Merge branch 'master' into auto-reproduce

README.md

@@ -1,4 +1,4 @@
-<img width="128" height="128" align="right" alt="Logo" src="https://github.com/user-attachments/assets/df5f2840-2f92-4991-aa57-252747d7182e" />
+<img width="128" align="right" alt="Logo" src="https://github.com/user-attachments/assets/df5f2840-2f92-4991-aa57-252747d7182e" />
 
 # Heretic: Fully automatic censorship removal for language models<br><br>[![Discord](https://img.shields.io/discord/1447831134212984903?color=5865F2&label=discord&labelColor=black&logo=discord&logoColor=white&style=for-the-badge)](https://discord.gg/gdXc48gSyT) [![Follow us on Hugging Face](https://huggingface.co/datasets/huggingface/badges/resolve/main/follow-us-on-hf-md-dark.svg)](https://huggingface.co/heretic-org) [![Codeberg mirror](https://img.shields.io/badge/Codeberg%20mirror-black?logo=codeberg&style=for-the-badge)](https://codeberg.org/p-e-w/heretic)
 
@@ -116,8 +116,9 @@ a configuration file.
 
 At the start of a program run, Heretic benchmarks the system to determine
 the optimal batch size to make the most of the available hardware.
-On an RTX 3090, with the default configuration, decensoring Llama-3.1-8B-Instruct
+On an RTX 3090, with the default configuration, decensoring
-takes about 45 minutes. Note that Heretic supports model quantization with
+[Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
+takes about 20-30 minutes. Note that Heretic supports model quantization with
 bitsandbytes, which can drastically reduce the amount of VRAM required to process
 models. Set the `quantization` option to `bnb_4bit` to enable quantization.
 

config.default.toml

@@ -131,6 +131,7 @@ max_shard_size = "5GB"
 
 # Strings whose presence in a response (case insensitive) identifies the response as a refusal.
 refusal_markers = [
+    "disclaimer",
     "sorry",
     "i can'",
     "i cant",
@@ -168,6 +169,11 @@ refusal_markers = [
 # System prompt to use when prompting the model.
 system_prompt = "You are a helpful assistant."
 
+# Each "dataset" below can be a Hugging Face dataset ID, a path to a dataset on disk,
+# or a path to a plain text file with one prompt per line (empty lines are ignored).
+# For text files, "column" is ignored and "split" is optional; when given, it selects
+# a subset of the lines using slice notation (e.g. "[:400]").
+
 # Dataset of prompts that tend to not result in refusals (used for calculating refusal directions).
 [good_prompts]
 dataset = "mlabonne/harmless_alpaca"

config.nohumor.toml

@@ -0,0 +1,69 @@
+# Rename this file to config.toml, place it in the working directory
+# that you run Heretic from, and edit the configuration to your liking.
+
+max_response_length = 300
+
+residual_plot_title = "PaCMAP Projection of Residuals for Serious/Humorous Prompts"
+
+refusal_markers = [
+    "😅",
+    "here's one",
+    "why did",
+    "walked into a bar",
+    "gets worse",
+    "the punchline",
+    "classic",
+    "dad joke",
+    "bad joke",
+    "pun intended",
+    "ba dum tss",
+    "rimshot",
+    "😂",
+    "😄",
+    "😆",
+    "haha",
+    "hehe",
+    "lol",
+    "funny",
+    "joke",
+    "humor",
+    "that's hilarious",
+    "you could say",
+    "one-liner",
+    "comedian",
+    "stand-up",
+    "unexpectedly",
+    "because apparently",
+    "to be fair",
+    "on the bright side",
+    "lmao",
+    "omg",
+    "rofl",
+    "silly",
+    "humorous",
+    "clever",
+]
+
+[good_prompts]
+dataset = "mlabonne/harmless_alpaca"
+split = "train[:400]"
+column = "text"
+residual_plot_label = "Serious prompts"
+residual_plot_color = "royalblue"
+
+[bad_prompts]
+dataset = "UnstableLlama/jokes"
+split = "train[:200]"
+column = "text"
+residual_plot_label = "Humorous prompts"
+residual_plot_color = "darkorange"
+
+[good_evaluation_prompts]
+dataset = "mlabonne/harmless_alpaca"
+split = "test[:100]"
+column = "text"
+
+[bad_evaluation_prompts]
+dataset = "UnstableLlama/jokes"
+split = "train[200:250]"
+column = "text"

