fix: follow up after recent PRs

config.default.toml

@@ -7,8 +7,11 @@ dtypes = [
     "auto",
     # If that doesn't work (e.g. on pre-Ampere hardware), fall back to float16.
     "float16",
-    # If that still doesn't work (e.g. due to https://github.com/meta-llama/llama/issues/380),
+    # If "auto" resolves to float32, and that fails because it is too large,
-    # fall back to float32.
+    # and float16 fails due to range issues, try bfloat16.
+    "bfloat16",
+    # If neither of those work, fall back to float32 (which will of course fail
+    # if that was the dtype "auto" resolved to).
     "float32",
 ]
 

src/heretic/config.py

@@ -46,10 +46,11 @@ class Settings(BaseSettings):
             "auto",
             # If that doesn't work (e.g. on pre-Ampere hardware), fall back to float16.
             "float16",
-            # If float16 fails (e.g. due to range issues) and float32 is too large, try bfloat16.
+            # If "auto" resolves to float32, and that fails because it is too large,
+            # and float16 fails due to range issues, try bfloat16.
             "bfloat16",
-            # If that still doesn't work (e.g. due to https://github.com/meta-llama/llama/issues/380),
+            # If neither of those work, fall back to float32 (which will of course fail
-            # fall back to float32.
+            # if that was the dtype "auto" resolved to).
             "float32",
         ],
         description="List of PyTorch dtypes to try when loading model tensors. If loading with a dtype fails, the next dtype in the list will be tried.",

src/heretic/main.py

@@ -27,7 +27,7 @@ from optuna.exceptions import ExperimentalWarning
 from optuna.samplers import TPESampler
 from optuna.study import StudyDirection
 from pydantic import ValidationError
-from questionary import Choice, Style
+from questionary import Choice
 from rich.traceback import install
 
 from .analyzer import Analyzer
@@ -392,11 +392,7 @@ def run():
 
     while True:
         print()
-        trial = prompt_select(
+        trial = prompt_select("Which trial do you want to use?", choices)
-            "Which trial do you want to use?",
-            choices=choices,
-            style=Style([("highlighted", "reverse")]),
-        )
 
         if trial is None or trial == "":
             break
@@ -416,13 +412,12 @@ def run():
             print()
             action = prompt_select(
                 "What do you want to do with the decensored model?",
-                choices=[
+                [
                     "Save the model to a local folder",
                     "Upload the model to Hugging Face",
                     "Chat with the model",
                     "Nothing (return to trial selection menu)",
                 ],
-                style=Style([("highlighted", "reverse")]),
             )
 
             if action is None or action == "Nothing (return to trial selection menu)":
@@ -434,9 +429,7 @@ def run():
             try:
                 match action:
                     case "Save the model to a local folder":
-                        save_directory = prompt_path(
+                        save_directory = prompt_path("Path to the folder:")
-                            "Path to the folder:", only_directories=True
-                        )
                         if not save_directory:
                             continue
 
@@ -470,11 +463,10 @@ def run():
 
                         visibility = prompt_select(
                             "Should the repository be public or private?",
-                            choices=[
+                            [
                                 "Public",
                                 "Private",
                             ],
-                            style=Style([("highlighted", "reverse")]),
                         )
                         private = visibility == "Private"
 

src/heretic/model.py

@@ -70,7 +70,7 @@ class Model:
                 )
 
                 # If we reach this point and the model requires trust_remote_code,
-                # the user must have confirmed it.
+                # either the user accepted, or settings.trust_remote_code is True.
                 if self.trusted_models.get(settings.model) is None:
                     self.trusted_models[settings.model] = True
 

src/heretic/utils.py

@@ -22,7 +22,7 @@ from datasets.config import DATASET_STATE_JSON_FILENAME
 from datasets.download.download_manager import DownloadMode
 from datasets.utils.info_utils import VerificationMode
 from optuna import Trial
-from questionary import Choice
+from questionary import Choice, Style
 from rich.console import Console
 
 from .config import DatasetSpecification, Settings
@@ -31,15 +31,15 @@ print = Console(highlight=False).print
 
 
 def is_notebook() -> bool:
-    # Check for specific environment variables (Colab, Kaggle)
+    # Check for specific environment variables (Colab, Kaggle).
     # This is necessary because when running as a subprocess (e.g. !heretic),
     # get_ipython() might not be available or might not reflect the notebook environment.
     if os.getenv("COLAB_GPU") or os.getenv("KAGGLE_KERNEL_RUN_TYPE"):
         return True
 
