fix: improve the reproducibility system (#303)

* fix: various cleanups and improvements for the reproducibility system

* fix: save only essential settings

* fix: improve model commit handling

* feat: make including system information optional

* fix: improve formatting of reproducibility README

* fix: fix remaining issues

config.default.toml

@@ -150,7 +150,6 @@ split = "train[:400]"
 column = "text"
 residual_plot_label = '"Harmless" prompts'
 residual_plot_color = "royalblue"
-commit = ""
 
 # Dataset of prompts that tend to result in refusals (used for calculating refusal directions).
 [bad_prompts]
@@ -159,18 +158,15 @@ split = "train[:400]"
 column = "text"
 residual_plot_label = '"Harmful" prompts'
 residual_plot_color = "darkorange"
-commit = ""
 
 # Dataset of prompts that tend to not result in refusals (used for evaluating model performance).
 [good_evaluation_prompts]
 dataset = "mlabonne/harmless_alpaca"
 split = "test[:100]"
 column = "text"
-commit = ""
 
 # Dataset of prompts that tend to result in refusals (used for evaluating model performance).
 [bad_evaluation_prompts]
 dataset = "mlabonne/harmful_behaviors"
 split = "test[:100]"
 column = "text"
-commit = ""

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.")
@@ -53,15 +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.",
-    )
-    commit: str | None = Field(
-        default=None,
-        description="Hugging Face commit hash of the dataset.",
+        exclude=True,
     )
 
 
@@ -80,12 +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(
@@ -129,6 +144,8 @@ class Settings(BaseSettings):
     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(
@@ -139,6 +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(
@@ -183,36 +203,45 @@ class Settings(BaseSettings):
             "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(
@@ -291,6 +320,7 @@ class Settings(BaseSettings):
     study_checkpoint_dir: str = Field(
         default="checkpoints",
         description="Directory to save and load study progress to/from.",
+        exclude=True,
     )
 
     benchmarks: list[BenchmarkSpecification] = Field(
@@ -352,6 +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(

src/heretic/main.py

@@ -66,10 +66,10 @@ from .utils import (
     format_duration,
     get_readme_intro,
     get_trial_parameters,
+    is_hf_path,
     load_prompts,
     print,
     print_memory_usage,
-    prompt_confirm,
     prompt_password,
     prompt_path,
     prompt_select,
@@ -79,7 +79,7 @@ from .utils import (
 )
 
 
-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.
@@ -108,7 +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)
@@ -571,7 +572,8 @@ def run():
 
         trial.set_user_attr("kl_divergence", kl_divergence)
         trial.set_user_attr("refusals", refusals)
-        trial.set_user_attr("total_refusal_prompts", len(evaluator.bad_prompts))
+        trial.set_user_attr("base_refusals", evaluator.base_refusals)
+        trial.set_user_attr("n_bad_prompts", len(evaluator.bad_prompts))
 
         return score
 
@@ -772,17 +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:
                                 print("Saving merged model...")
                                 merged_model = model.get_merged_model()
-                                merged_model.save_pretrained(save_directory)
+                                merged_model.save_pretrained(
+                                    save_directory,
+                                    max_shard_size=settings.max_shard_size,
+                                )
                                 del merged_model
                                 empty_cache()
                                 model.tokenizer.save_pretrained(save_directory)
@@ -823,7 +831,7 @@ def run():
                                 continue
                             private = visibility == "Private"
 
-                            strategy = obtain_merge_strategy(settings)
+                            strategy = obtain_merge_strategy(settings, model)
                             if strategy is None:
                                 continue
 
@@ -835,27 +843,48 @@ def run():
                                 settings.good_evaluation_prompts.dataset,
                                 settings.bad_evaluation_prompts.dataset,
                             ]
-                            can_reproduce = not Path(settings.model).exists() and all(
-                                not Path(d).exists() for d in datasets
+                            is_reproducible = is_hf_path(settings.model) and all(
+                                is_hf_path(dataset) for dataset in datasets
                             )
 