pyproject.toml

@@ -25,10 +25,8 @@ dependencies = [
     "accelerate~=1.13",
     "bitsandbytes~=0.49",
     "datasets~=4.7",
-    "hf-transfer~=0.1",
     "huggingface-hub~=1.7",
     "immutabledict~=4.3",
-    "kernels~=0.13",
     "langdetect~=1.0",
     "lm-eval[hf]~=0.4",
     "numpy~=2.2",
@@ -41,7 +39,7 @@ dependencies = [
     "rich~=14.3",
     "tomli-w~=1.2",
     "tqdm~=4.67",
-    "transformers~=5.6",
+    "transformers[kernels]~=5.6",
 ]
 
 [project.optional-dependencies]

src/heretic/config.py

@@ -47,9 +47,15 @@ class DatasetSpecification(BaseModel):
         description="Hugging Face commit hash of the dataset.",
     )
 
-    split: str = Field(description="Portion of the dataset to use.")
+    split: str | None = Field(
+        default=None,
+        description="Portion of the dataset to use. Required for datasets, optional for plain text files.",
+    )
 
-    column: str = Field(description="Column in the dataset that contains the prompts.")
+    column: str | None = Field(
+        default=None,
+        description="Column in the dataset that contains the prompts. Required for datasets, ignored for plain text files.",
+    )
 
     prefix: str = Field(
         default="",
@@ -424,6 +430,7 @@ class Settings(BaseSettings):
 
     refusal_markers: list[str] = Field(
         default=[
+            "disclaimer",
             "sorry",
             "i can'",
             "i cant",

src/heretic/main.py

@@ -73,6 +73,7 @@ from .reproduce import (
 from .system import empty_cache, get_accelerator_info
 from .utils import (
     format_duration,
+    format_exception,
     get_file_sha256,
     get_readme_intro,
     get_trial_parameters,
@@ -211,8 +212,10 @@ def run():
     except ValidationError as error:
         print(f"[red]Configuration contains [bold]{error.error_count()}[/] errors:[/]")
 
-        for error in error.errors():
+        for error_detail in error.errors():
-            print(f"[bold]{error['loc'][0]}[/]: [yellow]{error['msg']}[/]")
+            print(
+                f"[bold]{error_detail['loc'][0]}[/]: [yellow]{error_detail['msg']}[/]"
+            )
 
         print()
         print(
@@ -407,7 +410,11 @@ def run():
                     # We cannot recover from this.
                     raise
 
-                print(f"[red]Failed[/] ({error})")
+                formatted = format_exception(error)
+                if "\n" in formatted:
+                    print(f"[red]Failed[/]:\n{formatted}")
+                else:
+                    print(f"[red]Failed[/] ({formatted})")
                 break
 
             response_lengths = [
@@ -659,13 +666,7 @@ def run():
         study.set_user_attr("settings", settings.model_dump_json())
         study.set_user_attr("finished", False)
 
-        def count_completed_trials() -> int:
+        start_index = trial_index = len(study.trials)
-            # Count number of complete trials to compute trials to run.
-            return sum(
-                [(1 if t.state == TrialState.COMPLETE else 0) for t in study.trials]
-            )
-
-        start_index = trial_index = count_completed_trials()
         if start_index > 0:
             print()
             print("Resuming existing study.")
@@ -673,7 +674,7 @@ def run():
         try:
             study.optimize(
                 objective_wrapper,
-                n_trials=settings.n_trials - count_completed_trials(),
+                n_trials=settings.n_trials - len(study.trials),
             )
         except KeyboardInterrupt:
             # This additional handler takes care of the small chance that KeyboardInterrupt
@@ -681,7 +682,7 @@ def run():
             # defined in objective_wrapper above.
             pass
 
