Another fix to call llama functions.

llm_eval/handler.py

@@ -85,14 +85,13 @@ def prepare_output(self):
         df = pd.concat([df, pd.DataFrame(rows)], ignore_index=True)
         return df
 
-
     def process_dataset(self):
         df = self.prepare_output()
-        for model_name, model_handler in self.models.items():  # Unpack the tuple correctly
+        for model_name, model_handler in self.models.items():
             self.current_model = model_name
             print(f"Loading {model_name}...")
-            self.tokenizer, self.model = model_handler.load_model_and_tokenizer(model_name)
-            for index, row in df[df['model'] == model_name].iterrows():  # Process the correct group rather than the entire df
+            self.tokenizer, self.model = model_handler().load_model_and_tokenizer(model_name)
+            for index, row in df[df['model'] == model_name].iterrows(): 
                 print(f"Generating outputs for {row['id']}")
                 for col in df.columns:
                     if col.endswith('.input'):
@@ -103,7 +102,6 @@ def process_dataset(self):
             self.unload_model(model_name)
         return df
 
-
     def unload_model(self, model_id):
         del self.model, self.tokenizer
         import torch

pyproject.toml

@@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "llm-eval"
-version = "0.6.1"
+version = "0.6.2"
 authors = [
     {name = "Jonathan Eisenzopf", email = "[email protected]"},
 ]