-                            if can_reproduce:
-                                # Pin the number of trials to the number of actual completed trials
-                                # for the reproduction configuration.
-                                settings.n_trials = count_completed_trials()
-
-                                include_reproduce = prompt_confirm(
-                                    """Include 'reproduce' folder?
-This saves your exact configuration and system information, along with the study checkpoint, to help others verify your results."""
+                            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:
-                                include_reproduce = False
+                                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:
@@ -864,6 +893,7 @@ This saves your exact configuration and system information, along with the study
                                 merged_model.push_to_hub(
                                     repo_id,
                                     private=private,
+                                    max_shard_size=settings.max_shard_size,
                                     token=token,
                                 )
                                 del merged_model
@@ -874,22 +904,18 @@ This saves your exact configuration and system information, along with the study
                                     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()
@@ -899,30 +925,35 @@ This saves your exact configuration and system information, along with the study
                                 card.data.tags.append("uncensored")
                                 card.data.tags.append("decensored")
                                 card.data.tags.append("abliterated")
+                                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 include_reproduce:
+                            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 and reproducibility files uploaded to [bold]{repo_id}[/]."
-                                )
-                            else:
-                                print(f"Model uploaded to [bold]{repo_id}[/].")
+
+                            print(f"Model uploaded to [bold]{repo_id}[/].")
 
                         case "Chat with the model":
                             print()

src/heretic/model.py

@@ -62,12 +62,17 @@ class Model:
         self.settings = settings
         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.
@@ -108,6 +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,
                 )
 
@@ -257,6 +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
@@ -318,6 +325,7 @@ 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,
         )
 

src/heretic/system.py

@@ -25,6 +25,7 @@ from accelerate.utils import (
 
 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.
@@ -48,6 +49,7 @@ def empty_cache():
 
 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"],
@@ -61,6 +63,7 @@ def get_nvidia_driver_version() -> str | 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(
@@ -101,6 +104,7 @@ def get_amdgpu_driver_version() -> str | None:
 
 def get_xpu_driver_version() -> str | None:
     """Gets the Intel XPU driver version."""
+
     try:
         output = subprocess.check_output(
             ["xpu-smi", "discovery"],
@@ -117,6 +121,7 @@ def get_xpu_driver_version() -> str | 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"],
@@ -133,6 +138,7 @@ def get_npu_driver_version() -> str | 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"],
@@ -156,6 +162,7 @@ class HereticVersionInfo:
 
 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.
@@ -171,6 +178,7 @@ def get_heretic_version_info() -> HereticVersionInfo:
     if not direct_url_content:
         # Standard PyPI installation.
         origin_metadata["type"] = "pypi"
+
         return HereticVersionInfo(
             version=base_version,
             origin="PyPI",
@@ -178,51 +186,48 @@ def get_heretic_version_info() -> HereticVersionInfo:
             metadata=origin_metadata,
         )
 
-    try:
-        data = json.loads(direct_url_content)
+    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")
+    # 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,
-                }
+        if requested_revision:
+            origin_str = (
+                f"Git ({repo_url}@{requested_revision} - commit: {commit_hash})"
             )
+        else:
+            origin_str = f"Git ({repo_url} @ {commit_hash})"
 
-            return HereticVersionInfo(
-                version=base_version,
-                origin=origin_str,
-                is_standard_pypi=False,
-                metadata=origin_metadata,
-            )
+        origin_metadata.update(
+            {
+                "type": "git",
+                "url": repo_url,
+                "commit_hash": commit_hash,
+                "requested_revision": requested_revision,
+            }
+        )
 
-        # 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=origin_str,
+            is_standard_pypi=False,
+            metadata=origin_metadata,
+        )
 
-    except json.JSONDecodeError:
-        pass
+    # 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,
@@ -234,6 +239,7 @@ def get_heretic_version_info() -> HereticVersionInfo:
 
 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
@@ -320,6 +326,7 @@ def get_accelerator_info_dict() -> dict[str, Any]:
 
 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:
@@ -350,6 +357,7 @@ def get_accelerator_info(include_warnings: bool = True) -> str:
 
 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 {
@@ -363,6 +371,7 @@ def get_cpu_info_dict() -> dict[str, str | int | None]:
 
 def get_cpu_info() -> str:
     """Gets the CPU brand name."""
+
     info = get_cpu_info_dict()
     parts = []
     parts.append(
@@ -397,12 +406,14 @@ def get_python_env_info_dict() -> dict[str, str]:
 
 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 | None:
+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)
@@ -411,8 +422,12 @@ def get_package_version(name: str) -> str | None:
 
 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()
 
@@ -445,18 +460,19 @@ def get_requirements_dict() -> dict[str, str]:
             # 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 name in required_packages:
+
+    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 name == "heretic-llm" and not version_info.is_standard_pypi:
+        if package == "heretic-llm" and not version_info.is_standard_pypi:
             continue
 
