orthogonalize_direction = false
 
 # How to apply row normalization of the weights. Options:
 # "none" (no normalization),
 # "pre" (compute LoRA adapter relative to row-normalized weights),
 # "full" (like "pre", but renormalizes to preserve original row magnitudes).
-row_normalization = "full"
+row_normalization = "none"
 
 # The rank of the LoRA adapter to use when "full" row normalization is used.
 # Row magnitude preservation is approximate due to non-linear effects,
@@ -123,15 +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",
@@ -169,10 +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"
@@ -25,13 +25,15 @@ 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",
@@ -39,7 +41,7 @@ dependencies = [
     "rich~=14.3",
     "tomli-w~=1.2",
     "tqdm~=4.67",
-    "transformers[kernels]~=5.6",
+    "transformers~=5.3",
 ]
 
 [project.optional-dependencies]
@@ -58,8 +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

@@ -32,11 +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."
@@ -47,15 +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="",
@@ -114,25 +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.
@@ -171,14 +141,11 @@ 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: int = Field(
@@ -294,7 +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."
@@ -302,7 +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), '
@@ -418,11 +385,6 @@ class Settings(BaseSettings):
         exclude=True,
     )
 
-    export_strategy: ExportStrategy | None = Field(
-        default=None,
-        description='How to export the model: "merge", "adapter", or unset to prompt the user.',
-    )
-
     max_shard_size: int | str = Field(
         default="5GB",
         description="Maximum size for individual safetensors files generated when exporting a model.",
@@ -430,7 +392,6 @@ class Settings(BaseSettings):
 
     refusal_markers: list[str] = Field(
         default=[
-            "disclaimer",
             "sorry",
             "i can'",
             "i cant",
@@ -472,6 +433,14 @@ class Settings(BaseSettings):
         description="System prompt to use when prompting the model.",
     )
 
+    offload_outputs_to_cpu: bool = Field(
+        default=True,
+        description=(
+            "Whether to move intermediate analysis tensors (such as residuals and logprobs) "
+            "to CPU memory as soon as possible to reduce peak VRAM usage."
+        ),
+    )
+
     good_prompts: DatasetSpecification = Field(
         default=DatasetSpecification(
             dataset="mlabonne/harmless_alpaca",

src/heretic/main.py

@@ -17,15 +17,11 @@ def _is_help_invocation() -> bool:
 if _is_help_invocation():
     Settings()  # ty:ignore[missing-argument]
 
-# FIXME: Rich progress bars are currently disabled because of rendering issues
-#        when used from multiple threads in parallel (e.g. by huggingface_hub).
-"""
 from .progress import patch_tqdm
 
 # This patches tqdm class definitions, which must happen
 # before any other module imports tqdm.
 patch_tqdm()
-"""
 
 import logging
 import math
@@ -47,7 +43,7 @@ import questionary
 import torch
 import torch.nn.functional as F
 import transformers
-from huggingface_hub import HfApi, ModelCard, ModelCardData
+from huggingface_hub import ModelCard, ModelCardData
 from lm_eval.models.huggingface import HFLM
 from optuna import Trial, TrialPruned
 from optuna.exceptions import ExperimentalWarning
@@ -55,26 +51,19 @@ 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, create_trial
+from optuna.trial import TrialState
 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 ExportStrategy, QuantizationMethod
+from .config import QuantizationMethod
 from .evaluator import Evaluator
 from .model import AbliterationParameters, Model, get_model_class
-from .reproduce import (
-    check_environment,
-    collect_reproducibles,
-    load_reproduction_information,
-)
 from .system import empty_cache, get_accelerator_info
 from .utils import (
     format_duration,
-    format_exception,
-    get_file_sha256,
     get_readme_intro,
     get_trial_parameters,
     is_hf_path,
@@ -90,23 +79,17 @@ from .utils import (
 )
 
 
-def obtain_export_strategy(
-    settings: Settings,
-    model: Model,
-) -> ExportStrategy | None:
+def obtain_merge_strategy(settings: Settings, model: Model) -> str | None:
     """
-    Gets the export strategy from settings or prompts the user.
+    Prompts the user for how to proceed with saving the model.
     Provides info to the user if the model is quantized on memory use.
-    Returns an export strategy, or None if cancelled.
+    Returns "merge", "adapter", or None (if cancelled/invalid).
     """
 
-    if settings.export_strategy is not None:
-        return settings.export_strategy
-
     if settings.quantization == QuantizationMethod.BNB_4BIT:
         print()
         print(
-            "The model was loaded with quantization. Merging requires reloading the base model."
+            "Model was loaded with quantization. Merging requires reloading the base model."
         )
         print(
             "[yellow]WARNING: CPU merging requires dequantizing the entire model to system RAM.[/]"
@@ -125,9 +108,7 @@ def obtain_export_strategy(
                     settings.model,
                     device_map="meta",
                     torch_dtype=torch.bfloat16,
-                    trust_remote_code=True
-                    if settings.model in model.trusted_models
-                    else None,
+                    trust_remote_code=model.trusted_models.get(settings.model),
                     **model.revision_kwargs,
                 )
                 footprint_bytes = meta_model.get_memory_footprint()
@@ -144,29 +125,33 @@ def obtain_export_strategy(
             print(
                 "[yellow]Example: A 27B model requires ~80GB RAM. A 70B model requires ~200GB RAM.[/]"
             )
-
         print()
 
