feat: Refactor save machinery and always allow user to save LoRA (#110)

src/heretic/main.py

@@ -53,9 +53,11 @@ from .utils import (
 
 def obtain_merge_strategy(settings: Settings) -> str | None:
     """
-    Prompts the user for how to proceed with quantized models.
+    Prompts the user for how to proceed with saving the model.
+    Provides info to the user if the model is quantized on memory use.
     Returns "merge", "adapter", or None (if cancelled/invalid).
     """
+
     # Prompt for all PEFT models to ensure user is aware of merge implications
     if settings.quantization == QuantizationMethod.BNB_4BIT:
         # Quantized models need special handling - we must reload the base model
@@ -103,7 +105,12 @@ def obtain_merge_strategy(settings: Settings) -> str | None:
         "How do you want to proceed?",
         choices=[
             Choice(
-                    title="Merge full model (reload base model on CPU - requires high RAM)",
+                title="Merge full model"
+                + (
+                    ""
+                    if settings.quantization == QuantizationMethod.NONE
+                    else " (reload base model on CPU - requires high RAM)"
+                ),
                 value="merge",
             ),
             Choice(
@@ -114,8 +121,31 @@ def obtain_merge_strategy(settings: Settings) -> str | None:
     )
     return merge_choice
 
-    # Default for non-quantized models
+
-    return "merge"
+def save_model(
+    model: Model,
+    save_directory: str,
+    settings: Settings,
+    strategy: str | None = None,
+) -> None:
+    print("Saving model...")
+    if strategy is None:
+        strategy = obtain_merge_strategy(settings)
+        if strategy is None:
+            print("[yellow]Action cancelled.[/]")
+            return
+
+    if strategy == "adapter":
+        model.model.save_pretrained(save_directory)
+    else:
+        merged_model = model.get_merged_model()
+        merged_model.save_pretrained(save_directory)
+        del merged_model
+        empty_cache()
+
+    model.tokenizer.save_pretrained(save_directory)
+
+    print(f"Model saved to [bold]{save_directory}[/].")
 
 
 def run():
@@ -601,22 +631,11 @@ def run():
                             if not save_directory:
                                 continue
 
-                            print("Saving model...")
+                            save_model(
-                            strategy = obtain_merge_strategy(settings)
+                                model,
-                            if strategy is None:
+                                save_directory,
-                                print("[yellow]Action cancelled.[/]")
+                                settings,
-                                continue
+                            )
-
-                            if strategy == "adapter":
-                                model.model.save_pretrained(save_directory)
-                            else:
-                                merged_model = model.get_merged_model()
-                                merged_model.save_pretrained(save_directory)
-                                del merged_model
-                                empty_cache()
-
-                            model.tokenizer.save_pretrained(save_directory)
-                            print(f"Model saved to [bold]{save_directory}[/].")
 
                         case "Upload the model to Hugging Face":
                             # We don't use huggingface_hub.login() because that stores the token on disk,