fix: fix remaining issues

Bumps [python-dotenv](https://github.com/theskumar/python-dotenv) from 1.2.1 to 1.2.2.
- [Release notes](https://github.com/theskumar/python-dotenv/releases)
- [Changelog](https://github.com/theskumar/python-dotenv/blob/main/CHANGELOG.md)
- [Commits](https://github.com/theskumar/python-dotenv/compare/v1.2.1...v1.2.2)

---
updated-dependencies:
- dependency-name: python-dotenv
  dependency-version: 1.2.2
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

config.default.toml

@@ -25,7 +25,7 @@ quantization = "none"
 device_map = "auto"
 
 # Maximum memory to allocate per device.
-# max_memory = {"0": "20GB", "cpu": "64GB"}
+# max_memory = { "0" = "20GB", "cpu" = "64GB" }
 
 # Number of input sequences to process in parallel (0 = auto).
 batch_size = 0  # auto
@@ -91,6 +91,10 @@ 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"
 
@@ -133,6 +137,12 @@ refusal_markers = [
 # System prompt to use when prompting the model.
 system_prompt = "You are a helpful assistant."
 
+# Move intermediate analysis tensors (such as residuals and logprobs)
+# to CPU memory as soon as possible to reduce peak VRAM usage.
+# This lowers peak VRAM usage during residual analysis and evaluation,
+# but may slightly reduce performance due to host/device transfers.
+offload_outputs_to_cpu = true
+
 # Dataset of prompts that tend to not result in refusals (used for calculating refusal directions).
 [good_prompts]
 dataset = "mlabonne/harmless_alpaca"

pyproject.toml

@@ -35,9 +35,11 @@ dependencies = [
     "optuna~=4.7",
     "peft~=0.18",
     "psutil~=7.2",
+    "py-cpuinfo~=9.0",
     "pydantic-settings~=2.13",
     "questionary~=2.1",
     "rich~=14.3",
+    "tomli-w~=1.2",
     "tqdm~=4.67",
     "transformers~=5.3",
 ]
@@ -71,5 +73,8 @@ heretic = "heretic.main:main"
 requires = ["uv_build>=0.8.11,<0.9.0"]
 build-backend = "uv_build"
 
+[tool.uv]
+exclude-newer = "7 days"
+
 [tool.uv.build-backend]
 module-name = "heretic"

src/heretic/config.py

@@ -13,6 +13,12 @@ from pydantic_settings import (
     TomlConfigSettingsSource,
 )
 
+# !!!IMPORTANT!!!
+#
+# Any settings added to the classes defined in this module
+# must be evaluated for privacy implications and have
+# exclude=True set in their field definitions if appropriate.
+
 
 class QuantizationMethod(str, Enum):
     NONE = "none"
@@ -31,6 +37,11 @@ class DatasetSpecification(BaseModel):
         description="Hugging Face dataset ID, or path to dataset on disk."
     )
 
+    commit: str | None = Field(
+        default=None,
+        description="Hugging Face commit hash of the dataset.",
+    )
+
     split: str = Field(description="Portion of the dataset to use.")
 
     column: str = Field(description="Column in the dataset that contains the prompts.")
@@ -53,11 +64,13 @@ class DatasetSpecification(BaseModel):
     residual_plot_label: str | None = Field(
         default=None,
         description="Label to use for the dataset in plots of residual vectors.",
+        exclude=True,
     )
 
     residual_plot_color: str | None = Field(
         default=None,
         description="Matplotlib color to use for the dataset in plots of residual vectors.",
+        exclude=True,
     )
 
 
@@ -76,12 +89,18 @@ class BenchmarkSpecification(BaseModel):
 class Settings(BaseSettings):
     model: str = Field(description="Hugging Face model ID, or path to model on disk.")
 
+    model_commit: str | None = Field(
+        default=None,
+        description="Hugging Face commit hash of the model.",
+    )
+
     evaluate_model: str | None = Field(
         default=None,
         description=(
             "If this model ID or path is set, then instead of abliterating the main model, "
             "evaluate this model relative to the main model."
         ),
+        exclude=True,
     )
 
     dtypes: list[str] = Field(
@@ -119,12 +138,14 @@ class Settings(BaseSettings):
 
     max_memory: Dict[str, str] | None = Field(
         default=None,
-        description='Maximum memory to allocate per device (e.g., {"0": "20GB", "cpu": "64GB"}).',
+        description='Maximum memory to allocate per device (e.g., { "0" = "20GB", "cpu" = "64GB" }).',
     )
 
     trust_remote_code: bool | None = Field(
         default=None,
         description="Whether to trust remote code when loading the model.",
+        # For security reasons, we don't store this setting.
+        exclude=True,
     )
 
     batch_size: int = Field(
@@ -135,6 +156,9 @@ class Settings(BaseSettings):
     max_batch_size: int = Field(
         default=128,
         description="Maximum batch size to try when automatically determining the optimal batch size.",
+        # When storing a settings object, the batch size is already fixed,
+        # either determined by the automatic mechanism or by explicit user choice.
+        exclude=True,
     )
 
     max_response_length: int = Field(
@@ -142,34 +166,82 @@ class Settings(BaseSettings):
         description="Maximum number of tokens to generate for each response.",
     )
 
+    response_prefix: str | None = Field(
+        default=None,
+        description=(
+            "Common prefix to assume for all responses, so that evaluation happens "
+            "at the point where responses start to differ for different prompts. "
+            "If not set, the prefix is determined automatically by comparing multiple responses."
+        ),
+    )
+
+    chain_of_thought_skips: list[tuple[str, str]] = Field(
+        default=[
+            # Most thinking models.
+            (
+                "<think>",
+                "<think></think>",
+            ),
+            # gpt-oss.
+            (
+                "<|channel|>analysis<|message|>",
+                "<|channel|>analysis<|message|><|end|><|start|>assistant<|channel|>final<|message|>",
+            ),
+            # Unknown, suggested by user.
+            (
+                "<thought>",
+                "<thought></thought>",
+            ),
+            # Unknown, suggested by user.
+            (
+                "[THINK]",
+                "[THINK][/THINK]",
+            ),
+        ],
+        description=(
+            "List of pairs of the form (cot_initializer, closed_cot_block) used to skip "
+            "the Chain-of-Thought block in responses, so that evaluation happens "
+            "at the start of the actual response."
+        ),
+        # When storing a settings object, the response prefix is already fixed,
+        # either determined by the automatic mechanism or by explicit user choice.
+        exclude=True,
+    )
+
     print_responses: bool = Field(
         default=False,
         description="Whether to print prompt/response pairs when counting refusals.",
+        exclude=True,
     )
 
     print_residual_geometry: bool = Field(
         default=False,
         description="Whether to print detailed information about residuals and refusal directions.",
+        exclude=True,
     )
 
     plot_residuals: bool = Field(
         default=False,
         description="Whether to generate plots showing PaCMAP projections of residual vectors.",
+        exclude=True,
     )
 
     residual_plot_path: str = Field(
         default="plots",
         description="Base path to save plots of residual vectors to.",
+        exclude=True,
     )
 
     residual_plot_title: str = Field(
         default='PaCMAP Projection of Residual Vectors for "Harmless" and "Harmful" Prompts',
         description="Title placed above plots of residual vectors.",
+        exclude=True,
     )
 
     residual_plot_style: str = Field(
         default="dark_background",
         description="Matplotlib style sheet to use for plots of residual vectors.",
+        exclude=True,
     )
 
     kl_divergence_scale: float = Field(
@@ -188,42 +260,6 @@ class Settings(BaseSettings):
         ),
     )
 
-    target_components: list[str] = Field(
-        default=["attn.o_proj", "mlp.down_proj"],
-        description=(
-            "List of component names to target for abliteration. "
-            'Currently supported values are "attn.o_proj" and "mlp.down_proj".'
-        ),
-    )
-
-    use_ara: bool = Field(
-        default=True,
-        description=(
-            "Whether to use Arbitrary-Rank Ablation (ARA), an abliteration method based on matrix optimization, "
-            "instead of traditional directional ablation."
-        ),
-    )
-    
-    use_ara_lora: bool = Field(
-        default=False,
-        description=(
-            "Use LoRA in ARA instead of full-weight editing. Makes it compatible with quantization and removes model reloads."
-        ),
-    )
-    
-    ara_lora_rank: int = Field(
-        default=128,
-        description="If LoRA is used in ARA, this sets up its rank. Keep it high enough to simulate the 'arbitrary' effect.",
-    )
-
-    use_piqa: bool = Field(
-        default=False,
-        description=(
-            "Whether to use the Physical Interaction: Question Answering (PIQA) benchmark "
-            "as the quality metric instead of the Kullback-Leibler divergence."
-        ),
-    )
-
     orthogonalize_direction: bool = Field(
         default=False,
         description=(
@@ -233,7 +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), '
@@ -273,9 +309,18 @@ class Settings(BaseSettings):
         description="Number of trials that use random sampling for the purpose of exploration.",
     )
 
+    seed: int | None = Field(
+        default=None,
+        description=(
+            "Random seed for reproducible optimization. "
+            "Applies to Python's random module, NumPy, PyTorch, and Optuna."
+        ),
+    )
+
     study_checkpoint_dir: str = Field(
         default="checkpoints",
         description="Directory to save and load study progress to/from.",
+        exclude=True,
     )
 
     benchmarks: list[BenchmarkSpecification] = Field(
@@ -337,6 +382,12 @@ class Settings(BaseSettings):
             ),
         ],
         description="Benchmarks to offer to the user for evaluating abliterated models.",
+        exclude=True,
+    )
+
+    max_shard_size: int | str = Field(
+        default="5GB",
+        description="Maximum size for individual safetensors files generated when exporting a model.",
     )
 
     refusal_markers: list[str] = Field(
@@ -382,6 +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/evaluator.py

@@ -1,9 +1,7 @@
 # SPDX-License-Identifier: AGPL-3.0-or-later
 # Copyright (C) 2025-2026  Philipp Emanuel Weidmann <pew@worldwidemann.com> + contributors
 
-import lm_eval
 import torch.nn.functional as F
-from lm_eval.models.huggingface import HFLM
 from torch import Tensor
 
 from .config import Settings
@@ -23,16 +21,15 @@ class Evaluator:
         self.settings = settings
         self.model = model
 
-        if not settings.use_piqa:
-            print()
-            print(
-                f"Loading good evaluation prompts from [bold]{settings.good_evaluation_prompts.dataset}[/]..."
-            )
-            self.good_prompts = load_prompts(settings, settings.good_evaluation_prompts)
-            print(f"* [bold]{len(self.good_prompts)}[/] prompts loaded")
+        print()
+        print(
+            f"Loading good evaluation prompts from [bold]{settings.good_evaluation_prompts.dataset}[/]..."
+        )
+        self.good_prompts = load_prompts(settings, settings.good_evaluation_prompts)
+        print(f"* [bold]{len(self.good_prompts)}[/] prompts loaded")
 
-            print("* Obtaining first-token probability distributions...")
-            self.base_logprobs = model.get_logprobs_batched(self.good_prompts)
+        print("* Obtaining first-token probability distributions...")
+        self.base_logprobs = model.get_logprobs_batched(self.good_prompts)
 
         print()
         print(
@@ -96,57 +93,35 @@ class Evaluator:
         return refusal_count
 
     def get_score(self) -> tuple[tuple[float, float], float, int]:
-        if self.settings.use_piqa:
-            print("  * Running PIQA benchmark...")
-            hflm = HFLM(
-                pretrained=self.model.model,  # ty:ignore[invalid-argument-type]
-                tokenizer=self.model.tokenizer,  # ty:ignore[invalid-argument-type]
-                batch_size="auto",
-            )
-            results = lm_eval.simple_evaluate(
-                model=hflm,
-                tasks=["piqa"],
-            )
-            piqa_acc_norm: float = results["results"]["piqa"]["acc_norm,none"]
-            print(f"  * PIQA acc_norm: [bold]{piqa_acc_norm:.4f}[/]")
-        else:
-            print("  * Obtaining first-token probability distributions...")
-            logprobs = self.model.get_logprobs_batched(self.good_prompts)
-            kl_divergence = F.kl_div(
-                logprobs,
-                self.base_logprobs,
-                reduction="batchmean",
-                log_target=True,
-            ).item()
-            print(f"  * KL divergence: [bold]{kl_divergence:.4f}[/]")
+        print("  * Obtaining first-token probability distributions...")
+        logprobs = self.model.get_logprobs_batched(self.good_prompts)
+        kl_divergence = F.kl_div(
+            logprobs,
+            self.base_logprobs,
+            reduction="batchmean",
+            log_target=True,
+        ).item()
+        print(f"  * KL divergence: [bold]{kl_divergence:.4f}[/]")
 
         print("  * Counting model refusals...")
         refusals = self.count_refusals()
         print(f"  * Refusals: [bold]{refusals}[/]/{len(self.bad_prompts)}")
 