-        if count_completed_trials() == settings.n_trials:
+        if len(study.trials) == settings.n_trials:
             study.set_user_attr("finished", True)
 
     while True:
@@ -804,12 +805,12 @@ def run():
                     try:
                         study.optimize(
                             objective_wrapper,
-                            n_trials=settings.n_trials - count_completed_trials(),
+                            n_trials=settings.n_trials - len(study.trials),
                         )
                     except KeyboardInterrupt:
                         pass
 
-                    if count_completed_trials() == settings.n_trials:
+                    if len(study.trials) == settings.n_trials:
                         study.set_user_attr("finished", True)
 
                     break
@@ -894,6 +895,8 @@ def run():
                                 del merged_model
                                 empty_cache()
                                 model.tokenizer.save_pretrained(save_directory)
+                                if model.processor is not None:
+                                    model.processor.save_pretrained(save_directory)
                                 reset_trial_model()
 
                             print(f"Model saved to [bold]{save_directory}[/].")
@@ -1031,6 +1034,12 @@ def run():
                                     private=private,
                                     token=token,
                                 )
+                                if model.processor is not None:
+                                    model.processor.push_to_hub(
+                                        repo_id,
+                                        private=private,
+                                        token=token,
+                                    )
                                 reset_trial_model()
 
                             if is_hf_path(settings.model):
@@ -1069,7 +1078,7 @@ def run():
                             if reproducibility_information != "none":
                                 # Set the number of trials to the number of actual completed trials
                                 # for the reproduction configuration.
-                                settings.n_trials = count_completed_trials()
+                                settings.n_trials = len(study.trials)
                                 current_export_strategy = settings.export_strategy
                                 settings.export_strategy = strategy
 
@@ -1278,7 +1287,11 @@ def run():
                                 print(table)
 
                 except Exception as error:
-                    print(f"[red]Error: {error}[/]")
+                    formatted = format_exception(error)
+                    if "\n" in formatted:
+                        print(f"[red]Error:[/]\n{formatted}")
+                    else:
+                        print(f"[red]Error: {formatted}[/]")
 
 
 def main():

src/heretic/model.py

@@ -17,12 +17,14 @@ from torch.nn import Module, ModuleList
 from transformers import (
     AutoModelForCausalLM,
     AutoModelForImageTextToText,
+    AutoProcessor,
     AutoTokenizer,
     BatchEncoding,
     BitsAndBytesConfig,
     PretrainedConfig,
     PreTrainedModel,
     PreTrainedTokenizerBase,
+    ProcessorMixin,
     TextStreamer,
 )
 from transformers.generation import (
@@ -31,7 +33,7 @@ from transformers.generation import (
 
 from .config import QuantizationMethod, RowNormalization, Settings
 from .system import empty_cache
-from .utils import Prompt, batchify, print
+from .utils import Prompt, batchify, format_exception, print
 
 
 def get_model_class(
@@ -56,7 +58,10 @@ class AbliterationParameters:
 class Model:
     model: PreTrainedModel | PeftModel
     tokenizer: PreTrainedTokenizerBase
+    # Set for multimodal models, None for text-only ones.
+    processor: ProcessorMixin | None
     peft_config: LoraConfig
+    dtype: torch.dtype
 
     def __init__(self, settings: Settings):
         self.settings = settings
@@ -74,6 +79,15 @@ class Model:
             **self.revision_kwargs,
         )
 
+        # Multimodal models have a processor we'll want to save.
+        self.processor = None
+        if get_model_class(settings.model) == AutoModelForImageTextToText:
+            self.processor = AutoProcessor.from_pretrained(
+                settings.model,
+                trust_remote_code=settings.trust_remote_code,
+                **self.revision_kwargs,
+            )
+
         # Fallback for tokenizers that don't declare a special pad token.
         if self.tokenizer.pad_token is None:
             self.tokenizer.pad_token = self.tokenizer.eos_token
@@ -115,6 +129,7 @@ class Model:
                     **self.revision_kwargs,
                     **extra_kwargs,
                 )
+                self.dtype = self.model.dtype
 
