Merge branch 'main' of https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard

app.py

@@ -216,22 +216,14 @@ def change_tab(query_param: str):
 # Searching and filtering
 def update_table(hidden_df: pd.DataFrame, current_columns_df: pd.DataFrame, columns: list, type_query: list, size_query: list, show_deleted: bool, query: str):
     filtered_df = filter_models(hidden_df, type_query, size_query, show_deleted)
-    df = search_table(filtered_df, current_columns_df, query)
-    df = select_columns(df, columns)
+    if query != "":
+        filtered_df = search_table(filtered_df, query)
+    df = select_columns(filtered_df, columns)
 
     return df
 
-def search_table(df: pd.DataFrame, current_columns_df: pd.DataFrame, query: str) -> pd.DataFrame:
-    current_columns = current_columns_df.columns
-    if AutoEvalColumn.model_type.name in current_columns:
-        filtered_df = df[
-            (df[AutoEvalColumn.dummy.name].str.contains(query, case=False))
-            | (df[AutoEvalColumn.model_type.name].str.contains(query, case=False))
-        ]
-    else:
-        filtered_df = df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))]
-
-    return filtered_df
+def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
+    return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))]
 
 def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
     always_here_cols = [

src/display_models/get_model_metadata.py

@@ -26,6 +26,7 @@ def get_model_infos_from_hub(leaderboard_data: List[dict]):
 
     for model_data in tqdm(leaderboard_data):
         model_name = model_data["model_name_for_query"]
+<<<<<<< HEAD
 
         if model_name in model_info_cache:
             model_info = model_info_cache[model_name]
@@ -39,6 +40,16 @@ def get_model_infos_from_hub(leaderboard_data: List[dict]):
                 model_data[AutoEvalColumn.likes.name] = None
                 model_data[AutoEvalColumn.params.name] = get_model_size(model_name, None)
                 continue
+=======
+        try:
+            model_info = api.model_info(model_name)
+        except huggingface_hub.utils._errors.RepositoryNotFoundError:
+            print("Repo not found!", model_name)
+            model_data[AutoEvalColumn.license.name] = "?"
+            model_data[AutoEvalColumn.likes.name] = 0
+            model_data[AutoEvalColumn.params.name] = get_model_size(model_name, None)
+            continue
+>>>>>>> 6e79cea283b9033350b77806ca64c34a2e0cd323
 
         model_data[AutoEvalColumn.license.name] = get_model_license(model_info)
         model_data[AutoEvalColumn.likes.name] = get_model_likes(model_info)
@@ -53,7 +64,7 @@ def get_model_license(model_info):
     try:
         return model_info.cardData["license"]
     except Exception:
-        return None
+        return "?"
 
 
 def get_model_likes(model_info):
@@ -73,7 +84,7 @@ def get_model_size(model_name, model_info):
             size = size_match.group(0)
             return round(float(size[:-1]) if size[-1] == "b" else float(size[:-1]) / 1e3, 3)
         except AttributeError:
-            return None
+            return 0
 
 
 def get_model_type(leaderboard_data: List[dict]):

src/display_models/model_metadata_type.py

@@ -22,6 +22,8 @@ class ModelType(Enum):
 
 MODEL_TYPE_METADATA: Dict[str, ModelType] = {
     "tiiuae/falcon-180B": ModelType.PT,
+    "tiiuae/falcon-180B-chat": ModelType.RL,
+    "microsoft/phi-1_5": ModelType.PT,
     "Qwen/Qwen-7B": ModelType.PT,
     "Qwen/Qwen-7B-Chat": ModelType.RL,
     "notstoic/PygmalionCoT-7b": ModelType.IFT,

src/display_models/read_results.py

@@ -27,7 +27,7 @@ class EvalResult:
     results: dict
     precision: str = ""
     model_type: str = ""
-    weight_type: str = ""
+    weight_type: str = "Original"
     date: str = ""
 
     def to_dict(self):