+        kl_divergence_scale = self.settings.kl_divergence_scale
+        kl_divergence_target = self.settings.kl_divergence_target
+
         refusals_score = (
             refusals / self.base_refusals if self.base_refusals > 0 else float(refusals)
         )
 
-        if self.settings.use_piqa:
-            score = (
-                -piqa_acc_norm,
-                refusals_score,
-            )
-
-            return score, -piqa_acc_norm, refusals
+        if kl_divergence >= kl_divergence_target:
+            kld_score = kl_divergence / kl_divergence_scale
         else:
-            kl_divergence_scale = self.settings.kl_divergence_scale
-            kl_divergence_target = self.settings.kl_divergence_target
+            kld_score = refusals_score * kl_divergence_target / kl_divergence_scale
 
-            if kl_divergence >= kl_divergence_target:
-                kld_score = kl_divergence / kl_divergence_scale
-            else:
-                kld_score = refusals_score * kl_divergence_target / kl_divergence_scale
+        score = (
+            kld_score,
+            refusals_score,
+        )
 
-            score = (
-                kld_score,
-                refusals_score,
-            )
-
-            return score, kl_divergence, refusals
+        return score, kl_divergence, refusals

src/heretic/main.py

@@ -3,6 +3,20 @@
 
 # ruff: noqa: E402
 
+import sys
+
+from .config import Settings
+
+
+def _is_help_invocation() -> bool:
+    args = sys.argv[1:]
+    return "-h" in args or "--help" in args
+
+
+# Parse and handle CLI help before importing heavyweight ML/runtime dependencies.
+if _is_help_invocation():
+    Settings()  # ty:ignore[missing-argument]
+
 from .progress import patch_tqdm
 
 # This patches tqdm class definitions, which must happen
@@ -12,7 +26,7 @@ patch_tqdm()
 import logging
 import math
 import os
-import sys
+import random
 import time
 import warnings
 from dataclasses import asdict
@@ -29,13 +43,6 @@ import questionary
 import torch
 import torch.nn.functional as F
 import transformers
-from accelerate.utils import (
-    is_mlu_available,
-    is_musa_available,
-    is_npu_available,
-    is_sdaa_available,
-    is_xpu_available,
-)
 from huggingface_hub import ModelCard, ModelCardData
 from lm_eval.models.huggingface import HFLM
 from optuna import Trial, TrialPruned
@@ -51,14 +58,15 @@ from rich.table import Table
 from rich.traceback import install
 
 from .analyzer import Analyzer
-from .config import QuantizationMethod, RowNormalization, Settings
+from .config import QuantizationMethod
 from .evaluator import Evaluator
-from .model import AbliterationParameters, ARAParameters, Model, get_model_class
+from .model import AbliterationParameters, Model, get_model_class
+from .system import empty_cache, get_accelerator_info
 from .utils import (
-    empty_cache,
     format_duration,
     get_readme_intro,
     get_trial_parameters,
+    is_hf_path,
     load_prompts,
     print,
     print_memory_usage,
@@ -66,10 +74,12 @@ from .utils import (
     prompt_path,
     prompt_select,
     prompt_text,
+    set_seed,
+    upload_reproduce_folder,
 )
 
 
-def obtain_merge_strategy(settings: Settings) -> str | None:
+def obtain_merge_strategy(settings: Settings, model: Model) -> str | None:
     """
     Prompts the user for how to proceed with saving the model.
     Provides info to the user if the model is quantized on memory use.
@@ -98,7 +108,8 @@ def obtain_merge_strategy(settings: Settings) -> str | None:
                     settings.model,
                     device_map="meta",
                     torch_dtype=torch.bfloat16,
-                    trust_remote_code=True,
+                    trust_remote_code=model.trusted_models.get(settings.model),
+                    **model.revision_kwargs,
                 )
                 footprint_bytes = meta_model.get_memory_footprint()
                 footprint_gb = footprint_bytes / (1024**3)
@@ -186,50 +197,15 @@ def run():
         )
         return
 
-    # Adapted from https://github.com/huggingface/accelerate/blob/main/src/accelerate/commands/env.py
-    if torch.cuda.is_available():
-        count = torch.cuda.device_count()
-        total_vram = sum(torch.cuda.mem_get_info(i)[1] for i in range(count))
-        print(
-            f"Detected [bold]{count}[/] CUDA device(s) ({total_vram / (1024**3):.2f} GB total VRAM):"
-        )
-        for i in range(count):
-            vram = torch.cuda.mem_get_info(i)[1] / (1024**3)
-            print(
-                f"* GPU {i}: [bold]{torch.cuda.get_device_name(i)}[/] ({vram:.2f} GB)"
-            )
-    elif is_xpu_available():
-        count = torch.xpu.device_count()
-        print(f"Detected [bold]{count}[/] XPU device(s):")
-        for i in range(count):
-            print(f"* XPU {i}: [bold]{torch.xpu.get_device_name(i)}[/]")
-    elif is_mlu_available():
-        count = torch.mlu.device_count()  # ty:ignore[unresolved-attribute]
-        print(f"Detected [bold]{count}[/] MLU device(s):")
-        for i in range(count):
-            print(f"* MLU {i}: [bold]{torch.mlu.get_device_name(i)}[/]")  # ty:ignore[unresolved-attribute]
-    elif is_sdaa_available():
-        count = torch.sdaa.device_count()  # ty:ignore[unresolved-attribute]
-        print(f"Detected [bold]{count}[/] SDAA device(s):")
-        for i in range(count):
-            print(f"* SDAA {i}: [bold]{torch.sdaa.get_device_name(i)}[/]")  # ty:ignore[unresolved-attribute]
-    elif is_musa_available():
-        count = torch.musa.device_count()  # ty:ignore[unresolved-attribute]
-        print(f"Detected [bold]{count}[/] MUSA device(s):")
-        for i in range(count):
-            print(f"* MUSA {i}: [bold]{torch.musa.get_device_name(i)}[/]")  # ty:ignore[unresolved-attribute]
-    elif is_npu_available():
-        print(f"NPU detected (CANN version: [bold]{torch.version.cann}[/])")  # ty:ignore[unresolved-attribute]
-    elif torch.backends.mps.is_available():
-        print("Detected [bold]1[/] MPS device (Apple Metal)")
-    else:
-        print(
-            "[bold yellow]No GPU or other accelerator detected. Operations will be slow.[/]"
-        )
+    if settings.seed is None:
+        settings.seed = random.randint(0, 2**32 - 1)
 
-    if not settings.use_ara:
-        # We don't need gradients as we only do inference.
-        torch.set_grad_enabled(False)
+    set_seed(settings.seed)
+
+    print(get_accelerator_info())
+
+    # We don't need gradients as we only do inference.
+    torch.set_grad_enabled(False)
 
     # While determining the optimal batch size, we will try many different batch sizes,
     # resulting in many computation graphs being compiled. Raising the limit (default = 8)
@@ -394,52 +370,44 @@ def run():
         settings.batch_size = best_batch_size
         print(f"* Chosen batch size: [bold]{settings.batch_size}[/]")
 
-    print()
-    print("Checking for common response prefix...")
-    prefix_check_prompts = good_prompts[:100] + bad_prompts[:100]
-    responses = model.get_responses_batched(prefix_check_prompts)
-
-    # Despite being located in os.path, commonprefix actually performs
-    # a naive string operation without any path-specific logic,
-    # which is exactly what we need here. Trailing spaces are removed
-    # to avoid issues where multiple different tokens that all start
-    # with a space character lead to the common prefix ending with
-    # a space, which would result in an uncommon tokenization.
-    model.response_prefix = commonprefix(responses).rstrip(" ")
-
-    # Suppress CoT output.
-    recheck_prefix = False
-    if model.response_prefix:
-        # When using any of the predefined prefixes below, we need to check that
-        # the prefix is actually complete (e.g. not missing a trailing newline).
-        recheck_prefix = True
-        if model.response_prefix.startswith("<think>"):
-            # Most thinking models.
-            model.response_prefix = "<think></think>"
-        elif model.response_prefix.startswith("<|channel|>analysis<|message|>"):
-            # gpt-oss.
-            model.response_prefix = "<|channel|>analysis<|message|><|end|><|start|>assistant<|channel|>final<|message|>"
-        elif model.response_prefix.startswith("<thought>"):
-            # Unknown, suggested by user.
-            model.response_prefix = "<thought></thought>"
-        elif model.response_prefix.startswith("[THINK]"):
-            # Unknown, suggested by user.
-            model.response_prefix = "[THINK][/THINK]"
-        else:
-            recheck_prefix = False
-
-    if model.response_prefix:
-        print(f"* Prefix found: [bold]{model.response_prefix!r}[/]")
-    else:
-        print("* None found")
-
-    if recheck_prefix:
-        print("* Rechecking with prefix...")
+    if settings.response_prefix is None:
+        print()
+        print("Checking for common response prefix...")
+        prefix_check_prompts = good_prompts[:100] + bad_prompts[:100]
         responses = model.get_responses_batched(prefix_check_prompts)
-        additional_prefix = commonprefix(responses).rstrip(" ")
-        if additional_prefix:
-            model.response_prefix += additional_prefix
-            print(f"* Extended prefix found: [bold]{model.response_prefix!r}[/]")
+
+        # Despite being located in os.path, commonprefix actually performs
+        # a naive string operation without any path-specific logic,
+        # which is exactly what we need here. Trailing spaces are removed
+        # to avoid issues where multiple different tokens that all start
+        # with a space character lead to the common prefix ending with
+        # a space, which would result in an uncommon tokenization.
+        settings.response_prefix = commonprefix(responses).rstrip(" ")
+
+        if settings.response_prefix:
+            print(f"* Prefix found: [bold]{settings.response_prefix!r}[/]")
+
+            for cot_initializer, closed_cot_block in settings.chain_of_thought_skips:
+                if settings.response_prefix.startswith(cot_initializer):
+                    settings.response_prefix = closed_cot_block
+                    print(
+                        f"* Closed Chain-of-Thought block: [bold]{settings.response_prefix!r}[/]"
+                    )
+
+                    # When using a Chain-of-Thought skip, we need to check that the prefix
+                    # is actually complete (e.g. not missing a trailing newline).
+                    print("* Rechecking with prefix...")
+                    responses = model.get_responses_batched(prefix_check_prompts)
+                    additional_prefix = commonprefix(responses).rstrip(" ")
+                    if additional_prefix:
+                        settings.response_prefix += additional_prefix
+                        print(
+                            f"* Extended prefix found: [bold]{settings.response_prefix!r}[/]"
+                        )
+
+                    break
+        else:
+            print("* None found")
 
     evaluator = Evaluator(settings, model)
 
@@ -452,15 +420,15 @@ def run():
         evaluator.get_score()
         return
 
-    if settings.use_ara:
-        print()
-        print("Obtaining module I/O for good prompts...")
-        good_module_io = model.get_module_io_batched(good_prompts)
-        print("Obtaining module I/O for bad prompts...")
-        bad_module_io = model.get_module_io_batched(bad_prompts)
-    else:
-        print()
-        print("Calculating per-layer refusal directions...")
+    print()
+    print("Calculating per-layer refusal directions...")
+
+    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)
         print("* Obtaining residuals for bad prompts...")
@@ -469,19 +437,6 @@ def run():
         good_means = good_residuals.mean(dim=0)
         bad_means = bad_residuals.mean(dim=0)
 
-        refusal_directions = F.normalize(bad_means - good_means, p=2, dim=1)
-
-        if settings.orthogonalize_direction:
-            # Implements https://huggingface.co/blog/grimjim/projected-abliteration
-            # Adjust the refusal directions so that only the component that is
-            # orthogonal to the good direction is subtracted during abliteration.
-            good_directions = F.normalize(good_means, p=2, dim=1)
-            projection_vector = torch.sum(refusal_directions * good_directions, dim=1)
-            refusal_directions = (
-                refusal_directions - projection_vector.unsqueeze(1) * good_directions
-            )
-            refusal_directions = F.normalize(refusal_directions, p=2, dim=1)
-
         analyzer = Analyzer(settings, model, good_residuals, bad_residuals)
 
         if settings.print_residual_geometry:
@@ -490,9 +445,29 @@ def run():
         if settings.plot_residuals:
             analyzer.plot_residuals()
 