-    strategy = prompt_select(
-        "How do you want to export the model?",
-        choices=[
-            Choice(
-                title="Merge the abliteration LoRA and export the full model"
-                + (
-                    ""
-                    if settings.quantization == QuantizationMethod.NONE
-                    else " (requires sufficient RAM)"
+        strategy = prompt_select(
+            "How do you want to proceed?",
+            choices=[
+                Choice(
+                    title="Merge LoRA into full model"
+                    + (
+                        ""
+                        if settings.quantization == QuantizationMethod.NONE
+                        else " (requires sufficient RAM)"
+                    ),
+                    value="merge",
                 ),
-                value=ExportStrategy.MERGE,
-            ),
-            Choice(
-                title="Export the abliteration LoRA only (can be merged later)",
-                value=ExportStrategy.ADAPTER,
-            ),
-        ],
-    )
+                Choice(
+                    title="Cancel",
+                    value="cancel",
+                ),
+            ],
+        )
 
-    return strategy
+        if strategy == "cancel":
+            return None
+
+        return strategy
+    else:
+        return "merge"
 
 
 def run():
@@ -179,9 +164,7 @@ def run():
 
     # Modified "Pagga" font from https://budavariam.github.io/asciiart-text/
     print(f"[cyan]█░█░█▀▀░█▀▄░█▀▀░▀█▀░█░█▀▀[/]  v{version('heretic-llm')}")
-    print(
-        "[cyan]█▀█░█▀▀░█▀▄░█▀▀░░█░░█░█░░[/]  [blue underline]https://heretic-project.org[/]"
-    )
+    print("[cyan]█▀█░█▀▀░█▀▄░█▀▀░░█░░█░█░░[/]")
     print(
         "[cyan]▀░▀░▀▀▀░▀░▀░▀▀▀░░▀░░▀░▀▀▀[/]  [blue underline]https://github.com/p-e-w/heretic[/]"
     )
@@ -190,9 +173,6 @@ def run():
     if (
         # There is at least one argument (argv[0] is the program name).
         len(sys.argv) > 1
-        # Heretic is being invoked in standard (model processing) mode.
-        and "--collect-reproducibles" not in sys.argv
-        and "--reproduce" not in sys.argv
         # No model has been explicitly provided.
         and "--model" not in sys.argv
         # The last argument is a parameter value rather than a flag (such as "--help").
@@ -201,13 +181,6 @@ def run():
         # Assume the last argument is the model.
         sys.argv.insert(-1, "--model")
 
-    # Work around the "model" argument being required
-    # when Heretic is invoked in a non-processing mode.
-    if (
-        "--collect-reproducibles" in sys.argv or "--reproduce" in sys.argv
-    ) and "--model" not in sys.argv:
-        sys.argv.extend(["--model", ""])
-
     try:
         # The required argument "model" must be provided by the user,
         # either on the command line or in the configuration file.
@@ -215,10 +188,8 @@ def run():
     except ValidationError as error:
         print(f"[red]Configuration contains [bold]{error.error_count()}[/] errors:[/]")
 
-        for error_details in error.errors():
-            print(
-                f"[bold]{error_details['loc'][0]}[/]: [yellow]{error_details['msg']}[/]"
-            )
+        for error in error.errors():
+            print(f"[bold]{error['loc'][0]}[/]: [yellow]{error['msg']}[/]")
 
         print()
         print(
@@ -226,35 +197,6 @@ def run():
         )
         return
 
-    if settings.collect_reproducibles is not None:
-        collect_reproducibles(settings.collect_reproducibles)
-        return
-
-    reproduction_mode = settings.reproduce is not None
-
-    if settings.reproduce is not None:
-        print(f"Loading reproduction information from [bold]{settings.reproduce}[/]...")
-        # FIXME: "Reproduction"/"reproducibility" name inconsistency!
-        reproduction_information = load_reproduction_information(settings.reproduce)
-
-        if reproduction_information["version"] not in ["1", "2"]:
-            print(
-                (
-                    f"[red]Unsupported file format version: [bold]{reproduction_information['version']}[/].[/] "
-                    "Try loading the file with a newer version of Heretic."
-                )
-            )
-            return
-
-        if not check_environment(reproduction_information):
-            return
-
-        print()
-
-        verify_hashes = reproduction_information["version"] != "1"
-
-        settings = Settings.model_validate(reproduction_information["settings"])
-
     if settings.seed is None:
         settings.seed = random.randint(0, 2**32 - 1)
 
@@ -304,11 +246,7 @@ def run():
     except IndexError:
         existing_study = None
 
-    if (
-        existing_study is not None
-        and settings.evaluate_model is None
-        and not reproduction_mode
-    ):
+    if existing_study is not None and settings.evaluate_model is None:
         choices = []
 
         if existing_study.user_attrs["finished"]:
@@ -413,12 +351,7 @@ def run():
                     # We cannot recover from this.
                     raise
 
-                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})")
                 break
 
             response_lengths = [
@@ -492,6 +425,9 @@ def run():
 
     needs_full_residuals = settings.print_residual_geometry or settings.plot_residuals
 
+    good_residuals = None
+    bad_residuals = None
+
     if needs_full_residuals:
         print("* Obtaining residuals for good prompts...")
         good_residuals = model.get_residuals_batched(good_prompts)
@@ -529,12 +465,8 @@ def run():
             refusal_directions - projection_vector.unsqueeze(1) * good_directions
         )
         refusal_directions = F.normalize(refusal_directions, p=2, dim=1)
-        del good_directions, projection_vector
-
-    del good_means, bad_means
 