-    # Check IPython shell type (for library usage)
+    # Check IPython shell type (for library usage).
     try:
-        from IPython import get_ipython
+        from IPython import get_ipython  # pyright: ignore[reportMissingModuleSource]
 
         shell = get_ipython()
         if shell is None:
@@ -57,11 +57,12 @@ def is_notebook() -> bool:
         return False
 
 
-def prompt_select(message: str, choices: list[Any], style=None) -> Any:
+def prompt_select(message: str, choices: list[Any]) -> Any:
     if is_notebook():
         print()
         print(message)
         real_choices = []
+
         for i, choice in enumerate(choices, 1):
             if isinstance(choice, Choice):
                 print(f"[{i}] {choice.title}")
@@ -73,47 +74,45 @@ def prompt_select(message: str, choices: list[Any], style=None) -> Any:
         while True:
             try:
                 selection = input("Enter number: ")
-                idx = int(selection) - 1
+                index = int(selection) - 1
-                if 0 <= idx < len(real_choices):
+                if 0 <= index < len(real_choices):
-                    return real_choices[idx]
+                    return real_choices[index]
                 print(
                     f"[red]Please enter a number between 1 and {len(real_choices)}[/]"
                 )
             except ValueError:
                 print("[red]Invalid input. Please enter a number.[/]")
     else:
-        return questionary.select(message, choices=choices, style=style).ask()
+        return questionary.select(
+            message,
+            choices=choices,
+            style=Style([("highlighted", "reverse")]),
+        ).ask()
 
 
 def prompt_text(
     message: str,
     default: str = "",
-    unsafe: bool = False,
     qmark: str = "?",
+    unsafe: bool = False,
 ) -> str:
     if is_notebook():
         print()
-        prompt_msg = f"{message} [{default}]: " if default else f"{message}: "
+        result = input(f"{message} [{default}]: " if default else f"{message}: ")
-        result = input(prompt_msg)
         return result if result else default
     else:
-        # For text input, we might need unsafe_ask if requested
+        question = questionary.text(message, default=default, qmark=qmark)
-        q = questionary.text(message, default=default, qmark=qmark)
         if unsafe:
-            return q.unsafe_ask()
+            return question.unsafe_ask()
-        return q.ask()
-
-
-def prompt_path(message: str, default: str = "", only_directories: bool = False) -> str:
-    if is_notebook():
-        print()
-        prompt_msg = f"{message} [{default}]: " if default else f"{message}: "
-        result = input(prompt_msg)
-        return result if result else default
         else:
-        return questionary.path(
+            return question.ask()
-            message, default=default, only_directories=only_directories
+
-        ).ask()
+
+def prompt_path(message: str) -> str:
+    if is_notebook():
+        return prompt_text(message)
+    else:
+        return questionary.path(message, only_directories=True).ask()
 
 
 def prompt_password(message: str) -> str:
@@ -140,20 +139,21 @@ def format_duration(seconds: float) -> str:
 def load_prompts(specification: DatasetSpecification) -> list[str]:
     path = specification.dataset
     split_str = specification.split
+
     if os.path.isdir(path):
         if Path(path, DATASET_STATE_JSON_FILENAME).exists():
             # Dataset saved with datasets.save_to_disk; needs special handling.
             # Path should be the subdirectory for a particular split.
             dataset = load_from_disk(path)
             # Parse the split instructions.
-            ri = ReadInstruction.from_spec(split_str)
+            instruction = ReadInstruction.from_spec(split_str)
             # Associate the split with its number of examples (lines).
             split_name = str(dataset.split)
             name2len = {split_name: len(dataset)}
             # Convert the instructions to absolute indices and select the first one.
-            abs_i = ri.to_absolute(name2len)[0]
+            abs_instruction = instruction.to_absolute(name2len)[0]
             # Get the dataset by applying the indices.
-            dataset = dataset[abs_i.from_ : abs_i.to]
+            dataset = dataset[abs_instruction.from_ : abs_instruction.to]
         else:
             # Path is a local directory.
             dataset = load_dataset(