Merge branch 'master' into ara

README.md

@@ -2,6 +2,8 @@
 
 # Heretic: Fully automatic censorship removal for language models<br><br>[![Discord](https://img.shields.io/discord/1447831134212984903?color=5865F2&label=discord&labelColor=black&logo=discord&logoColor=white&style=for-the-badge)](https://discord.gg/gdXc48gSyT) [![Follow us on Hugging Face](https://huggingface.co/datasets/huggingface/badges/resolve/main/follow-us-on-hf-md-dark.svg)](https://huggingface.co/heretic-org)
 
+[![#1 Repository of the Day](https://trendshift.io/api/badge/repositories/20538)](https://trendshift.io/repositories/20538)
+
 Heretic is a tool that removes censorship (aka "safety alignment") from
 transformer-based language models without expensive post-training.
 It combines an advanced implementation of directional ablation, also known

pyproject.toml

@@ -22,20 +22,24 @@ classifiers = [
     "Programming Language :: Python :: 3.12",
 ]
 dependencies = [
-    "accelerate~=1.10",
-    "bitsandbytes~=0.45",
-    "datasets~=4.0",
+    "accelerate~=1.13",
+    "bitsandbytes~=0.49",
+    "datasets~=4.7",
     "hf-transfer~=0.1",
-    "huggingface-hub~=0.34",
-    "kernels~=0.11",
-    "lm-eval[hf]~=0.4.11",
-    "optuna~=4.5",
-    "peft~=0.14",
-    "psutil~=7.1",
-    "pydantic-settings~=2.10",
+    "huggingface-hub~=1.7",
+    "immutabledict~=4.3",
+    "kernels~=0.12",
+    "langdetect~=1.0",
+    "lm-eval[hf]~=0.4",
+    "numpy~=2.2",
+    "optuna~=4.7",
+    "peft~=0.18",
+    "psutil~=7.2",
+    "pydantic-settings~=2.13",
     "questionary~=2.1",
-    "rich~=14.1",
-    "transformers~=4.57",
+    "rich~=14.3",
+    "tqdm~=4.67",
+    "transformers~=5.3",
 ]
 
 [project.optional-dependencies]
@@ -43,7 +47,6 @@ research = [
     "geom-median~=0.1",
     "imageio~=2.37",
     "matplotlib~=3.10",
-    "numpy~=2.2",
     "pacmap~=0.8",
     "scikit-learn~=1.7",
 ]

src/heretic/analyzer.py

@@ -3,9 +3,11 @@
 
 from pathlib import Path
 
+import numpy as np
 import torch
 import torch.linalg as LA
 import torch.nn.functional as F
+from numpy.typing import NDArray
 from rich.progress import track
 from rich.table import Table
 from torch import Tensor
@@ -156,11 +158,9 @@ class Analyzer:
         try:
             import imageio.v3 as iio  # ty:ignore[unresolved-import]
             import matplotlib.pyplot as plt  # ty:ignore[unresolved-import]
-            import numpy as np  # ty:ignore[unresolved-import]
             from geom_median.numpy import (  # ty:ignore[unresolved-import]
                 compute_geometric_median,
             )
-            from numpy.typing import NDArray  # ty:ignore[unresolved-import]
             from pacmap import PaCMAP  # ty:ignore[unresolved-import]
         except ImportError:
             print()

src/heretic/config.py

@@ -61,6 +61,18 @@ class DatasetSpecification(BaseModel):
     )
 
 
+class BenchmarkSpecification(BaseModel):
+    task: str = Field(
+        description="Task ID of the benchmark in the Language Model Evaluation Harness."
+    )
+
+    name: str = Field(description="Name of the benchmark for presentation purposes.")
+
+    description: str = Field(
+        description="Description of the benchmark for presentation purposes."
+    )
+
+
 class Settings(BaseSettings):
     model: str = Field(description="Hugging Face model ID, or path to model on disk.")
 