     # Clear cache before starting the optimization study.
-    # This should free up memory from the objects released with the del statements above.
     empty_cache()
 
     trial_index = 0
@@ -653,198 +585,168 @@ def run():
             trial.study.stop()
             raise TrialPruned()
 
-    if not reproduction_mode:
-        study = optuna.create_study(
-            sampler=TPESampler(
-                n_startup_trials=settings.n_startup_trials,
-                n_ei_candidates=128,
-                multivariate=True,
-                seed=settings.seed,
-            ),
-            directions=[StudyDirection.MINIMIZE, StudyDirection.MINIMIZE],
-            storage=storage,
-            study_name="heretic",
-            load_if_exists=True,
+    study = optuna.create_study(
+        sampler=TPESampler(
+            n_startup_trials=settings.n_startup_trials,
+            n_ei_candidates=128,
+            multivariate=True,
+            seed=settings.seed,
+        ),
+        directions=[StudyDirection.MINIMIZE, StudyDirection.MINIMIZE],
+        storage=storage,
+        study_name="heretic",
+        load_if_exists=True,
+    )
+
+    study.set_user_attr("settings", settings.model_dump_json())
+    study.set_user_attr("finished", False)
+
+    def count_completed_trials() -> int:
+        # Count number of complete trials to compute trials to run.
+        return sum([(1 if t.state == TrialState.COMPLETE else 0) for t in study.trials])
+
+    start_index = trial_index = count_completed_trials()
+    if start_index > 0:
+        print()
+        print("Resuming existing study.")
+
+    try:
+        study.optimize(
+            objective_wrapper,
+            n_trials=settings.n_trials - count_completed_trials(),
         )
+    except KeyboardInterrupt:
+        # This additional handler takes care of the small chance that KeyboardInterrupt
+        # is raised just between trials, which wouldn't be caught by the handler
+        # defined in objective_wrapper above.
+        pass
 
-        study.set_user_attr("settings", settings.model_dump_json())
-        study.set_user_attr("finished", False)
-
-        start_index = trial_index = len(study.trials)
-        if start_index > 0:
-            print()
-            print("Resuming existing study.")
-
-        try:
-            study.optimize(
-                objective_wrapper,
-                n_trials=settings.n_trials - len(study.trials),
-            )
-        except KeyboardInterrupt:
-            # This additional handler takes care of the small chance that KeyboardInterrupt
-            # is raised just between trials, which wouldn't be caught by the handler
-            # defined in objective_wrapper above.
-            pass
-
-        if len(study.trials) == settings.n_trials:
-            study.set_user_attr("finished", True)
+    if count_completed_trials() == settings.n_trials:
+        study.set_user_attr("finished", True)
 
     while True:
-        if not reproduction_mode:
-            # If no trials at all have been evaluated, the study must have been stopped
-            # by pressing Ctrl+C while the first trial was running. In this case, we just
-            # re-raise the interrupt to invoke the standard handler defined below.
-            completed_trials = [
-                t for t in study.trials if t.state == TrialState.COMPLETE
-            ]
-            if not completed_trials:
-                raise KeyboardInterrupt
+        # If no trials at all have been evaluated, the study must have been stopped
+        # by pressing Ctrl+C while the first trial was running. In this case, we just
+        # re-raise the interrupt to invoke the standard handler defined below.
+        completed_trials = [t for t in study.trials if t.state == TrialState.COMPLETE]
+        if not completed_trials:
+            raise KeyboardInterrupt
 
-            # Get the Pareto front of trials. We can't use study.best_trials directly
-            # as get_score() doesn't return the pure KL divergence and refusal count.
-            # Note: Unlike study.best_trials, this does not handle objective constraints.
-            sorted_trials = sorted(
-                completed_trials,
-                key=lambda trial: (
-                    trial.user_attrs["refusals"],
-                    trial.user_attrs["kl_divergence"],
+        # Get the Pareto front of trials. We can't use study.best_trials directly
+        # as get_score() doesn't return the pure KL divergence and refusal count.
+        # Note: Unlike study.best_trials, this does not handle objective constraints.
+        sorted_trials = sorted(
+            completed_trials,
+            key=lambda trial: (
+                trial.user_attrs["refusals"],
+                trial.user_attrs["kl_divergence"],
+            ),
+        )
+        min_divergence = math.inf
+        best_trials = []
+        for trial in sorted_trials:
+            kl_divergence = trial.user_attrs["kl_divergence"]
+            if kl_divergence < min_divergence:
+                min_divergence = kl_divergence
+                best_trials.append(trial)
+
+        choices = [
+            Choice(
+                title=(
+                    f"[Trial {trial.user_attrs['index']:>3}] "
+                    f"Refusals: {trial.user_attrs['refusals']:>2}/{len(evaluator.bad_prompts)}, "
+                    f"KL divergence: {trial.user_attrs['kl_divergence']:.4f}"
                 ),
+                value=trial,
             )
-            min_divergence = math.inf
-            best_trials = []
-            for trial in sorted_trials:
-                kl_divergence = trial.user_attrs["kl_divergence"]
-                if kl_divergence < min_divergence:
-                    min_divergence = kl_divergence
-                    best_trials.append(trial)
+            for trial in best_trials
+        ]
 