-        version_str = get_package_version(name)
-        if version_str:
-            dependencies[name] = version_str
+        dependencies[package] = get_package_version(package)
 
     return dependencies

src/heretic/utils.py

@@ -155,18 +155,6 @@ def prompt_password(message: str) -> str:
         return questionary.password(message).ask()
 
 
-def prompt_confirm(message: str, default: bool = True) -> bool:
-    if is_notebook():
-        print()
-        choices = "[Y/n]" if default else "[y/N]"
-        result = input(f"{message} {choices} ").strip().lower()
-        if not result:
-            return default
-        return result in ("y", "yes")
-    else:
-        return questionary.confirm(message, default=default).ask()
-
-
 def format_duration(seconds: float) -> str:
     seconds = round(seconds)
     hours, seconds = divmod(seconds, 3600)
@@ -180,6 +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
@@ -193,7 +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.
@@ -211,7 +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,
@@ -220,9 +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])
 
@@ -272,20 +275,30 @@ def get_trial_parameters(trial: Trial) -> dict[str, str]:
 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"
-    else:
-        model_link = f"[{settings.model}](https://huggingface.co/{settings.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:
+        reproducibility_instructions = ""
+
     return f"""# This is a decensored version of {
         model_link
     }, made using [Heretic](https://github.com/p-e-w/heretic) v{version_info.version}
-
+{reproducibility_instructions}
 ## Abliteration parameters
 
 | Parameter | Value |
@@ -304,9 +317,9 @@ def get_readme_intro(
 | Metric | This model | Original model ({model_link}) |
 | :----- | :--------: | :---------------------------: |
 | **KL divergence** | {trial.user_attrs["kl_divergence"]:.4f} | 0 *(by definition)* |
-| **Refusals** | {trial.user_attrs["refusals"]}/{len(bad_prompts)} | {base_refusals}/{
-        len(bad_prompts)
-    } |
+| **Refusals** | {trial.user_attrs["refusals"]}/{trial.user_attrs["n_bad_prompts"]} | {
+        trial.user_attrs["base_refusals"]
+    }/{trial.user_attrs["n_bad_prompts"]} |
 
 -----
 
@@ -315,250 +328,276 @@ def get_readme_intro(
 
 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 = get_requirements_dict()
-    sorted_requirements = sorted(
-        [f"{name}=={version}" for name, version in requirements.items()],
-        key=lambda x: x.lower(),
-    )
-    return "\n".join(sorted_requirements) + "\n"
+
+    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,
-    timestamp: str | None = None,
-    base_model_commit: str | None = None,
+    include_system_information: bool,
 ) -> str:
-    """Generates a README.md for the reproduce/ folder."""
-    torch_version = torch.__version__
-    install_hint = f"pip install torch=={torch_version}"
-    if "+" in torch_version:
-        suffix = torch_version.split("+")[1]
-        if suffix:
-            install_hint += f" --index-url https://download.pytorch.org/whl/{suffix}"
+    """Generates the contents of a README.md for the reproduce/ folder."""
 
     heterogeneous_warning = ""
-    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 = """
+
+    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 Detected!**
-> This system uses 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.**
+> **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.
 """
 
-    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].strip(")")
-            origin_warning = f"""
-> [!NOTE]
-> **Git Installation Detected**
-> This system installed `heretic-llm` from source repository: `{repo_info}`.
-> To reproduce these results, you must install Heretic from this exact repository and commit.
-"""
-        elif version_info.origin == "Local":
-            origin_warning = """
-> [!WARNING]
-> **Local Code Detected!**
-> This system installed `heretic-llm` from a local directory or wheel. Uncommitted or experimental code may have been executed. **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:
-            origin_warning = """
-> [!WARNING]
-> **Non-Standard Installation Detected!**
-> This system installed `heretic-llm` from an unknown non-standard source. **Reproducibility ***cannot*** be guaranteed in this environment.**
-"""
+            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}"
+            ]
 
-    def format_hf_link(
-        name: str, commit: str | None = None, is_dataset: bool = False
-    ) -> str:
-        if Path(name).exists():
-            return f"`{name}` (Local)"
+            if accelerators.get("api_name") and accelerators.get("api_version"):
+                accelerator_lines.append(
+                    f"  - **{accelerators['api_name']}:** {accelerators['api_version']}"
+                )
 
-        prefix = "datasets/" if is_dataset else ""
-        base_url = f"https://huggingface.co/{prefix}{name}"
-        link = f"[{name}]({base_url})"
-        if commit:
-            commit_url = f"{base_url}/commit/{commit}"
-            link += f" (Commit: [{commit[:7]}]({commit_url}))"
-        return link
+            if accelerators.get("driver_version"):
+                accelerator_lines.append(
+                    f"  - **Driver Version:** {accelerators['driver_version']}"
+                )
 
