Fixes model name problem for the lora modeules

Author: mrDzurb

ads/aqua/model/model.py

@@ -295,65 +295,73 @@ def create_multi(
 
         selected_models_deployment_containers = set()
 
-        # Process each model
+        # Process each model in the input list
         for model in models:
+            # Retrieve model metadata from the Model Catalog using the model ID
             source_model = DataScienceModel.from_id(model.model_id)
             display_name = source_model.display_name
             model_file_description = source_model.model_file_description
-            # Update model name in user's input model
+            # If model_name is not explicitly provided, use the model's display name
             model.model_name = model.model_name or display_name
 
-            # TODO Uncomment the section below, if only service models should be allowed for multi-model deployment
-            # if not source_model.freeform_tags.get(Tags.AQUA_SERVICE_MODEL_TAG, UNKNOWN):
-            #     raise AquaValueError(
-            #         f"Invalid selected model {display_name}. "
-            #         "Currently only service models are supported for multi model deployment."
-            #     )
+            if not model_file_description:
+                raise AquaValueError(
+                    f"Model '{source_model.display_name}' (ID: {model.model_id}) has no file description. "
+                    "Please register the model first."
+                )
 
-            # check if model is a fine-tuned model and if so, add the fine tuned weights path to the fine_tune_weights_location pydantic field
+            # Check if the model is a fine-tuned model based on its tags
             is_fine_tuned_model = (
                 Tags.AQUA_FINE_TUNED_MODEL_TAG in source_model.freeform_tags
             )
 
-            model_artifact_path = ""
+            base_model_artifact_path = ""
             fine_tune_path = ""
 
             if is_fine_tuned_model:
-                model_artifact_path, fine_tune_path = extract_fine_tune_artifacts_path(
-                    source_model
+                # Extract artifact paths for the base and fine-tuned model
+                base_model_artifact_path, fine_tune_path = (
+                    extract_fine_tune_artifacts_path(source_model)
                 )
-                # once we support multiple LoRA Modules use [LoraModuleSpec(**lora_module) for lora_module in model.fine_tune_weights]
+
+                # Create a single LoRA module specification for the fine-tuned model
+                # TODO: Support multiple LoRA modules in the future
                 model.fine_tune_weights = [
                     LoraModuleSpec(
                         model_id=model.model_id,
-                        model_name=display_name,
+                        model_name=model.model_name,
                         model_path=fine_tune_path,
                     )
                 ]
+
+                # Use the LoRA module name as the model's display name
+                display_name = model.model_name
+
+                # Temporarily override model ID and name with those of the base model
+                # TODO: Revisit this logic once proper base/FT model handling is implemented
                 model.model_id, model.model_name = extract_base_model_from_ft(
                     source_model
                 )
             else:
-                # Retrieve model artifact for base models
-                model_artifact_path = source_model.artifact
+                # For base models, use the original artifact path
+                base_model_artifact_path = source_model.artifact
+                display_name = model.model_name
 
-            if not model_artifact_path:
+            if not base_model_artifact_path:
+                # Fail if no artifact is found for the base model model
                 raise AquaValueError(
-                    f"Model '{display_name}' (ID: {model.model_id}) has no artifacts. "
+                    f"Model '{model.model_name}' (ID: {model.model_id}) has no artifacts. "
                     "Please register the model first."
                 )
 
-            # Update model artifact location in user's input model
-            model.artifact_location = model_artifact_path
+            # Update the artifact path in the model configuration
+            model.artifact_location = base_model_artifact_path
             display_name_list.append(display_name)
-            self._extract_model_task(model, source_model)
 
-            if not model_file_description:
-                raise AquaValueError(
-                    f"Model '{display_name}' (ID: {model.model_id}) has no file description. "
-                    "Please register the model first."
-                )
+            # Extract model task metadata from source model
+            self._extract_model_task(model, source_model)
 
+            # Track model file description in a validated structure
             model_file_description_list.append(
                 ModelFileDescription(**model_file_description)
             )