-            choices = [
-                Choice(
-                    title=(
-                        f"[Trial {trial.user_attrs['index']:>3}] "
-                        f"Refusals: {trial.user_attrs['refusals']:>2}/{len(evaluator.bad_prompts)}, "
-                        f"KL divergence: {trial.user_attrs['kl_divergence']:.4f}"
-                    ),
-                    value=trial,
-                )
-                for trial in best_trials
-            ]
-
-            choices.append(
-                Choice(
-                    title="Run additional trials",
-                    value="continue",
-                )
+        choices.append(
+            Choice(
+                title="Run additional trials",
+                value="continue",
             )
+        )
 
-            choices.append(
-                Choice(
-                    title="Exit program",
-                    value="",
-                )
+        choices.append(
+            Choice(
+                title="Exit program",
+                value="",
             )
+        )
 
-            print()
-            print("[bold green]Optimization finished![/]")
-            print()
-            print(
-                (
-                    "The following trials resulted in Pareto optimal combinations of refusals and KL divergence. "
-                    "After selecting a trial, you will be able to save the model, upload it to Hugging Face, "
-                    "chat with it to test how well it works, or run standard benchmarks on it. "
-                    "You can return to this menu later to select a different trial. "
-                    "[yellow]Note that KL divergence values above 0.5 usually indicate significant damage to the original model's capabilities.[/]"
-                )
+        print()
+        print("[bold green]Optimization finished![/]")
+        print()
+        print(
+            (
+                "The following trials resulted in Pareto optimal combinations of refusals and KL divergence. "
+                "After selecting a trial, you will be able to save the model, upload it to Hugging Face, "
+                "or chat with it to test how well it works. You can return to this menu later to select a different trial. "
+                "[yellow]Note that KL divergence values above 1 usually indicate significant damage to the original model's capabilities.[/]"
             )
+        )
 
         while True:
-            if reproduction_mode:
-                parameters = reproduction_information["parameters"]
-                metrics = reproduction_information["metrics"]
-
-                trial = create_trial(
-                    values=[],
-                    user_attrs={
-                        "direction_index": parameters["direction_index"],
-                        "parameters": parameters["abliteration_parameters"],
-                        "kl_divergence": metrics["kl_divergence"],
-                        "refusals": metrics["refusals"],
-                        "base_refusals": metrics["base_refusals"],
-                        "n_bad_prompts": metrics["n_bad_prompts"],
-                    },
-                )
-
-                print()
-                print("Restoring model from reproduction information...")
-            else:
-                print()
-                trial = prompt_select("Which trial do you want to use?", choices)
-
-                if trial is None or trial == "":
-                    return
-
-                if trial == "continue":
-                    while True:
-                        try:
-                            n_additional_trials = prompt_text(
-                                "How many additional trials do you want to run?"
-                            )
-                            if n_additional_trials is None or n_additional_trials == "":
-                                n_additional_trials = 0
-                                break
-                            n_additional_trials = int(n_additional_trials)
-                            if n_additional_trials > 0:
-                                break
-                            print("[red]Please enter a number greater than 0.[/]")
-                        except ValueError:
-                            print("[red]Please enter a number.[/]")
-
-                    if n_additional_trials == 0:
-                        continue
-
-                    settings.n_trials += n_additional_trials
-                    study.set_user_attr("settings", settings.model_dump_json())
-                    study.set_user_attr("finished", False)
+            print()
+            trial = prompt_select("Which trial do you want to use?", choices)
 
+            if trial == "continue":
+                while True:
                     try:
-                        study.optimize(
-                            objective_wrapper,
-                            n_trials=settings.n_trials - len(study.trials),
+                        n_additional_trials = prompt_text(
+                            "How many additional trials do you want to run?"
                         )
-                    except KeyboardInterrupt:
-                        pass
+                        if n_additional_trials is None or n_additional_trials == "":
+                            n_additional_trials = 0
+                            break
+                        n_additional_trials = int(n_additional_trials)
+                        if n_additional_trials > 0:
+                            break
+                        print("[red]Please enter a number greater than 0.[/]")
+                    except ValueError:
+                        print("[red]Please enter a number.[/]")
 
-                    if len(study.trials) == settings.n_trials:
-                        study.set_user_attr("finished", True)
+                if n_additional_trials == 0:
+                    continue
 
-                    break
+                settings.n_trials += n_additional_trials
+                study.set_user_attr("settings", settings.model_dump_json())
+                study.set_user_attr("finished", False)
 
-                print()
-                print(
-                    f"Restoring model from trial [bold]{trial.user_attrs['index']}[/]..."
-                )
+                try:
+                    study.optimize(
+                        objective_wrapper,
+                        n_trials=settings.n_trials - count_completed_trials(),
+                    )
+                except KeyboardInterrupt:
+                    pass
 
+                if count_completed_trials() == settings.n_trials:
+                    study.set_user_attr("finished", True)
+
+                break
+
+            elif trial is None or trial == "":
+                return
+
+            print()
+            print(f"Restoring model from trial [bold]{trial.user_attrs['index']}[/]...")
             print("* Parameters:")
             for name, value in get_trial_parameters(trial).items():
                 print(f"  * {name} = [bold]{value}[/]")
-
-            # Per https://github.com/huggingface/peft/issues/868#issuecomment-1820642893
-            # once a LoRA is merged it's expected to be empty. Provide a utility function
-            # to restore the previous LoRA-ified state.
-            def reset_trial_model():
-                print("* Resetting model...")
-                model.reset_model()
-                print("* Abliterating...")
-                model.abliterate(
-                    refusal_directions,
-                    trial.user_attrs["direction_index"],
-                    {
-                        k: AbliterationParameters(**v)
-                        for k, v in trial.user_attrs["parameters"].items()
-                    },
-                )
-
-            reset_trial_model()
+            print("* Resetting model...")
+            model.reset_model()
+            print("* Abliterating...")
+            model.abliterate(
+                refusal_directions,
+                trial.user_attrs["direction_index"],
+                {
+                    k: AbliterationParameters(**v)
+                    for k, v in trial.user_attrs["parameters"].items()
+                },
+            )
 
