cosmetic

README.md

@@ -46,4 +46,5 @@ You'll find
 
 # Todo
 
+* Change model types
 * Change background to white

app.py

@@ -60,6 +60,7 @@ LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS,
 def init_leaderboard(dataframe):
     if dataframe is None or dataframe.empty:
         raise ValueError("Leaderboard DataFrame is empty or None.")
+    
     return Leaderboard(
         value=dataframe,
         datatype=[c.type for c in fields(AutoEvalColumn)],
@@ -68,7 +69,7 @@ def init_leaderboard(dataframe):
             cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
             label="Select columns to display:",
         ),
-        # search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
+        search_columns=[AutoEvalColumn.model.name],#, AutoEvalColumn.license.name],
         hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
         filter_columns=[
             ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
@@ -161,7 +162,7 @@ with demo:
                         choices=[i.value.name for i in Precision if i != Precision.Unknown],
                         label="Precision",
                         multiselect=False,
-                        value="float16",
+                        value="bfloat16",
                         interactive=True,
                     )
                     weight_type = gr.Dropdown(

src/display/utils.py

@@ -62,10 +62,12 @@ class ModelDetails:
 
 
 class ModelType(Enum):
-    PT = ModelDetails(name="pretrained", symbol="🟢")
-    FT = ModelDetails(name="fine-tuned", symbol="🔶")
-    IFT = ModelDetails(name="instruction-tuned", symbol="⭕")
-    RL = ModelDetails(name="RL-tuned", symbol="🟦")
+    # PT = ModelDetails(name="pretrained", symbol="🟢")
+    # FT = ModelDetails(name="fine-tuned", symbol="🔶")
+    # IFT = ModelDetails(name="instruction-tuned", symbol="⭕")
+    # RL = ModelDetails(name="RL-tuned", symbol="🟦")
+    UNSP = ModelDetails(name="💬 Unspecialized", symbol="💬")
+    SP = ModelDetails(name="🏛️ Specialized", symbol="🏛️")
     Unknown = ModelDetails(name="", symbol="?")
 
     def to_str(self, separator=" "):
@@ -73,24 +75,28 @@ class ModelType(Enum):
 
     @staticmethod
     def from_str(type):
-        if "fine-tuned" in type or "🔶" in type:
-            return ModelType.FT
-        if "pretrained" in type or "🟢" in type:
-            return ModelType.PT
-        if "RL-tuned" in type or "🟦" in type:
-            return ModelType.RL
-        if "instruction-tuned" in type or "⭕" in type:
-            return ModelType.IFT
+        # if "fine-tuned" in type or "🔶" in type:
+        #     return ModelType.FT
+        # if "pretrained" in type or "🟢" in type:
+        #     return ModelType.PT
+        # if "RL-tuned" in type or "🟦" in type:
+        #     return ModelType.RL
+        # if "instruction-tuned" in type or "⭕" in type:
+        #     return ModelType.IFT
+        if "Specialized" in type or "🏛️" in type:
+            return ModelType.SP
+        if "Unspecialized" in type or "💬" in type:
+            return ModelType.UNSP
         return ModelType.Unknown
 
 class WeightType(Enum):
-    Adapter = ModelDetails("Adapter")
     Original = ModelDetails("Original")
+    Adapter = ModelDetails("Adapter")
     Delta = ModelDetails("Delta")
 
 class Precision(Enum):
-    float16 = ModelDetails("float16")
     bfloat16 = ModelDetails("bfloat16")
+    float16 = ModelDetails("float16")
     Unknown = ModelDetails("?")
 
     def from_str(precision):