-    model_link = format_hf_link(settings.model, base_model_commit)
-    dataset_info = f"""## Dataset Information
+            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)
 
-- **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)}"""
+        system_report = f"""## System
 
-    timestamp_str = f"- **Run started at (UTC):** `{timestamp}`" if timestamp else ""
-
-    # System and Accelerator info using structured dictionaries.
-    cpu = get_cpu_info_dict()
-    python_env = get_python_env_info_dict()
-    accelerator = get_accelerator_info_dict()
-
-    # Build System Environment section.
-    system_env_lines = [
-        f"- **OS:** `{platform.platform()}` (`{platform.machine()}`)",
-        f"- **CPU:** `{cpu['brand'] or 'Unknown CPU'}`",
-        f"  - **Information:** Family `{cpu['family']}`, Model `{cpu['model']}`, Stepping `{cpu['stepping']}`",
-    ]
-
-    system_env_lines.extend(
-        [
-            f"- **Python:** `{python_env['version']}` (`{python_env['implementation']}`, `{python_env['compiler']}`) [`{python_env['environment']}`]",
-            f"- **Heretic:** `v{version_info.version}`"
-            + (f" (Origin: `{version_info.origin}`)" if version_info.origin else ""),
-            f"- **PyTorch:** `{torch.__version__}`",
-        ]
-    )
-    system_environment_report = "\n".join(system_env_lines)
-
-    # Build Accelerators section.
-    if accelerator["type"] is None:
-        accelerator_report = "> [!WARNING]\n> **No GPU or other accelerator detected.**"
-    else:
-        devices = accelerator["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 ""
-        accelerator_lines = [
-            f"- **{accelerator['type']}:** Detected `{len(devices)}` device(s){vram_suffix}"
-        ]
-
-        if accelerator.get("api_name") and accelerator.get("api_version"):
-            accelerator_lines.append(
-                f"  - **{accelerator['api_name']}:** `{accelerator['api_version']}`"
-            )
-
-        if accelerator.get("driver_version"):
-            accelerator_lines.append(
-                f"  - **Driver Version:** `{accelerator['driver_version']}`"
-            )
-
-        accelerator_lines.append("- **Devices:**")
-        for i, dev in enumerate(devices):
-            vram = f" (`{dev['vram_gb']:.2f} GB`)" if dev.get("vram_gb") else ""
-            accelerator_lines.append(
-                f"  - **{accelerator['type']} {i}:** `{dev['name']}`{vram}"
-            )
-        accelerator_report = "\n".join(accelerator_lines)
-
-    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}
-
-## Model Information
-
-- **Base Model:** {model_link}
-{timestamp_str}
-
-{dataset_info}
-
-## Selected Trial
-
-- **Trial Number:** `#{trial.user_attrs["index"]}`
-- **Refusal Count:** `{trial.user_attrs.get("refusals")}/{trial.user_attrs.get("total_refusal_prompts")}`
-- **KL Divergence:** `{trial.user_attrs.get("kl_divergence", 0):.6f}`
-
-## System Environment
-
-{system_environment_report}
+- **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}
 
-## Contents
+"""
+        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 = ""
 
-- **config.toml**: The exact configuration used, including the seed `{settings.seed}`.
-- **requirements.txt**: The exact versions of all installed Python packages.
-- **{checkpoint_filename}**: The Optuna study journal containing the history of all trials.
-- **reproduce.json**: A machine-readable version of this report.
-- **SHA256SUMS**: Cryptographic hashes for all uploaded weight files (if applicable).
+    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.
+"""
 
-## How to Reproduce
+    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}"
+            )
 