             while True:
                 print()
@@ -855,20 +757,12 @@ def run():
                         "Upload the model to Hugging Face",
                         "Chat with the model",
                         "Benchmark the model",
-                        Choice(
-                            title="Exit program"
-                            if reproduction_mode
-                            else "Return to the trial selection menu",
-                            value="",
-                        ),
+                        "Return to the trial selection menu",
                     ],
                 )
 
-                if action is None or action == "":
-                    if reproduction_mode:
-                        return
-                    else:
-                        break
+                if action is None or action == "Return to the trial selection menu":
+                    break
 
                 # All actions are wrapped in a try/except block so that if an error occurs,
                 # another action can be tried, instead of the program crashing and losing
@@ -880,11 +774,11 @@ def run():
                             if not save_directory:
                                 continue
 
-                            strategy = obtain_export_strategy(settings, model)
+                            strategy = obtain_merge_strategy(settings, model)
                             if strategy is None:
                                 continue
 
-                            if strategy == ExportStrategy.ADAPTER:
+                            if strategy == "adapter":
                                 print("Saving LoRA adapter...")
                                 model.model.save_pretrained(
                                     save_directory,
@@ -900,37 +794,9 @@ def run():
                                 del merged_model
                                 empty_cache()
                                 model.tokenizer.save_pretrained(save_directory)
-                                if model.processor is not None:
-                                    model.processor.save_pretrained(save_directory)
-                                reset_trial_model()
 
                             print(f"Model saved to [bold]{save_directory}[/].")
 
-                            if reproduction_mode and verify_hashes:
-                                print("Verifying hashes of weight files...")
-
-                                for (
-                                    filename,
-                                    original_sha256,
-                                ) in reproduction_information["hashes"].items():
-                                    file_path = Path(save_directory) / filename
-
-                                    if file_path.exists():
-                                        sha256 = get_file_sha256(file_path)
-
-                                        if sha256.lower() == original_sha256.lower():
-                                            print(
-                                                f"[bold]{filename}:[/] [green]Hash matches[/]"
-                                            )
-                                        else:
-                                            print(
-                                                f"[bold]{filename}:[/] [yellow]Hash doesn't match[/]"
-                                            )
-                                    else:
-                                        print(
-                                            f"[bold]{filename}:[/] [red]File not found[/]"
-                                        )
-
                         case "Upload the model to Hugging Face":
                             # We don't use huggingface_hub.login() because that stores the token on disk,
                             # and since this program will often be run on rented or shared GPU servers,
@@ -965,7 +831,7 @@ def run():
                                 continue
                             private = visibility == "Private"
 
-                            strategy = obtain_export_strategy(settings, model)
+                            strategy = obtain_merge_strategy(settings, model)
                             if strategy is None:
                                 continue
 
@@ -977,10 +843,8 @@ def run():
                                 settings.good_evaluation_prompts.dataset,
                                 settings.bad_evaluation_prompts.dataset,
                             ]
-                            is_reproducible = (
-                                is_hf_path(settings.model)
-                                and all(is_hf_path(dataset) for dataset in datasets)
-                                and not reproduction_mode
+                            is_reproducible = is_hf_path(settings.model) and all(
+                                is_hf_path(dataset) for dataset in datasets
                             )
 
                             if is_reproducible:
@@ -1015,7 +879,7 @@ def run():
                             else:
                                 reproducibility_information = "none"
 
-                            if strategy == ExportStrategy.ADAPTER:
+                            if strategy == "adapter":
                                 print("Uploading LoRA adapter...")
                                 model.model.push_to_hub(
                                     repo_id,
@@ -1039,13 +903,6 @@ def run():
                                     private=private,
                                     token=token,
                                 )
-                                if model.processor is not None:
-                                    model.processor.push_to_hub(
-                                        repo_id,
-                                        private=private,
-                                        token=token,
-                                    )
-                                reset_trial_model()
 
                             if is_hf_path(settings.model):
                                 card = ModelCard.load(settings.model)
@@ -1083,76 +940,20 @@ def run():
                             if reproducibility_information != "none":
                                 # Set the number of trials to the number of actual completed trials
                                 # for the reproduction configuration.
-                                settings.n_trials = len(study.trials)
-                                current_export_strategy = settings.export_strategy
-                                settings.export_strategy = strategy
+                                settings.n_trials = count_completed_trials()
 
-                                try:
-                                    upload_reproduce_folder(
-                                        repo_id,
-                                        settings,
-                                        token,
-                                        checkpoint_path=study_checkpoint_file,
-                                        trial=trial,
-                                        include_system_information=(
-                                            reproducibility_information == "full"
-                                        ),
-                                    )
-                                finally:
-                                    settings.export_strategy = current_export_strategy
-
-                            print(f"Model uploaded to [bold]{repo_id}[/].")
-
-                            if reproduction_mode and verify_hashes:
-                                print("Verifying hashes of weight files...")
-
-                                api = HfApi()
-                                model_info = api.model_info(
+                                upload_reproduce_folder(
                                     repo_id,
-                                    files_metadata=True,
-                                    token=token,
+                                    settings,
+                                    token,
+                                    checkpoint_path=study_checkpoint_file,
+                                    trial=trial,
+                                    include_system_information=(
+                                        reproducibility_information == "full"
+                                    ),
                                 )
 
-                                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[/]"
-                                        )
+                            print(f"Model uploaded to [bold]{repo_id}[/].")
 