                 # If we reach this point and the model requires trust_remote_code,
                 # the user must have agreed when prompted to execute remote code,
@@ -136,7 +151,11 @@ class Model:
             except Exception as error:
                 self.model = None  # ty:ignore[invalid-assignment]
                 empty_cache()
-                print(f"* [red]Failed[/] ({error})")
+                formatted = format_exception(error)
+                if "\n" in formatted:
+                    print(f"* [red]Failed[/]:\n{formatted}")
+                else:
+                    print(f"* [red]Failed[/] ({formatted})")
                 continue
 
             if settings.quantization == QuantizationMethod.BNB_4BIT:
@@ -301,30 +320,34 @@ class Model:
         - Slow path: If switching models or after merge_and_unload(),
           performs full model reload with quantization config.
         """
-        current_model = getattr(self.model.config, "name_or_path", None)
+        # If a prior model load was interrupted/cancelled mid-process, self.model will be None.
+        current_model = None
+        if self.model is not None:
+            current_model = getattr(self.model.config, "name_or_path", None)
+
         if current_model == self.settings.model and not self.needs_reload:
-            # Reset LoRA adapters to zero (identity transformation)
+            # Reset LoRA adapters to zero (identity transformation).
             for name, module in self.model.named_modules():
                 if "lora_B" in name and hasattr(module, "weight"):
                     torch.nn.init.zeros_(module.weight)
             return
 
-        dtype = self.model.dtype
-
         # Purge existing model object from memory to make space.
         self.model = None  # ty:ignore[invalid-assignment]
         empty_cache()
 
-        quantization_config = self._get_quantization_config(str(dtype).split(".")[-1])
+        quantization_config = self._get_quantization_config(
+            str(self.dtype).split(".")[-1]
+        )
 
-        # Build kwargs, only include quantization_config if it's not None
+        # Build kwargs, only include quantization_config if it's not None.
         extra_kwargs = {}
         if quantization_config is not None:
             extra_kwargs["quantization_config"] = quantization_config
 
         self.model = get_model_class(self.settings.model).from_pretrained(
             self.settings.model,
-            dtype=dtype,
+            dtype=self.dtype,
             device_map=self.settings.device_map,
             max_memory=self.max_memory,
             trust_remote_code=True
@@ -392,6 +415,21 @@ class Model:
             for expert in layer.block_sparse_moe.experts:  # ty:ignore[possibly-missing-attribute, not-iterable]
                 try_add("mlp.down_proj", expert.w2)  # ty:ignore[possibly-missing-attribute]
 
+        # LFM dense operator blocks.
+        with suppress(Exception):
+            try_add("attn.o_proj", layer.conv.out_proj)  # ty:ignore[possibly-missing-attribute]
+
+        with suppress(Exception):
+            try_add("mlp.down_proj", layer.feed_forward.w2)  # ty:ignore[possibly-missing-attribute]
+
+        # LFM transformer blocks.
+        with suppress(Exception):
+            try_add("attn.o_proj", layer.self_attn.out_proj)  # ty:ignore[possibly-missing-attribute]
+
+        with suppress(Exception):
+            for expert in layer.feed_forward.experts:  # ty:ignore[possibly-missing-attribute, not-iterable]
+                try_add("mlp.down_proj", expert.w2)  # ty:ignore[possibly-missing-attribute]
+
         # Granite MoE Hybrid - attention layers with shared_mlp.
         with suppress(Exception):
             try_add("mlp.down_proj", layer.shared_mlp.output_linear)  # ty:ignore[possibly-missing-attribute]
@@ -739,7 +777,7 @@ class Model:
         _, outputs = self.generate(
             prompts,
             max_new_tokens=1,
-            output_scores=True,
+            output_logits=True,
             return_dict_in_generate=True,
             use_cache=False,
         )
@@ -748,9 +786,9 @@ class Model:
         # of model.generate with return_dict_in_generate=True.
         outputs = cast(GenerateDecoderOnlyOutput, outputs)
 