-1. Ensure your hardware and environment match the specifications in the **System Environment** section above.
-2. Install the exact package versions listed in `requirements.txt`.
-3. Place the provided `config.toml` in your working directory.
-4. Run `heretic` without any additional arguments.
-5. Verify the integrity of the reproduced files by comparing their SHA256 hashes against the manifest in `SHA256SUMS`.
+    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 a `checkpoints/` directory before running `heretic` on the same model.
-
-> [!IMPORTANT]
-> Make sure to install correct PyTorch version from: `{install_hint}`
+> 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 | None = None,
-    base_model_commit: str | None = None,
-    uploaded_model_hashes: dict[str, str] | None = None,
+    timestamp: str,
+    uploaded_model_hashes: dict[str, str],
+    include_system_information: bool,
 ) -> str:
-    """Generates a reproduce.json file for the reproduce/ folder."""
+    """Generates the contents of a reproduce.json file for the reproduce/ folder."""
+
     version_info = get_heretic_version_info()
+
     data = {
-        "base_model": {
-            "id": settings.model,
-            "commit_hash": base_model_commit,
-        },
-        "system": {
-            "os": {"platform": platform.platform(), "machine": platform.machine()},
-            "cpu": get_cpu_info_dict(),
-            "python": get_python_env_info_dict(),
+        "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__,
-            "accelerator": get_accelerator_info_dict(),
+            "requirements": get_requirements_dict(),
         },
-        "requirements": get_requirements_dict(),
-        "settings": settings.model_dump(exclude_none=True),
-        "trial": {
-            "direction_index": trial.user_attrs.get("direction_index"),
-            "parameters": trial.user_attrs.get("parameters"),
-            "metrics": {
-                "refusals": trial.user_attrs.get("refusals"),
-                "total_refusal_prompts": trial.user_attrs.get("total_refusal_prompts"),
-                "kl_divergence": trial.user_attrs.get("kl_divergence"),
-            },
+        "settings": settings.model_dump(),
+        "parameters": {
+            "direction_index": trial.user_attrs["direction_index"],
+            "abliteration_parameters": trial.user_attrs["parameters"],
         },
-        "timestamp": timestamp,
-        "uploaded_model_hashes": uploaded_model_hashes or {},
+        "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 a GNU Coreutils compatible SHA256SUMS file content."""
+    """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"
 
 
@@ -567,13 +606,17 @@ def create_reproduce_folder(
     settings: Settings,
     checkpoint_path: str | Path,
     trial: Trial,
-    uploaded_model_hashes: dict[str, str] | None = None,
-) -> None:
+    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,
@@ -581,50 +624,46 @@ def create_reproduce_folder(
         settings.good_evaluation_prompts,
         settings.bad_evaluation_prompts,
     ]:
-        if not Path(spec.dataset).exists():
-            # Fail if the dataset is missing or unreachable.
-            spec.commit = huggingface_hub.dataset_info(spec.dataset).sha
-
-    # Fetch commit hash for the base model if it's on HF.
-    base_model_commit = None
-    if not Path(settings.model).exists():
-        try:
-            base_model_commit = huggingface_hub.model_info(settings.model).sha
-        except Exception:
-            pass
+        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 / "config.toml").write_text(
-        generate_config_toml(settings), encoding="utf-8"
-    )
     (reproduce_dir / "requirements.txt").write_text(
-        generate_requirements_txt(), encoding="utf-8"
-    )
-    (reproduce_dir / "README.md").write_text(
-        generate_reproduce_readme(
-            settings,
-            checkpoint_filename,
-            trial,
-            timestamp=timestamp,
-            base_model_commit=base_model_commit,
-        ),
+        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"
+            generate_sha256sums(uploaded_model_hashes),
+            encoding="utf-8",
         )
+
     (reproduce_dir / "reproduce.json").write_text(
         generate_reproduce_json(
             settings,
             trial,
             timestamp=timestamp,
-            base_model_commit=base_model_commit,
             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",
     )
@@ -641,22 +680,25 @@ def upload_reproduce_folder(
     token: str,
     checkpoint_path: str | Path,
     trial: Trial,
-) -> None:
+    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 = {}
-    try:
-        api = huggingface_hub.HfApi()
-        info = api.model_info(repo_id=repo_id, files_metadata=True, token=token)
-        # For weights, we only care about safetensors.
-        weight_extensions = (".safetensors",)
-        if info.siblings is not None:
-            for file in info.siblings:
-                if file.rfilename.endswith(weight_extensions):
-                    sha256 = getattr(file, "lfs", {}).get("sha256")
-                    if sha256:
-                        uploaded_model_hashes[file.rfilename] = sha256
-    except Exception as e:
-        # Fail if integrity checks cannot be completed.
-        raise RuntimeError(f"Could not fetch uploaded model hashes: {e}") from e
+
+    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)
@@ -666,6 +708,7 @@ def upload_reproduce_folder(
             checkpoint_path=checkpoint_path,
             trial=trial,
             uploaded_model_hashes=uploaded_model_hashes,
+            include_system_information=include_system_information,
         )
 
         reproduce_dir = tmp_path / "reproduce"