-        # We don't need the residuals after computing refusal directions.
+        # We don't need the full residuals after computing their means and analyzing geometry.
         del good_residuals, bad_residuals, analyzer
-        empty_cache()
+    else:
+        print("* Obtaining residual mean for good prompts...")
+        good_means = model.get_residuals_mean(good_prompts)
+        print("* Obtaining residual mean for bad prompts...")
+        bad_means = model.get_residuals_mean(bad_prompts)
+
+    refusal_directions = F.normalize(bad_means - good_means, p=2, dim=1)
+
+    if settings.orthogonalize_direction:
+        # Implements https://huggingface.co/blog/grimjim/projected-abliteration
+        # Adjust the refusal directions so that only the component that is
+        # orthogonal to the good direction is subtracted during abliteration.
+        good_directions = F.normalize(good_means, p=2, dim=1)
+        projection_vector = torch.sum(refusal_directions * good_directions, dim=1)
+        refusal_directions = (
+            refusal_directions - projection_vector.unsqueeze(1) * good_directions
+        )
+        refusal_directions = F.normalize(refusal_directions, p=2, dim=1)
+
+    # Clear cache before starting the optimization study.
+    empty_cache()
 
     trial_index = 0
     start_index = 0
@@ -503,144 +478,83 @@ def run():
         trial_index += 1
         trial.set_user_attr("index", trial_index)
 
-        if settings.use_ara:
-            start_layer_index = trial.suggest_int(
-                "start_layer_index",
-                0,
-                len(model.get_layers()) // 2,
+        direction_scope = trial.suggest_categorical(
+            "direction_scope",
+            [
+                "global",
+                "per layer",
+            ],
+        )
+
+        last_layer_index = len(model.get_layers()) - 1
+
+        # Discrimination between "harmful" and "harmless" inputs is usually strongest
+        # in layers slightly past the midpoint of the layer stack. See the original
+        # abliteration paper (https://arxiv.org/abs/2406.11717) for a deeper analysis.
+        #
+        # Note that we always sample this parameter even though we only need it for
+        # the "global" direction scope. The reason is that multivariate TPE doesn't
+        # work with conditional or variable-range parameters.
+        direction_index = trial.suggest_float(
+            "direction_index",
+            0.4 * last_layer_index,
+            0.9 * last_layer_index,
+        )
+
+        if direction_scope == "per layer":
+            direction_index = None
+
+        parameters = {}
+
+        for component in model.get_abliterable_components():
+            # The parameter ranges are based on experiments with various models
+            # and much wider ranges. They are not set in stone and might have to be
+            # adjusted for future models.
+            max_weight = trial.suggest_float(
+                f"{component}.max_weight",
+                0.8,
+                1.5,
             )
-            end_layer_index = trial.suggest_int(
-                "end_layer_index",
-                len(model.get_layers()) // 2,
-                len(model.get_layers()),
+            max_weight_position = trial.suggest_float(
+                f"{component}.max_weight_position",
+                0.6 * last_layer_index,
+                1.0 * last_layer_index,
             )
-            preserve_good_behavior_weight = trial.suggest_float(
-                "preserve_good_behavior_weight",
+            # For sampling purposes, min_weight is expressed as a fraction of max_weight,
+            # again because multivariate TPE doesn't support variable-range parameters.
+            # The value is transformed into the actual min_weight value below.
+            min_weight = trial.suggest_float(
+                f"{component}.min_weight",
                 0.0,
                 1.0,
             )
-            steer_bad_behavior_weight = trial.suggest_float(
-                "steer_bad_behavior_weight",
-                0.0001,
+            min_weight_distance = trial.suggest_float(
+                f"{component}.min_weight_distance",
                 1.0,
-                log=True,
-            )
-            overcorrect_relative_weight = trial.suggest_float(
-                "overcorrect_relative_weight",
-                0.0,
-                1.3,
-            )
-            neighbor_count = trial.suggest_int(
-                "neighbor_count",
-                1,
-                15,
+                0.6 * last_layer_index,
             )
 
-            ara_parameters = ARAParameters(
-                start_layer_index=start_layer_index,
-                end_layer_index=end_layer_index,
-                preserve_good_behavior_weight=preserve_good_behavior_weight,
-                steer_bad_behavior_weight=steer_bad_behavior_weight,
-                overcorrect_relative_weight=overcorrect_relative_weight,
-                neighbor_count=neighbor_count,
+            parameters[component] = AbliterationParameters(
+                max_weight=max_weight,
+                max_weight_position=max_weight_position,
+                min_weight=(min_weight * max_weight),
+                min_weight_distance=min_weight_distance,
             )
 
-            trial.set_user_attr("ara_parameters", asdict(ara_parameters))
-        else:
-            direction_scope = trial.suggest_categorical(
-                "direction_scope",
-                [
-                    "global",
-                    "per layer",
-                ],
-            )
-
-            last_layer_index = len(model.get_layers()) - 1
-
-            # Discrimination between "harmful" and "harmless" inputs is usually strongest
-            # in layers slightly past the midpoint of the layer stack. See the original
-            # abliteration paper (https://arxiv.org/abs/2406.11717) for a deeper analysis.
-            #
-            # Note that we always sample this parameter even though we only need it for
-            # the "global" direction scope. The reason is that multivariate TPE doesn't
-            # work with conditional or variable-range parameters.
-            direction_index = trial.suggest_float(
-                "direction_index",
-                0.4 * last_layer_index,
-                0.9 * last_layer_index,
-            )
-
-            if direction_scope == "per layer":
-                direction_index = None
-
-            parameters = {}
-
-            for component in model.get_abliterable_components():
-                # The parameter ranges are based on experiments with various models
-                # and much wider ranges. They are not set in stone and might have to be
-                # adjusted for future models.
-                max_weight = trial.suggest_float(
-                    f"{component}.max_weight",
-                    0.8,
-                    1.5,
-                )
-                max_weight_position = trial.suggest_float(
-                    f"{component}.max_weight_position",
-                    0.6 * last_layer_index,
-                    1.0 * last_layer_index,
-                )
-                # For sampling purposes, min_weight is expressed as a fraction of max_weight,
-                # again because multivariate TPE doesn't support variable-range parameters.
-                # The value is transformed into the actual min_weight value below.
-                min_weight = trial.suggest_float(
-                    f"{component}.min_weight",
-                    0.0,
-                    1.0,
-                )
-                min_weight_distance = trial.suggest_float(
-                    f"{component}.min_weight_distance",
-                    1.0,
-                    0.6 * last_layer_index,
-                )
-
-                parameters[component] = AbliterationParameters(
-                    max_weight=max_weight,
-                    max_weight_position=max_weight_position,
-                    min_weight=(min_weight * max_weight),
-                    min_weight_distance=min_weight_distance,
-                )
-
-            trial.set_user_attr("direction_index", direction_index)
-            trial.set_user_attr(
-                "parameters", {k: asdict(v) for k, v in parameters.items()}
-            )
+        trial.set_user_attr("direction_index", direction_index)
+        trial.set_user_attr("parameters", {k: asdict(v) for k, v in parameters.items()})
 
         print()
         print(
             f"Running trial [bold]{trial_index}[/] of [bold]{settings.n_trials}[/]..."
         )
         print("* Parameters:")
-        for name, value in get_trial_parameters(settings, trial).items():
+        for name, value in get_trial_parameters(trial).items():
             print(f"  * {name} = [bold]{value}[/]")
-        if settings.use_ara_lora:
-            print("* Resetting model...")
-            model.reset_model()
-            print("* Abliterating (Arbitrary-Rank Ablation with LoRA)...")
-            model.ara_lora_abliterate(
-                good_module_io,
-                bad_module_io,
-                ARAParameters(**trial.user_attrs["ara_parameters"]),
-            )
-        elif settings.use_ara:
-            print("* Reloading model...")
-            model.reset_model()
-            print("* Abliterating (Arbitrary-Rank Ablation)...")
-            model.ara_abliterate(good_module_io, bad_module_io, ara_parameters)
-        else:
-            print("* Resetting model...")
-            model.reset_model()
-            print("* Abliterating...")
-            model.abliterate(refusal_directions, direction_index, parameters)
+        print("* Resetting model...")
+        model.reset_model()
+        print("* Abliterating...")
+        model.abliterate(refusal_directions, direction_index, parameters)
         print("* Evaluating...")
         score, kl_divergence, refusals = evaluator.get_score()
 
@@ -658,6 +572,8 @@ def run():
 
         trial.set_user_attr("kl_divergence", kl_divergence)
         trial.set_user_attr("refusals", refusals)
+        trial.set_user_attr("base_refusals", evaluator.base_refusals)
+        trial.set_user_attr("n_bad_prompts", len(evaluator.bad_prompts))
 
         return score
 
@@ -674,6 +590,7 @@ def run():
             n_startup_trials=settings.n_startup_trials,
             n_ei_candidates=128,
             multivariate=True,
+            seed=settings.seed,
         ),
         directions=[StudyDirection.MINIMIZE, StudyDirection.MINIMIZE],
         storage=storage,
@@ -738,7 +655,7 @@ def run():
                 title=(
                     f"[Trial {trial.user_attrs['index']:>3}] "
                     f"Refusals: {trial.user_attrs['refusals']:>2}/{len(evaluator.bad_prompts)}, "
-                    f"{'PIQA acc_norm' if settings.use_piqa else 'KL divergence'}: {(-1 if settings.use_piqa else 1) * trial.user_attrs['kl_divergence']:.4f}"
+                    f"KL divergence: {trial.user_attrs['kl_divergence']:.4f}"
                 ),
                 value=trial,
             )
@@ -817,38 +734,19 @@ def run():
             print()
             print(f"Restoring model from trial [bold]{trial.user_attrs['index']}[/]...")
             print("* Parameters:")
-            for name, value in get_trial_parameters(settings, trial).items():
+            for name, value in get_trial_parameters(trial).items():
                 print(f"  * {name} = [bold]{value}[/]")
-            if settings.use_ara_lora:
-                print("* Resetting model...")
-                model.reset_model()
-                print("* Abliterating (Arbitrary-Rank Ablation with LoRA)...")
-                model.ara_lora_abliterate(
-                    good_module_io,
-                    bad_module_io,
-                    ARAParameters(**trial.user_attrs["ara_parameters"]),
-                )
-            elif settings.use_ara:
-                print("* Reloading model...")
-                model.reset_model()
-                print("* Abliterating (Arbitrary-Rank Ablation)...")
-                model.ara_abliterate(
-                    good_module_io,
-                    bad_module_io,
-                    ARAParameters(**trial.user_attrs["ara_parameters"]),
-                )
-            else:
-                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()
-                    },
-                )
+            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()
@@ -876,21 +774,23 @@ def run():
                             if not save_directory:
                                 continue
 
-                            strategy = obtain_merge_strategy(settings)
+                            strategy = obtain_merge_strategy(settings, model)
                             if strategy is None:
                                 continue
 
                             if strategy == "adapter":
                                 print("Saving LoRA adapter...")
-                                model.model.save_pretrained(save_directory)
+                                model.model.save_pretrained(
+                                    save_directory,
+                                    max_shard_size=settings.max_shard_size,
+                                )
                             else:
-                                if settings.use_ara:
-                                    print("Saving model...")
-                                    merged_model = model.model
-                                else:
-                                    print("Saving merged model...")
-                                    merged_model = model.get_merged_model()
-                                merged_model.save_pretrained(save_directory)
+                                print("Saving merged model...")
+                                merged_model = model.get_merged_model()
+                                merged_model.save_pretrained(
+                                    save_directory,
+                                    max_shard_size=settings.max_shard_size,
+                                )
                                 del merged_model
                                 empty_cache()
                                 model.tokenizer.save_pretrained(save_directory)
@@ -931,27 +831,69 @@ def run():
                                 continue
                             private = visibility == "Private"
 
-                            strategy = obtain_merge_strategy(settings)
+                            strategy = obtain_merge_strategy(settings, model)
                             if strategy is None:
                                 continue
 