@@ -254,6 +266,67 @@ class Settings(BaseSettings):
         description="Directory to save and load study progress to/from.",
     )
 
+    benchmarks: list[BenchmarkSpecification] = Field(
+        default=[
+            BenchmarkSpecification(
+                task="agieval",
+                name="AGIEval",
+                description="A Human-Centric Benchmark for Evaluating Foundation Models",
+            ),
+            BenchmarkSpecification(
+                task="bbh",
+                name="BIG-Bench Hard (BBH)",
+                description="Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them",
+            ),
+            BenchmarkSpecification(
+                task="commonsense_qa",
+                name="CommonsenseQA",
+                description="A Question Answering Challenge Targeting Commonsense Knowledge",
+            ),
+            BenchmarkSpecification(
+                task="eq_bench",
+                name="EQ-Bench",
+                description="An Emotional Intelligence Benchmark for Large Language Models",
+            ),
+            BenchmarkSpecification(
+                task="gsm8k",
+                name="GSM8K",
+                description="Training Verifiers to Solve Math Word Problems",
+            ),
+            BenchmarkSpecification(
+                task="hellaswag",
+                name="HellaSwag",
+                description="Can a Machine Really Finish Your Sentence?",
+            ),
+            BenchmarkSpecification(
+                task="ifeval",
+                name="IFEval",
+                description="Instruction-Following Evaluation for Large Language Models",
+            ),
+            BenchmarkSpecification(
+                task="mmlu",
+                name="MMLU",
+                description="Measuring Massive Multitask Language Understanding",
+            ),
+            BenchmarkSpecification(
+                task="mmlu_pro",
+                name="MMLU-Pro",
+                description="A More Robust and Challenging Multi-Task Language Understanding Benchmark",
+            ),
+            BenchmarkSpecification(
+                task="piqa",
+                name="PIQA",
+                description="Reasoning about Physical Commonsense in Natural Language",
+            ),
+            BenchmarkSpecification(
+                task="winogrande",
+                name="WinoGrande",
+                description="An Adversarial Winograd Schema Challenge at Scale",
+            ),
+        ],
+        description="Benchmarks to offer to the user for evaluating abliterated models.",
+    )
+
     refusal_markers: list[str] = Field(
         default=[
             "sorry",

src/heretic/evaluator.py

@@ -121,7 +121,9 @@ class Evaluator:
         refusals = self.count_refusals()
         print(f"  * Refusals: [bold]{refusals}[/]/{len(self.bad_prompts)}")
 
-        refusals_score = refusals / self.base_refusals
+        refusals_score = (
+            refusals / self.base_refusals if self.base_refusals > 0 else float(refusals)
+        )
 
         if self.settings.use_piqa:
             score = (

src/heretic/main.py

@@ -1,6 +1,15 @@
 # SPDX-License-Identifier: AGPL-3.0-or-later
 # Copyright (C) 2025-2026  Philipp Emanuel Weidmann <pew@worldwidemann.com> + contributors
 
+# ruff: noqa: E402
+
+from .progress import patch_tqdm
+
+# This patches tqdm class definitions, which must happen
+# before any other module imports tqdm.
+patch_tqdm()
+
+import logging
 import math
 import os
 import sys
@@ -10,9 +19,13 @@ from dataclasses import asdict
 from importlib.metadata import version
 from os.path import commonprefix
 from pathlib import Path
+from typing import Any
 
 import huggingface_hub
+import lm_eval
+import numpy as np
 import optuna
+import questionary
 import torch
 import torch.nn.functional as F
 import transformers
@@ -24,6 +37,7 @@ from accelerate.utils import (
     is_xpu_available,
 )
 from huggingface_hub import ModelCard, ModelCardData
+from lm_eval.models.huggingface import HFLM
 from optuna import Trial, TrialPruned
 from optuna.exceptions import ExperimentalWarning
 from optuna.samplers import TPESampler
@@ -32,7 +46,8 @@ from optuna.storages.journal import JournalFileBackend, JournalFileOpenLock
 from optuna.study import StudyDirection
 from optuna.trial import TrialState
 from pydantic import ValidationError
-from questionary import Choice
+from questionary import Choice, Style
+from rich.table import Table
 from rich.traceback import install
 
 from .analyzer import Analyzer
@@ -174,9 +189,15 @@ def run():
     # Adapted from https://github.com/huggingface/accelerate/blob/main/src/accelerate/commands/env.py
     if torch.cuda.is_available():
         count = torch.cuda.device_count()
-        print(f"Detected [bold]{count}[/] CUDA device(s):")
+        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):
-            print(f"* GPU {i}: [bold]{torch.cuda.get_device_name(i)}[/]")
+            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):")
@@ -220,6 +241,9 @@ def run():
     # In my entire career I've never seen a useful warning from that library.
     transformers.logging.set_verbosity_error()
 