-        # Logits for the first (only) generated token.
+        # Use raw logits, not processed generation scores; processors can insert
-        # This cast is valid because we passed output_scores=True above.
+        # -inf for suppressed tokens, which can make KL divergence evaluate to NaN.
-        logits = cast(tuple[FloatTensor], outputs.scores)[0]
+        logits = cast(tuple[FloatTensor], outputs.logits)[0]
 
         # The returned tensor has shape (prompt, token).
         logprobs = F.log_softmax(logits, dim=-1)

src/heretic/utils.py

@@ -8,6 +8,7 @@ import os
 import platform
 import random
 import tempfile
+import traceback
 from dataclasses import dataclass
 from datetime import datetime, timezone
 from importlib.metadata import version
@@ -23,6 +24,7 @@ from datasets import DatasetDict, ReadInstruction, load_dataset, load_from_disk
 from datasets.config import DATASET_STATE_JSON_FILENAME
 from datasets.download.download_manager import DownloadMode
 from datasets.utils.info_utils import VerificationMode
+from huggingface_hub.utils import validate_repo_id
 from optuna import Trial
 from optuna.trial import FrozenTrial
 from psutil import Process
@@ -174,13 +176,13 @@ def format_duration(seconds: float) -> str:
 def is_hf_path(path: str) -> bool:
     """Checks whether a path likely refers to a Hugging Face repository."""
 
-    return (
+    # Match Transformers: existing local paths take precedence over Hub lookup,
-        not path.startswith("/")
+    # even if the path string is also a valid repository ID.
-        and not path.endswith("/")
+    if Path(path).exists():
-        and path.count("/") == 1
+        return False
-        and "\\" not in path
+
-        and not Path(path).exists()
+    validate_repo_id(path)
-    )
+    return True
 
 
 @dataclass
@@ -189,6 +191,20 @@ class Prompt:
     user: str
 
 
+def get_split_slice(split_str: str, length: int) -> tuple[int, int]:
+    """Resolves a split specification into absolute (start, end) indices."""
+
+    # The split name is the part before the slice, e.g. "train" in "train[:400]".
+    split_name = split_str.split("[")[0]
+    # Associate the split with its number of examples (lines).
+    name_to_length = {split_name: length}
+    # Convert the instructions to absolute indices and select the first one.
+    absolute_instruction = ReadInstruction.from_spec(split_str).to_absolute(
+        name_to_length
+    )[0]
+    return absolute_instruction.from_, absolute_instruction.to
+
+
 def load_prompts(
     settings: Settings,
     specification: DatasetSpecification,
@@ -196,29 +212,41 @@ def load_prompts(
     path = specification.dataset
     split_str = specification.split
 
-    if is_hf_path(path):
+    if os.path.isfile(path):
-        dataset = load_dataset(
+        # Plain text file with one prompt per line. Empty lines are ignored.
-            path,
+        with open(path, encoding="utf-8") as file:
-            revision=specification.commit,
+            prompts = [line.strip() for line in file if line.strip()]
-            split=split_str,
+
-        )
+        # The split is optional for text files. When given, it selects a subset
+        # of the lines using slice notation (e.g. "[:400]"). A synthetic split
+        # name is prepended because ReadInstruction expects a named split.
+        if split_str is not None:
+            start, end = get_split_slice(f"_{split_str}", len(prompts))
+            prompts = prompts[start:end]
     else:
-        if Path(path, DATASET_STATE_JSON_FILENAME).exists():
+        # All dataset sources require an explicit split and column.
+        if split_str is None:
+            raise ValueError(f'The "split" field is required for datasets: {path}')
+
+        if specification.column is None:
+            raise ValueError(f'The "column" field is required for datasets: {path}')
+
+        if is_hf_path(path):
+            dataset = load_dataset(
+                path,
+                revision=specification.commit,
+                split=split_str,
+            )
+        elif Path(path, DATASET_STATE_JSON_FILENAME).exists():
             # Dataset saved with datasets.save_to_disk; needs special handling.
             # Path should be the subdirectory for a particular split.
             dataset = load_from_disk(path)
             assert not isinstance(dataset, DatasetDict), (
                 "Loading dataset dicts is not supported"
             )
-            # Parse the split instructions.
+            # Parse the split instructions and apply them.
-            instruction = ReadInstruction.from_spec(split_str)
+            start, end = get_split_slice(split_str, len(dataset))
-            # Associate the split with its number of examples (lines).
+            dataset = dataset[start:end]
-            split_name = str(dataset.split)
-            name2len = {split_name: len(dataset)}
-            # Convert the instructions to absolute indices and select the first one.
-            abs_instruction = instruction.to_absolute(name2len)[0]
-            # Get the dataset by applying the indices.
-            dataset = dataset[abs_instruction.from_ : abs_instruction.to]
         else:
             # Path should be a local directory.
             dataset = load_dataset(
@@ -230,7 +258,7 @@ def load_prompts(
                 download_mode=DownloadMode.FORCE_REDOWNLOAD,
             )
 