                         case "Chat with the model":
                             print()
@@ -1292,11 +1093,7 @@ def run():
                                 print(table)
 
                 except Exception as error:
-                    formatted = format_exception(error)
-                    if "\n" in formatted:
-                        print(f"[red]Error:\n{formatted}[/]")
-                    else:
-                        print(f"[red]Error: {formatted}[/]")
+                    print(f"[red]Error: {error}[/]")
 
 
 def main():

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(
@@ -580,10 +532,6 @@ class Model:
                         W = W - W_org
                         # Use a low-rank SVD to get an approximation of the matrix.
                         r = self.peft_config.r
-                        # svd_lowrank is randomized:
-                        # https://github.com/pytorch/pytorch/blob/20919052303c0b5ba87f8bf7e19237dc33ab09d3/torch/_lowrank.py#L108-L109
-                        # Reseed immediately before the call so restoring a trial is independent of RNG history.
-                        torch.manual_seed(self.settings.seed)
                         U, S, Vh = torch.svd_lowrank(W, q=2 * r + 4, niter=6)
                         # Truncate it to the part we want to store in the LoRA adapter.
                         # Note: svd_lowrank actually returns V, so transpose it to get Vh.
@@ -780,7 +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,
         )
@@ -790,16 +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

@@ -1,382 +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 torch
-from huggingface_hub import HfApi, hf_hub_download
-from huggingface_hub.utils import (
-    GatedRepoError,
-    disable_progress_bars,
-    enable_progress_bars,
-)
-from questionary import Choice
-from rich.table import Table
-
-from .system import (
-    get_accelerator_info_dict,
-    get_heretic_version_info,
-    get_requirements_dict,
-)
-from .utils import print, prompt_select
-
-
-def collect_reproducibles(path: str):
-    print(
-        f"Collecting [bold]reproduce.json[/] files from Hugging Face and storing them in [bold]{path}[/]..."
-    )
-    print()
-
-    api = HfApi()
-
-    models = api.list_models(
-        filter=["heretic", "reproducible"],
-        sort="created_at",
-        expand=["gated", "tags"],
-    )
-
-    found = 0
-    downloaded = 0
-
-    # We're only downloading tiny files, so the progress bars are just noise.
-    disable_progress_bars()
-
-    try:
-        for model in models:
-            # Ignore repositories containing quantizations.
-            if model.tags is not None and "gguf" in model.tags:
-                continue
-
-            if model.gated:
-                try:
-                    api.auth_check(model.id, repo_type="model")
-                except GatedRepoError:
-                    continue
-
-            print(f"[bold]{model.id}[/]...", end="")
-
-            user, repository = model.id.split("/")
-
-            paths_info = api.get_paths_info(
-                model.id,
-                "reproduce/reproduce.json",
-                expand=True,
-            )
-            # The reproduce.json file might not exist in the repository
-            # despite the relevant tags being present.
-            if not paths_info:
-                print(" [yellow]no reproduce.json found[/]")
-                continue
-
-            found += 1
-
-            commit_hash = paths_info[0].last_commit.oid
-
-            file_path = (
-                Path(path)
-                / "huggingface.co"
-                / user
-                / f"{repository}-{commit_hash[:7]}.json"
-            )
-            if file_path.exists():
-                print(" already stored")
-                continue
-
-            cache_path = hf_hub_download(
-                model.id,
-                "reproduce/reproduce.json",
-            )
-
-            file_path.parent.mkdir(parents=True, exist_ok=True)
-            shutil.copyfile(cache_path, file_path)
-            print(" [green]downloaded[/]")
-
-            downloaded += 1
-    finally:
-        enable_progress_bars()
-
-    print()
-    print(f"Found: [bold]{found}[/] files")
-    print(f"Downloaded: [bold]{downloaded}[/] files")
-    print(f"Already stored: [bold]{found - downloaded}[/] files")
-
-
-def load_reproduction_information(path: str) -> dict[str, Any]:
-    if path.lower().startswith(("http://", "https://")):
-        # The path is a URL on the web.
-
-        # Obtain raw download URL.
-        path = path.replace("/blob/", "/raw/")  # Hugging Face, GitHub
-        path = path.replace("/src/branch/", "/raw/branch/")  # Codeberg
-
-        json_str = urlopen(path).read().decode("utf-8")
-    else:
-        # The path is (assumed to be) a local file system path.
-        json_str = Path(path).read_text(encoding="utf-8")
-
-    return json.loads(json_str)
-
-
-class MismatchSeverity(IntEnum):
-    LOW = 1
-    MEDIUM = 2
-    HIGH = 3
-    CRITICAL = 4
-
-    def __rich__(self) -> str:
-        match self:
-            case MismatchSeverity.LOW:
-                return "[green]low[/]"
-            case MismatchSeverity.MEDIUM:
-                return "[yellow]medium[/]"
-            case MismatchSeverity.HIGH:
-                return "[red]high[/]"
-            case MismatchSeverity.CRITICAL:
-                return "[bold red]critical[/]"
-            case _:
-                raise ValueError(f"unknown MismatchSeverity value: {self}")
-
-
-def get_package_mismatch_severity(package_name: str) -> MismatchSeverity:
-    if package_name in [
-        "heretic-llm",
-    ]:
-        return MismatchSeverity.CRITICAL
-    elif package_name in [
-        "torch",
-        "transformers",
-    ]:
-        return MismatchSeverity.HIGH
-    elif package_name in [
-        "accelerate",
-        "bitsandbytes",
-        "kernels",
-        "optuna",
-        "peft",
-        "tokenizers",
-        "triton",
-    ]:
-        return MismatchSeverity.MEDIUM
-    else:
-        return MismatchSeverity.LOW
-
-
-def format_version_information(version_information: dict[str, Any]) -> str:
-    version = version_information["version"]
-    metadata = version_information["metadata"]
-
-    if "type" in metadata:
-        match metadata["type"]:
-            case "pypi":
-                return version
-            case "git":
-                return f"{version}-git+{metadata['url']}@{metadata['commit_hash']}"
-            case "local":
-                # Append a random number to ensure that two local installations
-                # are always considered to be different versions.