+    # Another library that generates warning spam.
+    logging.getLogger("lm_eval").setLevel(logging.ERROR)
+
     # We do our own trial logging, so we don't need the INFO messages
     # about parameters and results.
     optuna.logging.set_verbosity(optuna.logging.WARNING)
@@ -372,7 +396,8 @@ def run():
 
     print()
     print("Checking for common response prefix...")
-    responses = model.get_responses_batched(good_prompts[:100] + bad_prompts[:100])
+    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,
@@ -383,24 +408,39 @@ def run():
     model.response_prefix = commonprefix(responses).rstrip(" ")
 
     # Suppress CoT output.
-    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]"
+    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...")
+        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}[/]")
+
     evaluator = Evaluator(settings, model)
 
     if settings.evaluate_model is not None:
@@ -800,6 +840,7 @@ def run():
                         "Save the model to a local folder",
                         "Upload the model to Hugging Face",
                         "Chat with the model",
+                        "Benchmark the model",
                         "Return to the trial selection menu",
                     ],
                 )
@@ -868,6 +909,8 @@ def run():
                                     "Private",
                                 ],
                             )
+                            if visibility is None:
+                                continue
                             private = visibility == "Private"
 
                             strategy = obtain_merge_strategy(settings)
@@ -901,11 +944,23 @@ def run():
                                     token=token,
                                 )
 
-                            # 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
+                            # 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.
-                            if not Path(settings.model).exists():
+                            model_path = Path(settings.model)
+                            if model_path.exists():
+                                card_path = (
+                                    model_path / 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()
                                 if card.data.tags is None:
@@ -965,6 +1020,113 @@ def run():
                                     # Ctrl+C/Ctrl+D
                                     break
 
+                        case "Benchmark the model":
+                            benchmarks = questionary.checkbox(
+                                "Which benchmarks do you want to run?",
+                                [
+                                    Choice(
+                                        title=f"{benchmark.name}: {benchmark.description}",
+                                        value=benchmark,
+                                    )
+                                    for benchmark in settings.benchmarks
+                                ],
+                                style=Style([("highlighted", "reverse")]),
+                            ).ask()
+                            if not benchmarks:
+                                continue
+
+                            scope = prompt_select(
+                                (
+                                    "Do you want to benchmark the original model along with the decensored model? "
+                                    "Benchmarking both models allows you to compare the scores, but it takes twice as much time."
+                                ),
+                                [
+                                    "Benchmark only the decensored model",
+                                    "Benchmark both models",
+                                ],
+                            )
+                            if scope is None:
+                                continue
+                            benchmark_original_model = scope == "Benchmark both models"
+
+                            hflm = HFLM(
+                                pretrained=model.model,  # ty:ignore[invalid-argument-type]
+                                tokenizer=model.tokenizer,  # ty:ignore[invalid-argument-type]
+                            )
+
+                            table = Table()
+                            table.add_column("Benchmark")
+                            table.add_column("Metric")
+                            if benchmark_original_model:
+                                table.add_column("This model", justify="right")
+                                table.add_column("Original model", justify="right")
+                            else:
+                                table.add_column("Value", justify="right")
+
+                            try:
+                                first_benchmark = True
+
+                                for benchmark in benchmarks:
+                                    print(
+                                        f"Running benchmark [bold]{benchmark.name}[/]..."
+                                    )
+
+                                    def get_results() -> dict[str, Any]:
+                                        results = lm_eval.simple_evaluate(
+                                            model=hflm,
+                                            tasks=[benchmark.task],
+                                            batch_size="auto",
+                                        )
+                                        return results["results"][benchmark.task]
+
+                                    results = get_results()
+                                    if benchmark_original_model:
+                                        with model.model.disable_adapter():  # ty:ignore[call-non-callable]
+                                            original_results = get_results()
+
+                                    first_row = True
+
+                                    for metric, value in results.items():
+                                        if metric != "alias":
+                                            if first_row and not first_benchmark:
+                                                if benchmark_original_model:
+                                                    table.add_row("", "", "", "")
+                                                else:
+                                                    table.add_row("", "", "")
+
+                                            def format_value(value: Any) -> str:
+                                                if isinstance(
+                                                    value,
+                                                    (float, np.floating),
+                                                ):
+                                                    return f"{value:.4f}"
+                                                else:
+                                                    return f"{value}"
+
+                                            cells = [
+                                                benchmark.name if first_row else "",
+                                                metric,
+                                                format_value(value),
+                                            ]
+                                            if benchmark_original_model:
+                                                cells.append(
+                                                    format_value(
+                                                        original_results[metric]
+                                                    )
+                                                )
+                                            table.add_row(*cells)
+
+                                            first_row = False
+                                            first_benchmark = False
+                            except KeyboardInterrupt:
+                                pass
+
+                            # The benchmark run might have been cancelled by the user
+                            # before any benchmark was completed, so we only print results
+                            # if there actually are some.
+                            if table.rows:
+                                print(table)
+
                 except Exception as error:
                     print(f"[red]Error: {error}[/]")
 