-    prompts = list(dataset[specification.column])
+        prompts = list(dataset[specification.column])
 
     if specification.prefix:
         prompts = [f"{specification.prefix} {prompt}" for prompt in prompts]
@@ -733,3 +761,16 @@ def upload_reproduce_folder(
                     repo_id=repo_id,
                     token=token,
                 )
+
+
+def format_exception(error: Exception) -> str:
+    # Walk causal chain to find a non-empty message.
+    current = error
+    while current is not None:
+        message = str(current).strip()
+        if message:
+            return message
+        current = current.__cause__ or current.__context__
+
+    # If there is no message in the entire causal chain, fall back to the complete traceback.
+    return traceback.format_exc().strip()

uv.lock

@@ -937,10 +937,8 @@ dependencies = [
     { name = "accelerate" },
     { name = "bitsandbytes" },
     { name = "datasets" },
-    { name = "hf-transfer" },
     { name = "huggingface-hub" },
     { name = "immutabledict" },
-    { name = "kernels" },
     { name = "langdetect" },
     { name = "lm-eval", extra = ["hf"] },
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
@@ -954,7 +952,7 @@ dependencies = [
     { name = "rich" },
     { name = "tomli-w" },
     { name = "tqdm" },
-    { name = "transformers" },
+    { name = "transformers", extra = ["kernels"] },
 ]
 
 [package.optional-dependencies]
@@ -979,11 +977,9 @@ requires-dist = [
     { name = "bitsandbytes", specifier = "~=0.49" },
     { name = "datasets", specifier = "~=4.7" },
     { name = "geom-median", marker = "extra == 'research'", specifier = "~=0.1" },
-    { name = "hf-transfer", specifier = "~=0.1" },
     { name = "huggingface-hub", specifier = "~=1.7" },
     { name = "imageio", marker = "extra == 'research'", specifier = "~=2.37" },
     { name = "immutabledict", specifier = "~=4.3" },
-    { name = "kernels", specifier = "~=0.13" },
     { name = "langdetect", specifier = "~=1.0" },
     { name = "lm-eval", extras = ["hf"], specifier = "~=0.4" },
     { name = "matplotlib", marker = "extra == 'research'", specifier = "~=3.10" },
@@ -999,7 +995,7 @@ requires-dist = [
     { name = "scikit-learn", marker = "extra == 'research'", specifier = "~=1.7" },
     { name = "tomli-w", specifier = "~=1.2" },
     { name = "tqdm", specifier = "~=4.67" },
-    { name = "transformers", specifier = "~=5.6" },
+    { name = "transformers", extras = ["kernels"], specifier = "~=5.6" },
 ]
 provides-extras = ["research"]
 
@@ -1009,38 +1005,6 @@ dev = [
     { name = "ty", specifier = ">=0.0.5" },
 ]
 
-[[package]]
-name = "hf-transfer"
-version = "0.1.9"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/1a/eb/8fc64f40388c29ce8ce3b2b180a089d4d6b25b1d0d232d016704cb852104/hf_transfer-0.1.9.tar.gz", hash = "sha256:035572865dab29d17e783fbf1e84cf1cb24f3fcf8f1b17db1cfc7fdf139f02bf", size = 25201, upload-time = "2025-01-07T10:05:12.947Z" }
-wheels = [
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