-                return f"{version}-local-{random.randint(2**16, 2**17)}"
-            case _:
-                raise ValueError(
-                    f"unknown metadata.type value in version information: {metadata['type']}"
-                )
-    else:
-        return f"{version}-unknown-{random.randint(2**16, 2**17)}"
-
-
-def check_environment(reproduction_information: dict[str, Any]) -> bool:
-    mismatch_severity: MismatchSeverity | None = None
-
-    system_mismatches = []
-    package_mismatches = []
-
-    def verify(
-        mismatch_list: list[tuple[str, Any, Any, MismatchSeverity]],
-        name: str,
-        this: Any,
-        original: Any,
-        severity: MismatchSeverity,
-    ):
-        nonlocal mismatch_severity
-        if this != original:
-            mismatch_list.append((name, this, original, severity))
-            if mismatch_severity is None:
-                mismatch_severity = severity
-            else:
-                mismatch_severity = max(severity, mismatch_severity)
-
-    if "system" in reproduction_information:
-        system = reproduction_information["system"]
-
-        verify(
-            system_mismatches,
-            "Python version",
-            platform.python_version(),
-            system["python"]["version"],
-            MismatchSeverity.LOW,
-        )
-
-        verify(
-            system_mismatches,
-            "Operating system",
-            platform.platform(),
-            system["os"]["platform"],
-            MismatchSeverity.LOW,
-        )
-
-        verify(
-            system_mismatches,
-            "CPU",
-            cpuinfo.get_cpu_info().get("brand_raw"),
-            system["cpu"]["brand"],
-            MismatchSeverity.LOW,
-        )
-
-        accelerators = get_accelerator_info_dict()
-
-        verify(
-            system_mismatches,
-            "Accelerator type",
-            accelerators["type"],
-            system["accelerators"]["type"],
-            MismatchSeverity.HIGH,
-        )
-
-        if (
-            accelerators["type"]
-            and accelerators["type"] == system["accelerators"]["type"]
-        ):
-            verify(
-                system_mismatches,
-                accelerators["api_name"],
-                accelerators["api_version"],
-                system["accelerators"]["api_version"],
-                MismatchSeverity.MEDIUM,
-            )
-            verify(
-                system_mismatches,
-                "Driver version",
-                accelerators["driver_version"],
-                system["accelerators"]["driver_version"],
-                MismatchSeverity.MEDIUM,
-            )
-            verify(
-                system_mismatches,
-                "Devices",
-                "\n".join([device["name"] for device in accelerators["devices"]]),
-                "\n".join(
-                    [device["name"] for device in system["accelerators"]["devices"]]
-                ),
-                MismatchSeverity.MEDIUM,
-            )
-
-    else:
-        print(
-            (
-                "[yellow]The provided JSON file does not contain system information. "
-                "Some system parameters can affect reproducibility, but due to the lack of system information, "
-                "Heretic is unable to verify that those parameters match the original environment. "
-                "Reproduction may or may not produce a byte-for-byte identical model.[/]"
-            )
-        )
-
-    requirements = get_requirements_dict()
-    requirements["heretic-llm"] = format_version_information(
-        asdict(get_heretic_version_info())
-    )
-    requirements["torch"] = torch.__version__
-
-    original_requirements = reproduction_information["environment"]["requirements"]
-    original_requirements["heretic-llm"] = format_version_information(
-        reproduction_information["environment"]["heretic"]
-    )
-    original_requirements["torch"] = reproduction_information["environment"][
-        "pytorch_version"
-    ]
-
-    package_names = sorted(requirements.keys() | original_requirements.keys())
-
-    for package_name in package_names:
-        verify(
-            package_mismatches,
-            package_name,
-            requirements.get(package_name),
-            original_requirements.get(package_name),
-            get_package_mismatch_severity(package_name),
-        )
-
-    if system_mismatches or package_mismatches:
-        print()
-        print(
-            (
-                "[yellow]Your local environment doesn't perfectly match the environment "
-                "used to produce the original model. The following components differ:[/]"
-            )
-        )
-
-    if system_mismatches:
-        table = Table()
-        table.add_column("Component")
-        table.add_column("This system", overflow="fold")
-        table.add_column("Original system", overflow="fold")
-        table.add_column("Severity", width=8)
-
-        for component, this, original, severity in system_mismatches:
-            table.add_row(f"[bold]{component}[/]", this, original, severity)
-
-        print()
-        print("[bold]System Mismatches[/]")
-        print(table)
-
-    if package_mismatches:
-        table = Table()
-        table.add_column("Package")
-        table.add_column("This system", overflow="fold")
-        table.add_column("Original system", overflow="fold")
-        table.add_column("Severity", width=8)
-
-        for package, this, original, severity in package_mismatches:
-            table.add_row(f"[bold]{package}[/]", this, original, severity)
-
-        print()
-        print("[bold]Package Mismatches[/]")
-        print(table)
-
-    if system_mismatches or package_mismatches:
-        print()
-        print(
-            (
-                f"There is a {cast(MismatchSeverity, mismatch_severity).__rich__()} chance "
-                "that reproduction won't produce a byte-for-byte identical model. "
-                "However, the resulting model will very likely still behave similarly "
-                "to the original model."
-            )
-        )
-
-        print()
-        choice = prompt_select(
-            "How would you like to proceed?",
-            [
-                Choice(
-                    title="Attempt to reproduce the model anyway",
-                    value=True,
-                ),
-                Choice(
-                    title="Exit program",
-                    value=False,
-                ),
-            ],
-        )
-
-        return choice
-    else:
-        # There are no mismatches at all, so there is nothing to confirm.
-        return True