src/heretic/model.py

@@ -110,7 +110,7 @@ class Model:
             self.trusted_models[settings.evaluate_model] = settings.trust_remote_code
 
         for dtype in settings.dtypes:
-            print(f"* Trying dtype [bold]{dtype}[/]... ", end="")
+            print(f"* Trying dtype [bold]{dtype}[/]...")
 
             try:
                 quantization_config = self._get_quantization_config(dtype)
@@ -150,13 +150,11 @@ class Model:
             except Exception as error:
                 self.model = None  # ty:ignore[invalid-assignment]
                 empty_cache()
-                print(f"[red]Failed[/] ({error})")
+                print(f"* [red]Failed[/] ({error})")
                 continue
 
             if settings.quantization == QuantizationMethod.BNB_4BIT:
-                print("[green]Ok[/] (quantized to 4-bit precision)")
-            else:
-                print("[green]Ok[/]")
+                print("* Quantized to 4-bit precision")
 
             break
 
@@ -171,24 +169,38 @@ class Model:
 
         print(f"* Transformer model with [bold]{len(self.get_layers())}[/] layers")
         print("* Abliterable components:")
-        for component, modules in self.get_layer_modules(0).items():
-            print(
-                f"  * [bold]{component}[/]: [bold]{len(modules)}[/] modules per layer"
-            )
+        all_components = {}
+        for layer_index in range(len(self.get_layers())):
+            for component, modules in self.get_layer_modules(layer_index).items():
+                if component not in all_components:
+                    all_components[component] = 0
+                all_components[component] += len(modules)
+        for component, count in all_components.items():
+            print(f"  * [bold]{component}[/]: [bold]{count}[/] modules total")
 
     def _apply_lora(self):
         # Guard against calling this method at the wrong time.
         assert isinstance(self.model, PreTrainedModel)
 
         # Always use LoRA adapters for abliteration (faster reload, no weight modification).
-        # We use the leaf names (e.g. "o_proj") as target modules.
-        # This may cause LoRA adapters to be attached to unrelated modules (e.g. "conv.o_proj"),
-        # but this is harmless as we only abliterate the modules we target in `abliterate()`,
-        # leaving the others at their default (identity) state.
-        # NOTE: This will need to be updated when hybrid layer support (#43) is merged.
-        target_modules = [
-            comp.split(".")[-1] for comp in self.get_abliterable_components()
-        ]
+        # Collect actual leaf module names from the model for LoRA targeting.
+        # This is more robust than splitting component keys (e.g. "attn.o_proj" -> "o_proj")
+        # 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()
+
+        for layer_index, layer in enumerate(self.get_layers()):
+            module_id_to_leaf_name = {
+                id(module): module_name.split(".")[-1]
+                for module_name, module in layer.named_modules()
+            }
+
+            for modules in self.get_layer_modules(layer_index).values():
+                for module in modules:
+                    if id(module) in module_id_to_leaf_name:
+                        target_modules_set.add(module_id_to_leaf_name[id(module)])
+
+        target_modules = list(target_modules_set)
 
         if self.settings.row_normalization != RowNormalization.FULL:
             # Rank 1 is sufficient for directional ablation without renormalization.
@@ -368,9 +380,14 @@ class Model:
                     f"Unexpected Tensor in {component} - expected nn.Module"
                 )
 