+                            # Reproducibility requires that the model and all datasets
+                            # are available on the Hugging Face Hub (not local paths).
+                            datasets = [
+                                settings.good_prompts.dataset,
+                                settings.bad_prompts.dataset,
+                                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
+                            )
+
+                            if is_reproducible:
+                                print(
+                                    (
+                                        "Heretic can add information to the repository that allows others to reproduce the model. "
+                                        "This is optional, but valuable to the community as both a learning tool and to preserve computational work already done. "
+                                        "Guaranteeing reproducibility requires basic system information (Python and OS version, CPU and GPU/accelerator info) "
+                                        "as tensor operations can give different results in different system environments. "
+                                        "[bold]The information does not include any file system paths or other private data.[/]"
+                                    )
+                                )
+                                reproducibility_information = prompt_select(
+                                    "Which reproducibility information do you want to add?",
+                                    [
+                                        Choice(
+                                            title="Full: Settings, package versions, and system information",
+                                            value="full",
+                                        ),
+                                        Choice(
+                                            title="Basic: Settings and package versions",
+                                            value="basic",
+                                        ),
+                                        Choice(
+                                            title="Don't add any reproducibility information",
+                                            value="none",
+                                        ),
+                                    ],
+                                )
+                                if reproducibility_information is None:
+                                    continue
+                            else:
+                                reproducibility_information = "none"
+
                             if strategy == "adapter":
                                 print("Uploading LoRA adapter...")
                                 model.model.push_to_hub(
                                     repo_id,
                                     private=private,
+                                    max_shard_size=settings.max_shard_size,
                                     token=token,
                                 )
                             else:
-                                if settings.use_ara:
-                                    print("Uploading model...")
-                                    merged_model = model.model
-                                else:
-                                    print("Uploading merged model...")
-                                    merged_model = model.get_merged_model()
+                                print("Uploading merged model...")
+                                merged_model = model.get_merged_model()
                                 merged_model.push_to_hub(
                                     repo_id,
                                     private=private,
+                                    max_shard_size=settings.max_shard_size,
                                     token=token,
                                 )
                                 del merged_model
@@ -962,22 +904,18 @@ def run():
                                     token=token,
                                 )
 
-                            # If the model path exists locally and includes the
-                            # card, use it directly. If the model path doesn't
-                            # exist locally, it can be assumed to be a model
-                            # hosted on the Hugging Face Hub, in which case
-                            # we can retrieve the model card.
-                            model_path = Path(settings.model)
-                            if model_path.exists():
+                            if is_hf_path(settings.model):
+                                card = ModelCard.load(settings.model)
+                            else:
                                 card_path = (
-                                    model_path / huggingface_hub.constants.REPOCARD_NAME
+                                    Path(settings.model)
+                                    / huggingface_hub.constants.REPOCARD_NAME
                                 )
                                 if card_path.exists():
                                     card = ModelCard.load(card_path)
                                 else:
                                     card = None
-                            else:
-                                card = ModelCard.load(settings.model)
+
                             if card is not None:
                                 if card.data is None:
                                     card.data = ModelCardData()
@@ -987,25 +925,34 @@ def run():
                                 card.data.tags.append("uncensored")
                                 card.data.tags.append("decensored")
                                 card.data.tags.append("abliterated")
-                                if settings.use_ara:
-                                    card.data.tags.append("ara")
-                                elif (
-                                    settings.orthogonalize_direction
-                                    and settings.row_normalization
-                                    == RowNormalization.FULL
-                                ):
-                                    card.data.tags.append("mpoa")
+                                if reproducibility_information != "none":
+                                    card.data.tags.append("reproducible")
                                 card.text = (
                                     get_readme_intro(
                                         settings,
                                         trial,
-                                        evaluator.base_refusals,
-                                        evaluator.bad_prompts,
+                                        reproducibility_information != "none",
                                     )
                                     + card.text
                                 )
                                 card.push_to_hub(repo_id, token=token)
 
+                            if reproducibility_information != "none":
+                                # Set the number of trials to the number of actual completed trials
+                                # for the reproduction configuration.
+                                settings.n_trials = count_completed_trials()
+
+                                upload_reproduce_folder(
+                                    repo_id,
+                                    settings,
+                                    token,
+                                    checkpoint_path=study_checkpoint_file,
+                                    trial=trial,
+                                    include_system_information=(
+                                        reproducibility_information == "full"
+                                    ),
+                                )
+
                             print(f"Model uploaded to [bold]{repo_id}[/].")
 
                         case "Chat with the model":

src/heretic/model.py

@@ -4,7 +4,7 @@
 import math
 from contextlib import suppress
 from dataclasses import dataclass
-from typing import Any, Callable, Type, TypeAlias, cast
+from typing import Any, Type, cast
 
 import bitsandbytes as bnb
 import torch
@@ -14,8 +14,6 @@ from peft import LoraConfig, PeftModel, get_peft_model
 from peft.tuners.lora.layer import Linear
 from torch import FloatTensor, LongTensor, Tensor
 from torch.nn import Module, ModuleList
-from torch.optim import LBFGS
-from torch.utils.hooks import RemovableHandle
 from transformers import (
     AutoModelForCausalLM,
     AutoModelForImageTextToText,
@@ -32,7 +30,8 @@ from transformers.generation import (
 )
 
 from .config import QuantizationMethod, RowNormalization, Settings
-from .utils import Prompt, batchify, empty_cache, mean_distances_to_knn, print
+from .system import empty_cache
+from .utils import Prompt, batchify, print
 
 
 def get_model_class(
@@ -54,23 +53,6 @@ class AbliterationParameters:
     min_weight_distance: float
 
 
-@dataclass
-class ARAParameters:
-    start_layer_index: int
-    end_layer_index: int
-    preserve_good_behavior_weight: float
-    steer_bad_behavior_weight: float
-    overcorrect_relative_weight: float
-    neighbor_count: int
-
-
-# The list contains one element per layer.
-# Each element maps from the component name to a (possibly sparse) mapping
-# from the module index to an (input, output) tuple containing the I/O
-# tensors of shape (prompt, component).
-ModuleIO: TypeAlias = list[dict[str, dict[int, tuple[Tensor, Tensor]]]]
-
-
 class Model:
     model: PreTrainedModel | PeftModel
     tokenizer: PreTrainedTokenizerBase
@@ -78,15 +60,19 @@ class Model:
 
     def __init__(self, settings: Settings):
         self.settings = settings
-        self.response_prefix = ""
         self.needs_reload = False
 
+        self.revision_kwargs = {}
+        if settings.model_commit is not None:
+            self.revision_kwargs["revision"] = settings.model_commit
+
         print()
         print(f"Loading model [bold]{settings.model}[/]...")
 
         self.tokenizer = AutoTokenizer.from_pretrained(
             settings.model,
             trust_remote_code=settings.trust_remote_code,
+            **self.revision_kwargs,
         )
 
         # Fallback for tokenizers that don't declare a special pad token.
@@ -127,6 +113,7 @@ class Model:
                     device_map=settings.device_map,
                     max_memory=self.max_memory,
                     trust_remote_code=self.trusted_models.get(settings.model),
+                    **self.revision_kwargs,
                     **extra_kwargs,
                 )
 
@@ -161,8 +148,7 @@ class Model:
         if self.model is None:
             raise Exception("Failed to load model with all configured dtypes.")
 
-        if not settings.use_ara or settings.use_ara_lora:
-            self._apply_lora()
+        self._apply_lora()
 
         # LoRA B matrices are initialized to zero by default in PEFT,
         # so we don't need to do anything manually.
@@ -188,7 +174,7 @@ class Model:
         # because hybrid models like Qwen3.5 MoE have modules with different names
         # across layers (e.g. "o_proj" on attention layers, "out_proj" on linear attention layers).
         target_modules_set: set[str] = set()
-        
+
         module_id_to_full_name = {
             id(module): module_name
             for module_name, module in self.model.named_modules()
@@ -203,9 +189,7 @@ class Model:
 
         target_modules = sorted(target_modules_set)
 
-        if self.settings.use_ara_lora:
-            lora_rank = self.settings.ara_lora_rank
-        elif self.settings.row_normalization != RowNormalization.FULL:
+        if self.settings.row_normalization != RowNormalization.FULL:
             # Rank 1 is sufficient for directional ablation without renormalization.
             lora_rank = 1
         else:
@@ -279,6 +263,7 @@ class Model:
                 torch_dtype=self.model.dtype,
                 device_map="cpu",
                 trust_remote_code=self.trusted_models.get(self.settings.model),
+                **self.revision_kwargs,
             )
 
             # Apply LoRA adapters to the CPU model
@@ -314,11 +299,7 @@ class Model:
           performs full model reload with quantization config.
         """
         current_model = getattr(self.model.config, "name_or_path", None)
-        if (
-            current_model == self.settings.model
-            and not self.needs_reload
-            and (not self.settings.use_ara or self.settings.use_ara_lora)
-        ):
+        if current_model == self.settings.model and not self.needs_reload:
             # Reset LoRA adapters to zero (identity transformation)
             for name, module in self.model.named_modules():
                 if "lora_B" in name and hasattr(module, "weight"):
@@ -344,11 +325,11 @@ class Model:
             device_map=self.settings.device_map,
             max_memory=self.max_memory,
             trust_remote_code=self.trusted_models.get(self.settings.model),
+            **self.revision_kwargs,
             **extra_kwargs,
         )
 
-        if not self.settings.use_ara or self.settings.use_ara_lora:
-            self._apply_lora()
+        self._apply_lora()
 
         self.needs_reload = False
 
@@ -372,9 +353,6 @@ class Model:
         modules = {}
 
         def try_add(component: str, module: Any):
-            if component not in self.settings.target_components:
-                return
-
             # Only add if it's a proper nn.Module (PEFT can wrap these with LoRA)
             if isinstance(module, Module):
                 if component not in modules:
@@ -575,228 +553,6 @@ class Model:
                     weight_A.data = lora_A.to(weight_A.dtype)
                     weight_B.data = lora_B.to(weight_B.dtype)
 
