fix: make reset_model null-safe to handle study cancellations (#77) (#367)

* fix: make reset_model null-safe to handle study cancellations (#77)

* fix: address bot review, use nested getattr and fallback to settings dtypes

* fix: address maintainer review comments in model.py

* fix: address maintainer review feedback on reset_model

* fix: update Model.dtype type annotation to torch.dtype

* chore: revert pyproject.toml and uv.lock changes

src/heretic/model.py

@@ -61,6 +61,7 @@ class Model:
     # 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
@@ -129,6 +130,7 @@ class Model:
                     **self.revision_kwargs,
                     **extra_kwargs,
                 )
+                self.dtype = self.model.dtype
 
                 # If we reach this point and the model requires trust_remote_code,
                 # either the user accepted, or settings.trust_remote_code is True.
@@ -317,30 +319,34 @@ 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=self.trusted_models.get(self.settings.model),