-        # Exceptions aren't suppressed here, because there is currently
-        # no alternative location for the attention out-projection.
-        try_add("attn.o_proj", layer.self_attn.o_proj)  # ty:ignore[possibly-missing-attribute]
+        # Standard self-attention out-projection (most models).
+        with suppress(Exception):
+            try_add("attn.o_proj", layer.self_attn.o_proj)  # ty:ignore[possibly-missing-attribute]
+
+        # Qwen3.5 MoE hybrid layers use GatedDeltaNet (linear attention) instead
+        # of standard self-attention, so self_attn.o_proj doesn't exist on those layers.
+        with suppress(Exception):
+            try_add("attn.o_proj", layer.linear_attn.out_proj)  # ty:ignore[possibly-missing-attribute]
 
         # Most dense models.
         with suppress(Exception):
@@ -402,7 +419,12 @@ class Model:
         return modules
 
     def get_abliterable_components(self) -> list[str]:
-        return list(self.get_layer_modules(0).keys())
+        # Scan all layers because hybrid models (e.g. Qwen3.5 MoE) have different
+        # components on different layers (some have self_attn, others linear_attn).
+        components: set[str] = set()
+        for layer_index in range(len(self.get_layers())):
+            components.update(self.get_layer_modules(layer_index).keys())
+        return sorted(components)
 
     def abliterate(
         self,
@@ -959,7 +981,12 @@ class Model:
             max_new_tokens=4096,
         )  # ty:ignore[call-non-callable]
 
-        return self.tokenizer.decode(
-            outputs[0, inputs["input_ids"].shape[1] :],
-            skip_special_tokens=True,
+        # This cast is valid because str is the return type
+        # when passing a sequence of token IDs.
+        return cast(
+            str,
+            self.tokenizer.decode(
+                outputs[0, inputs["input_ids"].shape[1] :],
+                skip_special_tokens=True,
+            ),
         )

src/heretic/progress.py

@@ -0,0 +1,40 @@
+# SPDX-License-Identifier: AGPL-3.0-or-later
+# Copyright (C) 2025-2026  Philipp Emanuel Weidmann <pew@worldwidemann.com> + contributors
+
+from typing import Any
+
+import tqdm
+import tqdm.auto
+from rich.progress import Progress
+
+
+# A class that provides the same interface as tqdm,
+# but displays progress bars using Rich.
+class TqdmShim(tqdm.tqdm):
+    def __init__(self, *args: Any, **kwargs: Any):
+        self.rich_progress = Progress(transient=True)
+        self.rich_progress.start()
+        self.rich_task_id = self.rich_progress.add_task(
+            kwargs.get("desc", ""),
+            total=kwargs.get("total", None),
+        )
+
+        # Chain up to the parent constructor to ensure that the internal state of the superclass
+        # is correctly initialized, which some methods that we don't override might rely on.
+        super().__init__(*args, **kwargs)
+
+    def display(self, *args: Any, **kwargs: Any):
+        self.rich_progress.update(
+            self.rich_task_id,
+            description=self.desc,
+            total=self.total,
+            completed=self.n,
+        )
+
+    def close(self, *args: Any, **kwargs: Any):
+        self.rich_progress.stop()
+
+
+def patch_tqdm():
+    tqdm.tqdm = TqdmShim  # ty:ignore[invalid-assignment]
+    tqdm.auto.tqdm = TqdmShim  # ty:ignore[invalid-assignment]

src/heretic/utils.py

@@ -39,11 +39,17 @@ def print_memory_usage():
     p("Resident system RAM", Process().memory_info().rss)
 