-    def ara_abliterate(
-        self,
-        good_module_io: ModuleIO,
-        bad_module_io: ModuleIO,
-        parameters: ARAParameters,
-    ):
-        for layer_index in range(
-            parameters.start_layer_index,
-            parameters.end_layer_index,
-        ):
-            for component, modules in self.get_layer_modules(layer_index).items():
-                for module_index, module in enumerate(modules):
-                    # See above for a (partial) justification of this cast.
-                    module = cast(Linear, module)
-                    matrix = module.weight
-
-                    row_norms = LA.vector_norm(matrix, dim=1, keepdim=True).detach()
-
-                    # Helper function for reparameterization (row-norm preservation constraint).
-                    def get_matrix() -> Tensor:
-                        if self.settings.row_normalization == RowNormalization.FULL:
-                            # See https://huggingface.co/blog/grimjim/norm-preserving-biprojected-abliteration
-                            return row_norms * F.normalize(matrix, p=2, dim=1)
-                        else:
-                            return matrix
-
-                    good_input, good_output = good_module_io[layer_index][component][
-                        module_index
-                    ]
-                    bad_input, bad_output = bad_module_io[layer_index][component][
-                        module_index
-                    ]
-
-                    good_input = good_input.to(matrix.device)
-                    good_output = good_output.to(matrix.device)
-                    bad_input = bad_input.to(matrix.device)
-                    bad_output = bad_output.to(matrix.device)
-
-                    def objective(matrix: Tensor) -> Tensor:
-                        new_good_output = good_input @ matrix.T
-                        new_bad_output = bad_input @ matrix.T
-
-                        # The outputs for "good" prompts should change as little as possible.
-                        preserve_good_behavior = (
-                            (new_good_output - good_output) ** 2
-                        ).mean()
-
-                        steer_bad_behavior = (
-                            # Pull the outputs for "bad" prompts towards
-                            # the original outputs for "good" prompts.
-                            mean_distances_to_knn(
-                                new_bad_output,
-                                good_output,
-                                parameters.neighbor_count,
-                            ).mean()
-                            # Push the outputs for "bad" prompts away from
-                            # the original outputs for "bad" prompts.
-                            # In combination with the above, this overcorrects
-                            # away from the original residuals, which results
-                            # in stronger steering that can overcome more complex
-                            # refusal mechanisms.
-                            + parameters.overcorrect_relative_weight
-                            * -mean_distances_to_knn(
-                                new_bad_output,
-                                bad_output,
-                                parameters.neighbor_count,
-                            ).mean()
-                        )
-
-                        return (
-                            parameters.preserve_good_behavior_weight
-                            * preserve_good_behavior
-                            + parameters.steer_bad_behavior_weight * steer_bad_behavior
-                        )
-
-                    optimizer = LBFGS(
-                        [matrix],
-                        lr=1.0,
-                        max_iter=20,  # Number of internal iterations per step, *not* the number of steps.
-                        history_size=10,
-                        line_search_fn="strong_wolfe",
-                    )
-
-                    def closure() -> Tensor:
-                        optimizer.zero_grad()
-                        loss = objective(get_matrix())
-                        loss.backward()
-                        return loss
-
-                    # Convergence usually happens within 2-3 steps, so this is more than enough.
-                    for step in range(5):
-                        loss = optimizer.step(closure)
-                        # print(
-                        #    f"\\[{layer_index}/{component}/{module_index}] Step: {step}, Loss: {loss.item():.6f}"
-                        # )
-
-                    # Free the gradient buffers accumulated on the weight parameters
-                    # during optimization. Without this, they persist on the model
-                    # (one full-size gradient per processed weight) and can easily
-                    # consume tens of GiB of VRAM, causing out-of-memory errors
-                    # during the subsequent evaluation.
-                    optimizer.zero_grad(set_to_none=True)
-
-                    with torch.no_grad():
-                        matrix.copy_(get_matrix())
-
-    def ara_lora_abliterate(
-        self,
-        good_module_io: ModuleIO,
-        bad_module_io: ModuleIO,
-        parameters: ARAParameters,
-    ):
-        for layer_index in range(
-            parameters.start_layer_index,
-            parameters.end_layer_index,
-        ):
-            for component, modules in self.get_layer_modules(layer_index).items():
-                for module_index, module in enumerate(modules):
-                    # Cast to Linear to access weights and LoRA adapters.
-                    module = cast(Linear, module)
-
-                    # Base weight handling and dequantization.
-                    # We need the base weight in float32 to compute the effective weight.
-                    base_weight = cast(Tensor, module.base_layer.weight)
-                    quant_state = getattr(base_weight, "quant_state", None)
-
-                    if quant_state is None:
-                        W_base = base_weight.to(torch.float32)
-                    else:
-                        # Maintain the original dequantization logic for bitsandbytes.
-                        W_base = cast(
-                            Tensor,
-                            bnb.functional.dequantize_4bit(
-                                base_weight.data, 
-                                quant_state
-                            ).to(torch.float32),
-                        )
-
-                    # Row normalization setup.
-                    # Pre-calculate the original row norms to preserve them.
-                    # This implements the RowNormalization.FULL logic.
-                    W_row_norms = LA.vector_norm(W_base, dim=1, keepdim=True).detach()
-
-                    # Adapter target identification.
-                    # We optimize the LoRA weights A and B.
-                    lora_A = cast(Tensor, module.lora_A["default"].weight)
-                    lora_B = cast(Tensor, module.lora_B["default"].weight)
-
-                    # Data preparation.
-                    # Move I/O tensors to the device of the adapter weights.
-                    good_input, good_output = good_module_io[layer_index][component][module_index]
-                    bad_input, bad_output = bad_module_io[layer_index][component][module_index]
-
-                    good_input = good_input.float().to(lora_A.device)
-                    good_output = good_output.float().to(lora_A.device)
-                    bad_input = bad_input.float().to(lora_A.device)
-                    bad_output = bad_output.float().to(lora_A.device)
-
-                    # The objective function.
-                    def objective(A: Tensor, B: Tensor) -> Tensor:
-                        # Calculate effective weight: W_eff = W_base + B @ A.
-                        W_eff = W_base + (B @ A)
-
-                        # Apply Row Normalization (keep original norms).
-                        if self.settings.row_normalization == RowNormalization.FULL:
-                            # Normalize to unit length, then scale by original norms.
-                            W_eff = F.normalize(W_eff, p=2, dim=1) * W_row_norms
-
-                        # Compute outputs using the effective weight.
-                        new_good_output = good_input @ W_eff.T
-                        new_bad_output = bad_input @ W_eff.T
-
-                        # The original ARA loss function.
-                        preserve_good_behavior = (
-                            (new_good_output - good_output) ** 2
-                        ).mean()
-
-                        steer_bad_behavior = (
-                            mean_distances_to_knn(
-                                new_bad_output,
-                                good_output,
-                                parameters.neighbor_count,
-                            ).mean()
-                            + parameters.overcorrect_relative_weight
-                            * -mean_distances_to_knn(
-                                new_bad_output,
-                                bad_output,
-                                parameters.neighbor_count,
-                            ).mean()
-                        )
-
-                        return (
-                            parameters.preserve_good_behavior_weight
-                            * preserve_good_behavior
-                            + parameters.steer_bad_behavior_weight * steer_bad_behavior
-                        )
-
-                    # Optimization loop.
-                    # We optimize A and B, not the base matrix.
-                    optimizer = LBFGS(
-                        [lora_A, lora_B],
-                        lr=1.0,
-                        max_iter=20,
-                        history_size=10,
-                        line_search_fn="strong_wolfe",
-                    )
-
-                    def closure():
-                        optimizer.zero_grad()
-                        # Pass the actual tensors being optimized to the objective.
-                        loss = objective(lora_A, lora_B)
-                        loss.backward()
-                        return loss
-
-                    # Run optimization steps.
-                    for step in range(5):
-                        optimizer.step(closure)
-
-                    # Free the gradient buffers accumulated on the LoRA adapter
-                    # parameters during optimization (see ara_abliterate for details).
-                    optimizer.zero_grad(set_to_none=True)
-
     def generate(
         self,
         prompts: list[Prompt],
@@ -821,10 +577,12 @@ class Model:
             ),
         )
 
-        if self.response_prefix:
+        if self.settings.response_prefix:
             # Append the common response prefix to the prompts so that evaluation happens
             # at the point where responses start to differ for different prompts.
-            chat_prompts = [prompt + self.response_prefix for prompt in chat_prompts]
+            chat_prompts = [
+                prompt + self.settings.response_prefix for prompt in chat_prompts
+            ]
 
         inputs = self.tokenizer(
             chat_prompts,
@@ -886,6 +644,9 @@ class Model:
             max_new_tokens=1,
             output_hidden_states=True,
             return_dict_in_generate=True,
+            # KV cache is unnecessary here because we only need the hidden states
+            # for the first generated token.
+            use_cache=False,
         )
 
         # This cast is valid because GenerateDecoderOnlyOutput is the return type
@@ -919,7 +680,11 @@ class Model:
                 dim=2,
                 keepdim=True,
             )
-            return torch.clamp(residuals, -thresholds, thresholds)
+            residuals = torch.clamp(residuals, -thresholds, thresholds)
+
+        if self.settings.offload_outputs_to_cpu:
+            residuals = residuals.cpu()
+            empty_cache()
 
         return residuals
 
@@ -931,131 +696,29 @@ class Model:
 
         return torch.cat(residuals, dim=0)
 
-    def get_module_io(
-        self,
-        prompts: list[Prompt],
-    ) -> ModuleIO:
-        # The list contains one element per layer.
-        # Each element maps from the component name to a (possibly sparse) mapping
-        # from the module index to an (input, output) tuple containing the I/O
-        # tensors of shape (prompt, component).
-        module_io: ModuleIO = []
+    def get_residuals_mean(self, prompts: list[Prompt]) -> Tensor:
+        if not prompts:
+            raise ValueError("prompts must not be empty")
 
-        def get_hook(
-            layer_index: int,
-            component: str,
-            module_index: int,
-        ) -> Callable[[Module, tuple[Tensor, ...], Tensor], None]:
-            def hook(
-                module: Module,
-                inputs: tuple[Tensor, ...],
-                outputs: Tensor,
-            ) -> None:
-                if len(module_io) == layer_index:
-                    # First invocation of the hook for this layer.
-                    module_io.append({})
+        running_sum = None
+        total_count = 0
 
-                # Layers are invoked in order during inference,
-                # so this should always hold.
-                assert len(module_io) == layer_index + 1
+        for batch in batchify(prompts, self.settings.batch_size):
+            batch_residuals = self.get_residuals(batch)
 
-                if component not in module_io[layer_index]:
-                    module_io[layer_index][component] = {}
+            # Accumulate in high precision on CPU to reduce peak VRAM usage.
+            batch_sum = batch_residuals.sum(dim=0, dtype=torch.float64).cpu()
 
-                # Each module should be invoked at most once per inference step.
-                assert module_index not in module_io[layer_index][component]
+            if running_sum is None:
+                running_sum = batch_sum
+            else:
+                running_sum += batch_sum
 
-                # inputs[0] and outputs have shape (prompt, position, component),
-                # so this extracts the input/output at the end of each prompt.
-                # Move to CPU to decouple from device assignments, which can
-                # change between model reloads in multi-GPU configurations.
-                input = inputs[0][:, -1, :].detach().clone().cpu()
-                output = outputs[:, -1, :].detach().clone().cpu()
+            total_count += batch_residuals.shape[0]
 
-                # The modules associated with a component (e.g. expert MLPs)
-                # are not necessarily invoked in order, nor are all of them
-                # necessarily invoked in each inference step, so we cannot
-                # use a list here.
-                module_io[layer_index][component][module_index] = (input, output)
+        assert running_sum is not None
 
-            return hook
-
-        hook_handles: list[RemovableHandle] = []
-
-        for layer_index in range(len(self.get_layers())):
-            for component, modules in self.get_layer_modules(layer_index).items():
-                for module_index, module in enumerate(modules):
-                    hook_handles.append(
-                        module.register_forward_hook(
-                            get_hook(layer_index, component, module_index)
-                        )
-                    )
-
-        self.generate(prompts, max_new_tokens=1)
-
-        for hook_handle in hook_handles:
-            hook_handle.remove()
-
-        return module_io
-
-    def get_module_io_batched(
-        self,
-        prompts: list[Prompt],
-    ) -> ModuleIO:
-        # Aggregating batch results is more complicated for module I/O
-        # than for other get_*_batched methods, because the structure of the results
-        # might differ between batches, as whether individual modules activate
-        # can depend on the prompt (in particular for MoE models).
-        # In practice, inhomogeneous results should be very rare, but to be fully
-        # generic, this logic is required.
-        module_io_batches: list[ModuleIO] = [
-            self.get_module_io(batch)
-            for batch in batchify(prompts, self.settings.batch_size)
-        ]
-
-        module_io: ModuleIO = []
-
-        for layer_index in range(len(self.get_layers())):
-            module_io.append({})
-
-            for module_io_batch in module_io_batches:
-                for component, io_map in module_io_batch[layer_index].items():
-                    if component not in module_io[layer_index]:
-                        module_io[layer_index][component] = {}
-
-                    for module_index in io_map:
-                        if module_index not in module_io[layer_index][component]:
-                            # This is a placeholder; the actual aggregation happens below.
-                            # We need to iterate over the batches twice because we don't
-                            # know in advance which components and module indices are present.
-                            module_io[layer_index][component][module_index] = (
-                                torch.empty(0),
-                                torch.empty(0),
-                            )
-
-            for component, io_map in module_io[layer_index].items():
-                for module_index in io_map:
-                    inputs_outputs = [
-                        module_io_batch[layer_index][component][module_index]
-                        for module_io_batch in module_io_batches
-                        if component in module_io_batch[layer_index]
-                        and module_index in module_io_batch[layer_index][component]
-                    ]
-                    input = torch.cat(
-                        [input_output[0] for input_output in inputs_outputs],
-                        dim=0,
-                    )
-                    output = torch.cat(
-                        [input_output[1] for input_output in inputs_outputs],
-                        dim=0,
-                    )
-
-                    # The key already exists, and replacing existing values
-                    # in a dictionary while iterating over the same dictionary
-                    # is safe in Python.
-                    module_io[layer_index][component][module_index] = (input, output)
-
-        return module_io
+        return (running_sum / total_count).to(torch.float32)
 
     # We work with logprobs rather than probabilities for numerical stability
     # when computing the KL divergence.
@@ -1067,6 +730,7 @@ class Model:
             max_new_tokens=1,
             output_scores=True,
             return_dict_in_generate=True,
+            use_cache=False,
         )
 
         # This cast is valid because GenerateDecoderOnlyOutput is the return type
@@ -1078,7 +742,15 @@ class Model:
         logits = cast(tuple[FloatTensor], outputs.scores)[0]
 
