diff --git a/llm_eval/handler.py b/llm_eval/handler.py
index 288f936..a822264 100644
--- a/llm_eval/handler.py
+++ b/llm_eval/handler.py
@@ -67,19 +67,19 @@ def post_process_output(self, prompt, output):
     
     def prepare_output(self):
         rows = []
-        column_names = ['id', 'model']  # start with 'id' and 'model' columns
+        column_names = ['id', 'model']
         for prompt in self.prompts:
             column_names.append(prompt["name"] + '.input')
             column_names.append(prompt["name"] + '.output')
         df = pd.DataFrame(columns=column_names)
 
-        for model_name in self.models.items():
-            for data_id, data_value in self.dataset.items():  
+        for model_name in self.models:
+            for data_id, data_value in self.dataset.items():
                 row = {'id': data_id, 'model': model_name}
                 for prompt in self.prompts:
                     input_column_name = prompt["name"] + '.input'
                     output_column_name = prompt["name"] + '.output'
-                    row[input_column_name] = f"### Conversation ###\n{data_value}\n\n### Instruction ###\n{prompt['prompt']}\n\n### Output ###\n" 
+                    row[input_column_name] = f"### Conversation ###\n{data_value}\n\n### Instruction ###\n{prompt['prompt']}\n\n### Output ###\n"
                     row[output_column_name] = ""
                 rows.append(row)
         df = pd.concat([df, pd.DataFrame(rows)], ignore_index=True)
@@ -90,8 +90,11 @@ def process_dataset(self):
         for model_name, model_handler in self.models.items():
             self.current_model = model_name
             print(f"Loading {model_name}...")
-            handler = model_handler()
-            self.tokenizer, self.model = handler.load_model_and_tokenizer(self.device, model_name)
+            if model_handler == ModelHandler:
+                self.tokenizer, self.model = self.load_model_and_tokenizer(model_name)
+            else:
+                handler = model_handler()
+                self.tokenizer, self.model = handler.load_model_and_tokenizer(self.device, model_name)
             for index, row in df[df['model'] == model_name].iterrows(): 
                 print(f"Generating outputs for {row['id']}")
                 for col in df.columns:
diff --git a/pyproject.toml b/pyproject.toml
index b2d4721..ece0d8f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "llm-eval"
-version = "0.6.8"
+version = "0.6.9"
 authors = [
     {name = "Jonathan Eisenzopf", email = "jonathan.eisenzopf@talkmap.com"},
 ]