     if torch.cuda.is_available():
-        p("Allocated GPU VRAM", torch.cuda.memory_allocated())
-        p("Reserved GPU VRAM", torch.cuda.memory_reserved())
+        count = torch.cuda.device_count()
+        allocated = sum(torch.cuda.memory_allocated(device) for device in range(count))
+        reserved = sum(torch.cuda.memory_reserved(device) for device in range(count))
+        p("Allocated GPU VRAM", allocated)
+        p("Reserved GPU VRAM", reserved)
     elif is_xpu_available():
-        p("Allocated XPU memory", torch.xpu.memory_allocated())
-        p("Reserved XPU memory", torch.xpu.memory_reserved())
+        count = torch.xpu.device_count()
+        allocated = sum(torch.xpu.memory_allocated(device) for device in range(count))
+        reserved = sum(torch.xpu.memory_reserved(device) for device in range(count))
+        p("Allocated XPU memory", allocated)
+        p("Reserved XPU memory", reserved)
     elif torch.backends.mps.is_available():
         p("Allocated MPS memory", torch.mps.current_allocated_memory())
         p("Driver (reserved) MPS memory", torch.mps.driver_allocated_memory())
@@ -300,7 +306,11 @@ def get_readme_intro(
     base_refusals: int,
     bad_prompts: list[Prompt],
 ) -> str:
-    model_link = f"[{settings.model}](https://huggingface.co/{settings.model})"
+    if Path(settings.model).exists():
+        # Hide the path, which may contain private information.
+        model_link = "a model"
+    else:
+        model_link = f"[{settings.model}](https://huggingface.co/{settings.model})"
 
     return f"""# This is a decensored version of {
         model_link

uv.lock

@@ -18,7 +18,7 @@ wheels = [
 
 [[package]]
 name = "accelerate"
-version = "1.12.0"
+version = "1.13.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
@@ -30,9 +30,9 @@ dependencies = [
     { name = "safetensors" },
     { name = "torch" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/4a/8e/ac2a9566747a93f8be36ee08532eb0160558b07630a081a6056a9f89bf1d/accelerate-1.12.0.tar.gz", hash = "sha256:70988c352feb481887077d2ab845125024b2a137a5090d6d7a32b57d03a45df6", size = 398399, upload-time = "2025-11-21T11:27:46.973Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/ca/14/787e5498cd062640f0f3d92ef4ae4063174f76f9afd29d13fc52a319daae/accelerate-1.13.0.tar.gz", hash = "sha256:d631b4e0f5b3de4aff2d7e9e6857d164810dfc3237d54d017f075122d057b236", size = 402835, upload-time = "2026-03-04T19:34:12.359Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/9f/d2/c581486aa6c4fbd7394c23c47b83fa1a919d34194e16944241daf9e762dd/accelerate-1.12.0-py3-none-any.whl", hash = "sha256:3e2091cd341423207e2f084a6654b1efcd250dc326f2a37d6dde446e07cabb11", size = 380935, upload-time = "2025-11-21T11:27:44.522Z" },
+    { url = "https://files.pythonhosted.org/packages/7e/46/02ac5e262d4af18054b3e922b2baedbb2a03289ee792162de60a865defc5/accelerate-1.13.0-py3-none-any.whl", hash = "sha256:cf1a3efb96c18f7b152eb0fa7490f3710b19c3f395699358f08decca2b8b62e0", size = 383744, upload-time = "2026-03-04T19:34:10.313Z" },
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 ]
 
+[[package]]
+name = "annotated-doc"
+version = "0.0.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4", size = 7288, upload-time = "2025-11-10T22:07:42.062Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303, upload-time = "2025-11-10T22:07:40.673Z" },
+]
+
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
@@ -244,7 +253,7 @@ wheels = [
 
 [[package]]
 name = "bitsandbytes"
-version = "0.48.2"
+version = "0.49.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
@@ -253,9 +262,10 @@ dependencies = [
     { name = "torch" },
 ]
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+    { url = "https://files.pythonhosted.org/packages/b6/d4/501655842ad6771fb077f576d78cbedb5445d15b1c3c91343ed58ca46f0e/bitsandbytes-0.49.2-py3-none-win_amd64.whl", hash = "sha256:2e0ddd09cd778155388023cbe81f00afbb7c000c214caef3ce83386e7144df7d", size = 55372289, upload-time = "2026-02-16T21:26:16.267Z" },
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 [[package]]
@@ -578,7 +588,7 @@ wheels = [
 