         # The returned tensor has shape (prompt, token).
-        return F.log_softmax(logits, dim=-1)
+        logprobs = F.log_softmax(logits, dim=-1)
+
+        del outputs
+
+        if self.settings.offload_outputs_to_cpu:
+            logprobs = logprobs.cpu()
+            empty_cache()
+
+        return logprobs
 
     def get_logprobs_batched(self, prompts: list[Prompt]) -> Tensor:
         logprobs = []

src/heretic/system.py

@@ -0,0 +1,478 @@
+# SPDX-License-Identifier: AGPL-3.0-or-later
+# Copyright (C) 2025-2026  Philipp Emanuel Weidmann <pew@worldwidemann.com> + contributors
+
+import gc
+import importlib.metadata
+import json
+import os
+import platform
+import re
+import subprocess
+import sys
+from dataclasses import dataclass
+from typing import Any
+
+import cpuinfo
+import torch
+from accelerate.utils import (
+    is_mlu_available,
+    is_musa_available,
+    is_npu_available,
+    is_sdaa_available,
+    is_xpu_available,
+)
+
+
+def empty_cache():
+    """Clears the backend cache and collects garbage."""
+
+    # Collecting garbage is not an idempotent operation, and to avoid OOM errors,
+    # gc.collect() has to be called both before and after emptying the backend cache.
+    # See https://github.com/p-e-w/heretic/pull/17 for details.
+    gc.collect()
+
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    elif is_xpu_available():
+        torch.xpu.empty_cache()
+    elif is_mlu_available():
+        torch.mlu.empty_cache()  # ty:ignore[unresolved-attribute]
+    elif is_sdaa_available():
+        torch.sdaa.empty_cache()  # ty:ignore[unresolved-attribute]
+    elif is_musa_available():
+        torch.musa.empty_cache()  # ty:ignore[unresolved-attribute]
+    elif torch.backends.mps.is_available():
+        torch.mps.empty_cache()
+
+    gc.collect()
+
+
+def get_nvidia_driver_version() -> str | None:
+    """Gets the NVIDIA driver version using nvidia-smi."""
+
+    try:
+        output = subprocess.check_output(
+            ["nvidia-smi", "--query-gpu=driver_version", "--format=csv,noheader"],
+            stderr=subprocess.DEVNULL,
+            text=True,
+        )
+        return output.strip().split("\n")[0]
+    except (subprocess.CalledProcessError, FileNotFoundError, IndexError):
+        return None
+
+
+def get_amdgpu_driver_version() -> str | None:
+    """Gets the AMD GPU (ROCm) driver and suite version info."""
+
+    # 1. Try amd-smi (modern standard for ROCm 6.0+)
+    try:
+        output = subprocess.check_output(
+            ["amd-smi", "version"],
+            stderr=subprocess.DEVNULL,
+            text=True,
+        )
+        if output.strip():
+            return output.strip().replace("\n", " | ")
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        pass
+
+    # 2. Try rocm-smi --showdriverversion
+    try:
+        output = subprocess.check_output(
+            ["rocm-smi", "--showdriverversion"],
+            stderr=subprocess.DEVNULL,
+            text=True,
+        )
+        for line in output.split("\n"):
+            if "Driver version" in line:
+                return line.split(":")[-1].strip()
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        pass
+
+    # 3. Try /sys/module/amdgpu/version (Linux kernel driver version)
+    try:
+        if platform.system() == "Linux":
+            version_path = "/sys/module/amdgpu/version"
+            if os.path.exists(version_path):
+                with open(version_path, "r", encoding="utf-8") as f:
+                    return f.read().strip()
+    except Exception:
+        pass
+
+    return None
+
+
+def get_xpu_driver_version() -> str | None:
+    """Gets the Intel XPU driver version."""
+
+    try:
+        output = subprocess.check_output(
+            ["xpu-smi", "discovery"],
+            stderr=subprocess.DEVNULL,
+            text=True,
+        )
+        for line in output.split("\n"):
+            if "Driver Version" in line:
+                return line.split(":")[-1].strip()
+        return None
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        return None
+
+
+def get_npu_driver_version() -> str | None:
+    """Gets the Huawei NPU driver version."""
+
+    try:
+        output = subprocess.check_output(
+            ["npu-smi", "info", "-t", "board", "-i", "0"],
+            stderr=subprocess.DEVNULL,
+            text=True,
+        )
+        for line in output.split("\n"):
+            if "Software Version" in line:
+                return line.split()[-1].strip()
+        return None
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        return None
+
+
+def get_mps_driver_version() -> str | None:
+    """Gets the Apple Silicon (MPS) driver version via macOS version."""
+
+    try:
+        output = subprocess.check_output(
+            ["sw_vers", "-productVersion"],
+            stderr=subprocess.DEVNULL,
+            text=True,
+        )
+        return output.strip()
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        return None
+
+
+@dataclass
+class HereticVersionInfo:
+    """Detailed information about the heretic-llm installation."""
+
+    version: str
+    origin: str | None
+    is_standard_pypi: bool
+    metadata: dict[str, Any]
+
+
+def get_heretic_version_info() -> HereticVersionInfo:
+    """Detects version and installation source (PyPI, Git, Local) of heretic-llm."""
+
+    package_name = "heretic-llm"
+    origin_metadata: dict[str, Any] = {"type": "unknown"}
+    # This package must be installed for this code to run.
+    distribution = importlib.metadata.distribution(package_name)
+
+    base_version = distribution.version.lstrip("v")
+
+    try:
+        direct_url_content = distribution.read_text("direct_url.json")
+    except Exception:
+        direct_url_content = None
+
+    if not direct_url_content:
+        # Standard PyPI installation.
+        origin_metadata["type"] = "pypi"
+
+        return HereticVersionInfo(
+            version=base_version,
+            origin="PyPI",
+            is_standard_pypi=True,
+            metadata=origin_metadata,
+        )
+
+    data = json.loads(direct_url_content)
+
+    # Check for Git source.
+    if "vcs_info" in data and data["vcs_info"].get("vcs") == "git":
+        vcs_info = data["vcs_info"]
+        commit_hash = vcs_info.get("commit_id", "unknown")
+        repo_url = data.get("url", "unknown_repo")
+        requested_revision = vcs_info.get("requested_revision")
+
+        if requested_revision:
+            origin_str = (
+                f"Git ({repo_url}@{requested_revision} - commit: {commit_hash})"
+            )
+        else:
+            origin_str = f"Git ({repo_url} @ {commit_hash})"
+
+        origin_metadata.update(
+            {
+                "type": "git",
+                "url": repo_url,
+                "commit_hash": commit_hash,
+                "requested_revision": requested_revision,
+            }
+        )
+
+        return HereticVersionInfo(
+            version=base_version,
+            origin=origin_str,
+            is_standard_pypi=False,
+            metadata=origin_metadata,
+        )
+
+    # Check for local file/wheel directory.
+    if "url" in data and data["url"].startswith("file://"):
+        origin_metadata["type"] = "local"
+
+        return HereticVersionInfo(
+            version=base_version,
+            origin="Local",
+            is_standard_pypi=False,
+            metadata=origin_metadata,
+        )
+
+    return HereticVersionInfo(
+        version=base_version,
+        origin=None,
+        is_standard_pypi=False,
+        metadata=origin_metadata,
+    )
+
+
+def get_accelerator_info_dict() -> dict[str, Any]:
+    """Retrieves raw accelerator info (CUDA, ROCm, etc) directly into structured keys."""
+
+    if torch.cuda.is_available():
+        count = torch.cuda.device_count()
+        is_rocm = getattr(torch.version, "hip", None) is not None
+
+        # ROCm (AMD) and CUDA (NVIDIA) share the same API in PyTorch.
+        # We distinguish them by checking for the HIP version.
+        info: dict[str, Any] = {
+            "type": "ROCm" if is_rocm else "CUDA",
+            "api_name": "HIP Version" if is_rocm else "CUDA Version",
+            "api_version": torch.version.hip if is_rocm else torch.version.cuda,  # ty:ignore[unresolved-attribute]
+            "driver_version": get_amdgpu_driver_version()
+            if is_rocm
+            else get_nvidia_driver_version(),
+            "devices": [],
+        }
+
+        for i in range(count):
+            name = torch.cuda.get_device_name(i)
+            vram = torch.cuda.mem_get_info(i)[1] / (1024**3)
+            info["devices"].append({"name": name, "vram_gb": round(vram, 2)})
+
+        return info
+
+    if is_xpu_available():
+        count = torch.xpu.device_count()  # ty:ignore[unresolved-attribute]
+        return {
+            "type": "XPU",
+            "api_name": None,
+            "api_version": None,
+            "driver_version": get_xpu_driver_version(),
+            "devices": [{"name": torch.xpu.get_device_name(i)} for i in range(count)],  # ty:ignore[unresolved-attribute]
+        }
+
+    if is_mlu_available():
+        count = torch.mlu.device_count()  # ty:ignore[unresolved-attribute]
+        return {
+            "type": "MLU",
+            "api_name": None,
+            "api_version": None,
+            "driver_version": None,
+            "devices": [{"name": torch.mlu.get_device_name(i)} for i in range(count)],  # ty:ignore[unresolved-attribute]
+        }
+
+    if is_sdaa_available():
+        count = torch.sdaa.device_count()  # ty:ignore[unresolved-attribute]
+        return {
+            "type": "SDAA",
+            "api_name": None,
+            "api_version": None,
+            "driver_version": None,
+            "devices": [{"name": torch.sdaa.get_device_name(i)} for i in range(count)],  # ty:ignore[unresolved-attribute]
+        }
+
+    if is_musa_available():
+        count = torch.musa.device_count()  # ty:ignore[unresolved-attribute]
+        return {
+            "type": "MUSA",
+            "api_name": None,
+            "api_version": None,
+            "driver_version": None,
+            "devices": [{"name": torch.musa.get_device_name(i)} for i in range(count)],  # ty:ignore[unresolved-attribute]
+        }
+
+    if is_npu_available():
+        return {
+            "type": "NPU",
+            "api_name": "CANN Version",
+            "api_version": torch.version.cann,  # ty:ignore[unresolved-attribute]
+            "driver_version": get_npu_driver_version(),
+            "devices": [],  # Multi-NPU is less common.
+        }
+
+    if torch.backends.mps.is_available():
+        return {
+            "type": "MPS",
+            "api_name": None,
+            "api_version": None,
+            "driver_version": get_mps_driver_version(),
+            "devices": [{"name": "Apple Metal"}],
+        }
+
+    return {"type": None}
+
+
+def get_accelerator_info(include_warnings: bool = True) -> str:
+    """Convenience wrapper for hardware detection and console-friendly formatting."""
+
+    info = get_accelerator_info_dict()
+
+    if info["type"] is None:
+        suffix = " Operations will be slow." if include_warnings else ""
+        return (
+            f"[bold yellow]No GPU or other accelerator detected.{suffix}[/]\n".strip()
+        )
+
+    devices = info["devices"]
+    count = len(devices)
+    total_vram = sum(d.get("vram_gb", 0) for d in devices)
+
+    vram_suffix = f" ({total_vram:.2f} GB total VRAM)" if total_vram > 0 else ""
+    report = f"Detected [bold]{count or 1}[/] {info['type']} device(s){vram_suffix}\n"
+
+    if info.get("api_name") and info.get("api_version"):
+        report += f"{info['api_name']}: [bold]{info['api_version']}[/]\n"
+
+    driver = info.get("driver_version") or "Unknown"
+    report += f"Driver Version: [bold]{driver}[/]\n"
+
+    for i, dev in enumerate(devices):
+        vram = f" ({dev['vram_gb']:.2f} GB)" if dev.get("vram_gb") else ""
+        report += f"* {info['type']} {i}: [bold]{dev['name']}[/]{vram}\n"
+
+    return report.strip()
+
+
+def get_cpu_info_dict() -> dict[str, str | int | None]:
+    """Gets granular CPU identifiers using the py-cpuinfo library."""
+
+    info = cpuinfo.get_cpu_info()
+
+    return {
+        "brand": info.get("brand_raw"),
+        "vendor": info.get("vendor_id_raw"),
+        "family": info.get("family"),
+        "model": info.get("model"),
+        "stepping": info.get("stepping"),
+    }
+
+
+def get_cpu_info() -> str:
+    """Gets the CPU brand name."""
+
+    info = get_cpu_info_dict()
+    parts = []
+    parts.append(
+        f"Family {info['family']}, Model {info['model']}, Stepping {info['stepping']}"
+    )
+
+    details = f" ({'; '.join(parts)})" if parts else ""
+    brand = info["brand"] or "Unknown CPU"
+    return f"{brand}{details}"
+
+
+def get_python_env_info_dict() -> dict[str, str]:
+    implementation = platform.python_implementation()
+    compiler = platform.python_compiler()
+
+    # Check for Conda.
+    if "CONDA_PREFIX" in os.environ:
+        env_type = "Conda"
+    # Check for Virtualenv/Venv.
+    elif hasattr(sys, "base_prefix") and sys.base_prefix != sys.prefix:
+        env_type = "Virtualenv/Venv"
+    else:
+        env_type = "System"
+
+    return {
+        "version": platform.python_version(),
+        "implementation": implementation,
+        "compiler": compiler,
+        "environment": env_type,
+    }
+
+
+def get_python_env_info() -> str:
+    """Detects the type of Python environment (Conda, Venv, etc.) and build info."""
+
+    info = get_python_env_info_dict()
+    return f"{info['version']} ({info['implementation']}, {info['compiler']}) [{info['environment']}]"
+
+
+def get_package_version(name: str) -> str:
+    """Gets the installed version of a package, stripping local suffixes like +cu128."""
+
+    # Normalize name: pip considers hyphens and underscores equivalent.
+    normalized_name = name.lower().replace("_", "-")
+    version_str = importlib.metadata.version(normalized_name)
+    return version_str.split("+")[0] if "+" in version_str else version_str
+
+
+def get_requirements_dict() -> dict[str, str]:
+    """Recursively finds all direct and transitive dependencies of heretic-llm and core libraries."""
+
+    # We start with heretic-llm and the core compute libraries.
+    # PyTorch is not listed as a dependency in the heretic-llm package
+    # because installation is hardware-specific and must be done manually.
+    packages_to_check = ["heretic-llm", "torch", "torchaudio", "torchvision"]
+
+    visited = set()
+    required_packages = set()
+
+    while packages_to_check:
+        package = packages_to_check.pop(0)
+        # Normalize name: pip considers hyphens and underscores equivalent.
+        normalized_package = package.lower().replace("_", "-")
+        if normalized_package in visited:
+            continue
+        visited.add(normalized_package)
+
+        try:
+            distribution = importlib.metadata.distribution(normalized_package)
+            required_packages.add(normalized_package)
+            if distribution.requires:
+                for requirement in distribution.requires:
+                    # Requirements can include environment markers like '; extra == "hf"'
+                    # or version constraints. We should ignore optional 'extra' dependencies
+                    # to keep the reproduction environment clean and relevant.
+                    if ";" in requirement and "extra ==" in requirement:
+                        continue
+
+                    # We just want the base package name.
+                    match = re.match(r"^([a-zA-Z0-9_\-]+)", requirement)
+                    if match:
+                        dep_name = match.group(0).lower().replace("_", "-")
+                        if dep_name not in visited:
+                            packages_to_check.append(dep_name)
+        except importlib.metadata.PackageNotFoundError:
+            # If a package is listed as a dependency but not installed, we skip it.
+            continue
+
+    required_packages_sorted = sorted(required_packages)
+
+    # Lookup versions for all discovered packages.
+    dependencies = {}
+    version_info = get_heretic_version_info()
+
+    for package in required_packages_sorted:
+        # If heretic-llm was installed from source (Git/Local), exclude it
+        # from requirements.txt to prevent pip from downloading an unrelated
+        # version from PyPI during reproduction.
+        if package == "heretic-llm" and not version_info.is_standard_pypi:
+            continue
+
+        dependencies[package] = get_package_version(package)
+
+    return dependencies