src/heretic/utils.py

@@ -2,16 +2,13 @@
 # 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
 
@@ -24,9 +21,7 @@ from datasets import DatasetDict, ReadInstruction, load_dataset, load_from_disk
 from datasets.config import DATASET_STATE_JSON_FILENAME
 from datasets.download.download_manager import DownloadMode
 from datasets.utils.info_utils import VerificationMode
-from huggingface_hub.utils import validate_repo_id
 from optuna import Trial
-from optuna.trial import FrozenTrial
 from psutil import Process
 from questionary import Choice, Style
 from rich.console import Console
@@ -173,29 +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 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
@@ -204,23 +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,
@@ -228,41 +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(
@@ -274,7 +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]
@@ -304,7 +257,7 @@ def batchify(items: list[T], batch_size: int) -> list[list[T]]:
     return [items[i : i + batch_size] for i in range(0, len(items), batch_size)]
 
 
-def get_trial_parameters(trial: Trial | FrozenTrial) -> dict[str, str]:
+def get_trial_parameters(trial: Trial) -> dict[str, str]:
     params = {}
 
     direction_index = trial.user_attrs["direction_index"]
@@ -321,7 +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):
@@ -330,6 +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]
@@ -342,7 +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
 
@@ -413,7 +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."""
@@ -565,18 +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.
@@ -587,7 +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,
@@ -597,7 +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": {
@@ -651,23 +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,
 ):
@@ -741,7 +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()

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]]
@@ -931,14 +931,16 @@ wheels = [
 
 [[package]]
 name = "heretic-llm"
-version = "1.4.0"
+version = "1.2.0"
 source = { editable = "." }
 dependencies = [
     { name = "accelerate" },
     { name = "bitsandbytes" },
     { name = "datasets" },
+    { name = "hf-transfer" },
     { name = "huggingface-hub" },
     { name = "immutabledict" },
+    { name = "kernels" },
     { name = "langdetect" },
     { name = "lm-eval", extra = ["hf"] },
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
@@ -952,7 +954,7 @@ dependencies = [
     { name = "rich" },
     { name = "tomli-w" },
     { name = "tqdm" },
-    { name = "transformers", extra = ["kernels"] },
+    { name = "transformers" },
 ]
 
 [package.optional-dependencies]
@@ -977,16 +979,18 @@ requires-dist = [
     { name = "bitsandbytes", specifier = "~=0.49" },
     { name = "datasets", specifier = "~=4.7" },
     { name = "geom-median", marker = "extra == 'research'", specifier = "~=0.1" },
+    { name = "hf-transfer", specifier = "~=0.1" },
     { name = "huggingface-hub", specifier = "~=1.7" },
     { name = "imageio", marker = "extra == 'research'", specifier = "~=2.37" },
     { name = "immutabledict", specifier = "~=4.3" },
+    { name = "kernels", specifier = "~=0.12" },
     { name = "langdetect", specifier = "~=1.0" },
     { name = "lm-eval", extras = ["hf"], specifier = "~=0.4" },
     { name = "matplotlib", marker = "extra == 'research'", specifier = "~=3.10" },
     { name = "numpy", specifier = "~=2.2" },
     { name = "optuna", specifier = "~=4.7" },
     { name = "pacmap", marker = "extra == 'research'", specifier = "~=0.8" },
-    { name = "peft", specifier = "~=0.19" },
+    { name = "peft", specifier = "~=0.18" },
     { name = "psutil", specifier = "~=7.2" },
     { name = "py-cpuinfo", specifier = "~=9.0" },
     { name = "pydantic-settings", specifier = "~=2.13" },
@@ -995,7 +999,7 @@ requires-dist = [
     { name = "scikit-learn", marker = "extra == 'research'", specifier = "~=1.7" },
     { name = "tomli-w", specifier = "~=1.2" },
     { name = "tqdm", specifier = "~=4.67" },
-    { name = "transformers", extras = ["kernels"], specifier = "~=5.6" },
+    { name = "transformers", specifier = "~=5.3" },
 ]
 provides-extras = ["research"]
 
@@ -1005,6 +1009,38 @@ dev = [
     { name = "ty", specifier = ">=0.0.5" },
 ]
 
+[[package]]
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+version = "0.1.9"
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