 [[package]]
 name = "datasets"
-version = "4.4.1"
+version = "4.7.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "dill" },
@@ -597,9 +607,9 @@ dependencies = [
     { name = "tqdm" },
     { name = "xxhash" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/93/bf/0dae295d6d1ba0b1a200a9dd216838464b5bbd05da01407cb1330b377445/datasets-4.4.1.tar.gz", hash = "sha256:80322699aa8c0bbbdb7caa87906da689c3c2e29523cff698775c67f28fdab1fc", size = 585341, upload-time = "2025-11-05T16:00:38.162Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/1c/9c/ba18de0b70858533e422ed6cfe0e46789473cef7fc7fc3653e23fa494730/datasets-4.7.0.tar.gz", hash = "sha256:4984cdfc65d04464da7f95205a55cb50515fd94ae3176caacb50a1b7273792e2", size = 602008, upload-time = "2026-03-09T19:01:49.298Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/1e/03/c6d9c3119cf712f638fe763e887ecaac6acbb62bf1e2acc3cbde0df340fd/datasets-4.7.0-py3-none-any.whl", hash = "sha256:d5fe3025ec6acc3b5649f10d5576dff5e054134927604e6913c1467a04adc3c2", size = 527530, upload-time = "2026-03-09T19:01:47.443Z" },
 ]
 
 [[package]]
@@ -931,14 +941,19 @@ dependencies = [
     { name = "datasets" },
     { name = "hf-transfer" },
     { name = "huggingface-hub" },
+    { name = "immutabledict" },
     { name = "kernels" },
+    { name = "langdetect" },
     { name = "lm-eval", extra = ["hf"] },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "optuna" },
     { name = "peft" },
     { name = "psutil" },
     { name = "pydantic-settings" },
     { name = "questionary" },
     { name = "rich" },
+    { name = "tqdm" },
     { name = "transformers" },
 ]
 
@@ -947,8 +962,6 @@ research = [
     { name = "geom-median" },
     { name = "imageio" },
     { name = "matplotlib" },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "pacmap" },
     { name = "scikit-learn", version = "1.7.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
     { name = "scikit-learn", version = "1.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
@@ -962,26 +975,29 @@ dev = [
 
 [package.metadata]
 requires-dist = [
-    { name = "accelerate", specifier = "~=1.10" },
-    { name = "bitsandbytes", specifier = "~=0.45" },
-    { name = "datasets", specifier = "~=4.0" },
+    { name = "accelerate", specifier = "~=1.13" },
+    { name = "bitsandbytes", specifier = "~=0.49" },
+    { name = "datasets", specifier = "~=4.7" },
     { name = "geom-median", marker = "extra == 'research'", specifier = "~=0.1" },
     { name = "hf-transfer", specifier = "~=0.1" },
-    { name = "huggingface-hub", specifier = "~=0.34" },
+    { name = "huggingface-hub", specifier = "~=1.7" },
     { name = "imageio", marker = "extra == 'research'", specifier = "~=2.37" },
-    { name = "kernels", specifier = "~=0.11" },
-    { name = "lm-eval", extras = ["hf"], specifier = ">=0.4.11" },
+    { name = "immutabledict", specifier = "~=4.3" },
+    { name = "kernels", specifier = "~=0.12" },
+    { name = "langdetect", specifier = "~=1.0" },
+    { name = "lm-eval", extras = ["hf"], specifier = "~=0.4" },
     { name = "matplotlib", marker = "extra == 'research'", specifier = "~=3.10" },
-    { name = "numpy", marker = "extra == 'research'", specifier = "~=2.2" },
-    { name = "optuna", specifier = "~=4.5" },
+    { name = "numpy", specifier = "~=2.2" },
+    { name = "optuna", specifier = "~=4.7" },
     { name = "pacmap", marker = "extra == 'research'", specifier = "~=0.8" },
-    { name = "peft", specifier = "~=0.14" },
-    { name = "psutil", specifier = "~=7.1" },
-    { name = "pydantic-settings", specifier = "~=2.10" },
+    { name = "peft", specifier = "~=0.18" },
+    { name = "psutil", specifier = "~=7.2" },
+    { name = "pydantic-settings", specifier = "~=2.13" },
     { name = "questionary", specifier = "~=2.1" },
-    { name = "rich", specifier = "~=14.1" },
+    { name = "rich", specifier = "~=14.3" },
     { name = "scikit-learn", marker = "extra == 'research'", specifier = "~=1.7" },
-    { name = "transformers", specifier = "~=4.57" },
+    { name = "tqdm", specifier = "~=4.67" },
+    { name = "transformers", specifier = "~=5.3" },
 ]
 provides-extras = ["research"]
 
@@ -1025,31 +1041,34 @@ wheels = [
 
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