src/heretic/utils.py

@@ -1,22 +1,22 @@
 # SPDX-License-Identifier: AGPL-3.0-or-later
 # Copyright (C) 2025-2026  Philipp Emanuel Weidmann <pew@worldwidemann.com> + contributors
 
-import gc
 import getpass
+import json
 import os
+import platform
+import random
+import tempfile
 from dataclasses import dataclass
-from importlib.metadata import version
+from datetime import datetime, timezone
 from pathlib import Path
 from typing import Any, TypeVar
 
+import huggingface_hub
+import numpy as np
 import questionary
+import tomli_w
 import torch
-from accelerate.utils import (
-    is_mlu_available,
-    is_musa_available,
-    is_sdaa_available,
-    is_xpu_available,
-)
 from datasets import DatasetDict, ReadInstruction, load_dataset, load_from_disk
 from datasets.config import DATASET_STATE_JSON_FILENAME
 from datasets.download.download_manager import DownloadMode
@@ -25,9 +25,16 @@ from optuna import Trial
 from psutil import Process
 from questionary import Choice, Style
 from rich.console import Console
-from torch import Tensor
 
-from .config import DatasetSpecification, RowNormalization, Settings
+from .config import DatasetSpecification, Settings
+from .system import (
+    get_accelerator_info_dict,
+    get_cpu_info_dict,
+    get_heretic_version_info,
+    get_python_env_info_dict,
+    get_requirements_dict,
+    is_xpu_available,
+)
 
 print = Console(highlight=False).print
 
@@ -161,6 +168,18 @@ def format_duration(seconds: float) -> str:
         return f"{seconds}s"
 
 
+def is_hf_path(path: str) -> bool:
+    """Checks whether a path likely refers to a Hugging Face repository."""
+
+    return (
+        not path.startswith("/")
+        and not path.endswith("/")
+        and path.count("/") == 1
+        and "\\" not in path
+        and not Path(path).exists()
+    )
+
+
 @dataclass
 class Prompt:
     system: str
@@ -174,7 +193,13 @@ def load_prompts(
     path = specification.dataset
     split_str = specification.split
 
-    if os.path.isdir(path):
+    if is_hf_path(path):
+        dataset = load_dataset(
+            path,
+            revision=specification.commit,
+            split=split_str,
+        )
+    else:
         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.
@@ -192,7 +217,7 @@ def load_prompts(
             # Get the dataset by applying the indices.
             dataset = dataset[abs_instruction.from_ : abs_instruction.to]
         else:
-            # Path is a local directory.
+            # Path should be a local directory.
             dataset = load_dataset(
                 path,
                 split=split_str,
@@ -201,9 +226,6 @@ def load_prompts(
                 # But also don't use cached data, as the dataset may have changed on disk.
                 download_mode=DownloadMode.FORCE_REDOWNLOAD,
             )
-    else:
-        # Probably a repository path; let load_dataset figure it out.
-        dataset = load_dataset(path, split=split_str)
 
     prompts = list(dataset[specification.column])
 
@@ -235,96 +257,48 @@ 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)]
 
 
-# For each vector in the 2D-tensor `a`, computes the mean Euclidean distance
-# to the `k` nearest neighbors of the vector among the vectors in the 2D-tensor `b`.
-def mean_distances_to_knn(a: Tensor, b: Tensor, k: int) -> Tensor:
-    distances = torch.cdist(a, b)
-    nearest_distances, _ = distances.topk(k, dim=1, largest=False)
-    return nearest_distances.mean(1)
+def get_trial_parameters(trial: Trial) -> dict[str, str]:
+    params = {}
 
+    direction_index = trial.user_attrs["direction_index"]
+    params["direction_index"] = (
+        "per layer" if (direction_index is None) else f"{direction_index:.2f}"
+    )
 
-def empty_cache():
-    # Collecting garbage is not an idempotent operation, and to avoid OOM errors,
-    # gc.collect() has to be called both before and after emptying the backend cache.
-    # See https://github.com/p-e-w/heretic/pull/17 for details.
-    gc.collect()
+    for component, parameters in trial.user_attrs["parameters"].items():
+        for name, value in parameters.items():
+            params[f"{component}.{name}"] = f"{value:.2f}"
 
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-    elif is_xpu_available():
-        torch.xpu.empty_cache()
-    elif is_mlu_available():
-        torch.mlu.empty_cache()  # ty:ignore[unresolved-attribute]
-    elif is_sdaa_available():
-        torch.sdaa.empty_cache()  # ty:ignore[unresolved-attribute]
-    elif is_musa_available():
-        torch.musa.empty_cache()  # ty:ignore[unresolved-attribute]
-    elif torch.backends.mps.is_available():
-        torch.mps.empty_cache()
-
-    gc.collect()
-
-
-def get_trial_parameters(settings: Settings, trial: Trial) -> dict[str, str]:
-    if settings.use_ara:
-        parameters = trial.user_attrs["ara_parameters"]
-
-        return {
-            name: (f"{value:.4f}" if isinstance(value, float) else f"{value}")
-            for name, value in parameters.items()
-        }
-    else:
-        params = {}
-
-        direction_index = trial.user_attrs["direction_index"]
-        params["direction_index"] = (
-            "per layer" if (direction_index is None) else f"{direction_index:.2f}"
-        )
-
-        for component, parameters in trial.user_attrs["parameters"].items():
-            for name, value in parameters.items():
-                params[f"{component}.{name}"] = f"{value:.2f}"
-
-        return params
-
-
-def get_method_description(settings: Settings) -> str:
-    if settings.use_ara:
-        return (
-            " with the [Arbitrary-Rank Ablation (ARA)](https://github.com/p-e-w/heretic/pull/211) method"
-            + (
-                " (with row-norm preservation)"
-                if settings.row_normalization == RowNormalization.FULL
-                else ""
-            )
-        )
-    elif (
-        settings.orthogonalize_direction
-        and settings.row_normalization == RowNormalization.FULL
-    ):
-        return " with a variant of the [Magnitude-Preserving Orthogonal Ablation (MPOA)](https://huggingface.co/blog/grimjim/norm-preserving-biprojected-abliteration) method"
-    else:
-        return ""
+    return params
 
 
 def get_readme_intro(
     settings: Settings,
     trial: Trial,
-    base_refusals: int,
-    bad_prompts: list[Prompt],
+    contains_reproducibility_information: bool,
 ) -> str:
-    if Path(settings.model).exists():
+    if is_hf_path(settings.model):
+        model_link = f"[{settings.model}](https://huggingface.co/{settings.model})"
+    else:
         # Hide the path, which may contain private information.
         model_link = "a model"
+
+    version_info = get_heretic_version_info()
+
+    if contains_reproducibility_information:
+        reproducibility_instructions = """
+> [!TIP]
+> **This model is reproducible!**
+>
+> See the [README](reproduce/README.md) in the `reproduce` directory for more information.
+"""
     else:
-        model_link = f"[{settings.model}](https://huggingface.co/{settings.model})"
+        reproducibility_instructions = ""
 
     return f"""# This is a decensored version of {
         model_link
-    }, made using [Heretic](https://github.com/p-e-w/heretic) v{version("heretic-llm")}{
-        get_method_description(settings)
-    }
-
+    }, made using [Heretic](https://github.com/p-e-w/heretic) v{version_info.version}
+{reproducibility_instructions}
 ## Abliteration parameters
 
 | Parameter | Value |
@@ -333,7 +307,7 @@ def get_readme_intro(
         chr(10).join(
             [
                 f"| **{name}** | {value} |"
-                for name, value in get_trial_parameters(settings, trial).items()
+                for name, value in get_trial_parameters(trial).items()
             ]
         )
     }
@@ -342,14 +316,407 @@ def get_readme_intro(
 
 | Metric | This model | Original model ({model_link}) |
 | :----- | :--------: | :---------------------------: |
-| **{"PIQA acc_norm" if settings.use_piqa else "KL divergence"}** | {
-        (-1 if settings.use_piqa else 1) * trial.user_attrs["kl_divergence"]:.4f} | {
-        "*Unknown*" if settings.use_piqa else "0 *(by definition)*"
-    } |
-| **Refusals** | {trial.user_attrs["refusals"]}/{len(bad_prompts)} | {base_refusals}/{
-        len(bad_prompts)
-    } |
+| **KL divergence** | {trial.user_attrs["kl_divergence"]:.4f} | 0 *(by definition)* |
+| **Refusals** | {trial.user_attrs["refusals"]}/{trial.user_attrs["n_bad_prompts"]} | {
+        trial.user_attrs["base_refusals"]
+    }/{trial.user_attrs["n_bad_prompts"]} |
 
 -----
 
 """
+
+
+def generate_config_toml(settings: Settings) -> str:
+    """Serializes the full Settings object to TOML."""
+
+    return tomli_w.dumps(settings.model_dump(exclude_none=True))
+
+
+def generate_requirements_txt() -> str:
+    """Collects direct project dependencies as a formatted string."""
+
+    requirements = [
+        f"{package}=={version}" for package, version in get_requirements_dict().items()
+    ]
+    return "\n".join(requirements) + "\n"
+
+
+def set_seed(seed: int):
+    """Sets the seed for all RNGs."""
+
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+
+
+def format_hf_link(
+    path: str,
+    commit: str | None = None,
+    is_dataset: bool = False,
+) -> str:
+    prefix = "datasets/" if is_dataset else ""
+    base_url = f"https://huggingface.co/{prefix}{path}"
+    link = f"[{path}]({base_url})"
+
+    if commit:
+        commit_url = f"{base_url}/commit/{commit}"
+        link += f" (Commit: [`{commit[:7]}`]({commit_url}))"
+
+    return link
+
+
+def generate_reproduce_readme(
+    settings: Settings,
+    checkpoint_filename: str,
+    trial: Trial,
+    include_system_information: bool,
+) -> str:
+    """Generates the contents of a README.md for the reproduce/ folder."""
+
+    heterogeneous_warning = ""
+
+    if include_system_information:
+        if torch.cuda.is_available():
+            count = torch.cuda.device_count()
+            if count > 1:
+                device_names = {torch.cuda.get_device_name(i) for i in range(count)}
+                if len(device_names) > 1:
+                    heterogeneous_warning = """
+> [!WARNING]
+> **Heterogeneous GPUs**
+>
+> This model was generated using multiple non-identical GPUs. When operations are distributed across different GPUs
+> (e.g. via `device_map='auto'`), non-deterministic behavior can occur.
+>
+> Reproducibility *cannot* be guaranteed in this environment.
+"""
+
+        cpu = get_cpu_info_dict()
+        python_env = get_python_env_info_dict()
+
+        accelerators = get_accelerator_info_dict()
+        if accelerators["type"] is None:
+            accelerator_report = "**No GPU or other accelerator detected.**"
+        else:
+            devices = accelerators["devices"]
+            total_vram = sum(device.get("vram_gb", 0) for device in devices)
+            vram_suffix = f" ({total_vram:.2f} GB total VRAM)" if total_vram > 0 else ""
+            accelerator_lines = [
+                f"- **{accelerators['type']}:** Detected {len(devices)} device(s){vram_suffix}"
+            ]
+
+            if accelerators.get("api_name") and accelerators.get("api_version"):
+                accelerator_lines.append(
+                    f"  - **{accelerators['api_name']}:** {accelerators['api_version']}"
+                )
+
+            if accelerators.get("driver_version"):
+                accelerator_lines.append(
+                    f"  - **Driver Version:** {accelerators['driver_version']}"
+                )
+
+            accelerator_lines.append("- **Devices:**")
+            for i, device in enumerate(devices):
+                vram = f" ({device['vram_gb']:.2f} GB)" if device.get("vram_gb") else ""
+                accelerator_lines.append(
+                    f"  - **{accelerators['type']} {i}:** {device['name']}{vram}"
+                )
+            accelerator_report = "\n".join(accelerator_lines)
+
+        system_report = f"""## System
+
+- **Python:** {python_env["version"]} ({python_env["implementation"]}, {python_env["compiler"]}) [{python_env["environment"]}]
+- **Operating system:** {platform.platform()} ({platform.machine()})
+- **CPU:** {cpu["brand"] or "Unknown"}
+
+### Accelerators
+
+{accelerator_report}
+
+"""
+        system_instructions = (
+            "1. Ensure your system matches the specifications in the **System** section above. "
+            "Exact reproducibility is only guaranteed if all aspects of your system are identical to the one the model was originally generated on.\n"
+        )
+    else:
+        system_report = ""
+        system_instructions = ""
+
+    version_info = get_heretic_version_info()
+    origin_warning = ""
+    if not version_info.is_standard_pypi:
+        if version_info.origin and version_info.origin.startswith("Git"):
+            repo_info = version_info.origin.split("Git (")[1].rstrip(")")
+            origin_warning = f"""
+> [!IMPORTANT]
+> **Git installation**
+>
+> This system installed Heretic from a Git repository: {repo_info}
+>
+> To reproduce the model, you must install Heretic from this exact repository and commit.
+"""
+        elif version_info.origin == "Local":
+            origin_warning = """
+> [!WARNING]
+> **Local code**
+>
+> This system installed Heretic from a local directory or wheel. Uncommitted or experimental code may have been executed.
+>
+> Reproducibility *cannot* be guaranteed in this environment.
+"""
+        else:
+            origin_warning = """
+> [!WARNING]
+> **Non-standard installation**
+>
+> This system installed Heretic from an unknown non-standard source.
+>
+> Reproducibility *cannot* be guaranteed in this environment.
+"""
+
+    pytorch_version = torch.__version__
+    pytorch_install_command = f"pip install torch=={pytorch_version}"
+    if "+" in pytorch_version:
+        suffix = pytorch_version.split("+")[1]
+        if suffix:
+            pytorch_install_command += (
+                f" --index-url https://download.pytorch.org/whl/{suffix}"
+            )
+
+    return f"""# Reproduction guide
+
+This directory contains the necessary information and assets to reproduce the results obtained during this Heretic run.{heterogeneous_warning}{origin_warning}
+
+## Models
+
+- **Base model:** {format_hf_link(settings.model, settings.model_commit)}
+
+## Datasets
+
+- **Good prompts:** {format_hf_link(settings.good_prompts.dataset, settings.good_prompts.commit, is_dataset=True)}
+- **Bad prompts:** {format_hf_link(settings.bad_prompts.dataset, settings.bad_prompts.commit, is_dataset=True)}
+- **Good evaluation prompts:** {format_hf_link(settings.good_evaluation_prompts.dataset, settings.good_evaluation_prompts.commit, is_dataset=True)}
+- **Bad evaluation prompts:** {format_hf_link(settings.bad_evaluation_prompts.dataset, settings.bad_evaluation_prompts.commit, is_dataset=True)}
+
+## Selected trial
+
+- **Trial number:** {trial.user_attrs["index"]}
+- **KL divergence:** {trial.user_attrs["kl_divergence"]:.6f}
+- **Refusals:** {trial.user_attrs["refusals"]}/{trial.user_attrs["n_bad_prompts"]}
+
+{system_report}## Environment
+
+- **Heretic:** v{version_info.version}{f" (Origin: {version_info.origin})" if version_info.origin else ""}
+- **PyTorch:** {pytorch_version}
+- **Other dependencies:** See [`requirements.txt`](requirements.txt).
+
+## Contents of this directory
+
+- [`requirements.txt`](requirements.txt): The exact versions of all Python packages.
+- [`config.toml`](config.toml): The exact configuration used, including the RNG seed.
+- [`{checkpoint_filename}`]({checkpoint_filename}): The Optuna study journal containing the history of all trials.
+- [`SHA256SUMS`](SHA256SUMS): Cryptographic hashes for all weight files.
+- [`reproduce.json`](reproduce.json): A machine-readable file containing all reproducibility information.
+
+## How to reproduce
+
+{system_instructions}1. Install the exact version of Heretic indicated in the **Environment** section above, from its original source.
+1. Install the packages listed in `requirements.txt`: `pip install -r requirements.txt`
+1. Install the correct version of PyTorch: `{pytorch_install_command}`
+1. Place the provided `config.toml` in your working directory.
+1. Run Heretic without any additional arguments: `heretic`
+1. Wait for the run to finish, then select trial **{trial.user_attrs["index"]}** and export the model.
+1. Verify that the weight files have been exactly reproduced by comparing their SHA-256 hashes against those in `SHA256SUMS`: `sha256sum -c SHA256SUMS` (or look at the hashes online if you uploaded to Hugging Face)
+
+> [!TIP]
+> To use the included Optuna study journal `{checkpoint_filename}`, place it in the checkpoints directory (usually `checkpoints/`) before running Heretic.
+>
+> This allows you to export other models from the Pareto front, or to run additional trials without having to re-run the stored trials.
+"""
+
+
+def generate_reproduce_json(
+    settings: Settings,
+    trial: Trial,
+    timestamp: str,
+    uploaded_model_hashes: dict[str, str],
+    include_system_information: bool,
+) -> str:
+    """Generates the contents of a reproduce.json file for the reproduce/ folder."""
+
+    version_info = get_heretic_version_info()
+
+    data = {
+        "version": "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": {
+            "heretic": {
+                "version": version_info.version,
+                "is_standard_pypi": version_info.is_standard_pypi,
+                "metadata": version_info.metadata,
+            },
+            "pytorch_version": torch.__version__,
+            "requirements": get_requirements_dict(),
+        },
+        "settings": settings.model_dump(),
+        "parameters": {
+            "direction_index": trial.user_attrs["direction_index"],
+            "abliteration_parameters": trial.user_attrs["parameters"],
+        },
+        "metrics": {
+            "kl_divergence": trial.user_attrs["kl_divergence"],
+            "refusals": trial.user_attrs["refusals"],
+            "base_refusals": trial.user_attrs["base_refusals"],
+            "n_bad_prompts": trial.user_attrs["n_bad_prompts"],
+        },
+        "hashes": uploaded_model_hashes,
+    }
+
+    if include_system_information:
+        data["system"] = {
+            "python": get_python_env_info_dict(),
+            "os": {
+                "platform": platform.platform(),
+                "machine": platform.machine(),
+            },
+            "cpu": get_cpu_info_dict(),
+            "accelerators": get_accelerator_info_dict(),
+        }
+    else:
+        del data["system"]
+
+    return json.dumps(data, indent=4)
+
+
+def generate_sha256sums(hashes: dict[str, str]) -> str:
+    """Generates GNU Coreutils compatible SHA256SUMS file content."""
+
+    lines = []
+
+    for filename, sha256 in sorted(hashes.items()):
+        # Use '*' to indicate binary mode for model weights.
+        lines.append(f"{sha256} *{filename}")
+
+    return "\n".join(lines) + "\n"
+
+
+def create_reproduce_folder(
+    path: Path,
+    settings: Settings,
+    checkpoint_path: str | Path,
+    trial: Trial,
+    uploaded_model_hashes: dict[str, str],
+    include_system_information: bool,
+):
+    reproduce_dir = path / "reproduce"
+    reproduce_dir.mkdir(parents=True, exist_ok=True)
+
+    checkpoint_filename = Path(checkpoint_path).name
+
+    # Fetch commit hash for the base model.
+    settings.model_commit = huggingface_hub.model_info(settings.model).sha
+
+    # Fetch commit hashes for all HF datasets to ensure reproducibility.
+    for spec in [
+        settings.good_prompts,
+        settings.bad_prompts,
+        settings.good_evaluation_prompts,
+        settings.bad_evaluation_prompts,
+    ]:
+        spec.commit = huggingface_hub.dataset_info(spec.dataset).sha
+
+    # Strip microseconds and timezone for a clean format.
+    timestamp = (
+        datetime.now(timezone.utc).replace(microsecond=0, tzinfo=None).isoformat()
+    )
+
+    (reproduce_dir / "requirements.txt").write_text(
+        generate_requirements_txt(),
+        encoding="utf-8",
+    )
+
+    (reproduce_dir / "config.toml").write_text(
+        generate_config_toml(settings),
+        encoding="utf-8",
+    )
+
+    if uploaded_model_hashes:
+        (reproduce_dir / "SHA256SUMS").write_text(
+            generate_sha256sums(uploaded_model_hashes),
+            encoding="utf-8",
+        )
+
+    (reproduce_dir / "reproduce.json").write_text(
+        generate_reproduce_json(
+            settings,
+            trial,
+            timestamp=timestamp,
+            uploaded_model_hashes=uploaded_model_hashes,
+            include_system_information=include_system_information,
+        ),
+        encoding="utf-8",
+    )
+
+    (reproduce_dir / "README.md").write_text(
+        generate_reproduce_readme(
+            settings,
+            checkpoint_filename,
+            trial,
+            include_system_information=include_system_information,
+        ),
+        encoding="utf-8",
+    )
+
+    # Copy Optuna study journal.
+    checkpoint_file = Path(checkpoint_path)
+    if checkpoint_file.exists():
+        (reproduce_dir / checkpoint_file.name).write_bytes(checkpoint_file.read_bytes())
+
+
+def upload_reproduce_folder(
+    repo_id: str,
+    settings: Settings,
+    token: str,
+    checkpoint_path: str | Path,
+    trial: Trial,
+    include_system_information: bool,
+):
+    api = huggingface_hub.HfApi()
+    info = api.model_info(repo_id=repo_id, files_metadata=True, token=token)
+
+    if not info.siblings:
+        raise RuntimeError("Could not fetch uploaded model hashes.")
+
+    # For weights, we only care about safetensors.
+    weight_extensions = (".safetensors",)
+
+    uploaded_model_hashes = {}
+
+    for file in info.siblings:
+        if file.rfilename.endswith(weight_extensions):
+            sha256 = getattr(file, "lfs", {}).get("sha256")
+            if not sha256:
+                raise RuntimeError("Could not fetch uploaded model hashes.")
+            uploaded_model_hashes[file.rfilename] = sha256
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        tmp_path = Path(tmpdir)
+        create_reproduce_folder(
+            tmp_path,
+            settings,
+            checkpoint_path=checkpoint_path,
+            trial=trial,
+            uploaded_model_hashes=uploaded_model_hashes,
+            include_system_information=include_system_information,
+        )
+
+        reproduce_dir = tmp_path / "reproduce"
+        for file_path in reproduce_dir.iterdir():
+            if file_path.is_file():
+                huggingface_hub.upload_file(
+                    path_or_fileobj=str(file_path),
+                    path_in_repo=f"reproduce/{file_path.name}",
+                    repo_id=repo_id,
+                    token=token,
+                )

uv.lock

@@ -7,6 +7,10 @@ resolution-markers = [
     "python_full_version < '3.11'",
 ]
 
+[options]
+exclude-newer = "2026-04-14T22:48:57.86057843Z"
+exclude-newer-span = "P7D"
+
 [[package]]
 name = "absl-py"
 version = "2.4.0"
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+version = "3.13.4"
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