added handle for empty + limited precision

src/display/utils.py

@@ -94,10 +94,10 @@ class WeightType(Enum):
 class Precision(Enum):
     float16 = ModelDetails("float16")
     bfloat16 = ModelDetails("bfloat16")
-    float32 = ModelDetails("float32")
-    #qt_8bit = ModelDetails("8bit")
-    #qt_4bit = ModelDetails("4bit")
-    #qt_GPTQ = ModelDetails("GPTQ")
+    # float32 = ModelDetails("float32")
+    qt_8bit = ModelDetails("8bit")
+    qt_4bit = ModelDetails("4bit")
+    qt_GPTQ = ModelDetails("GPTQ")
     Unknown = ModelDetails("?")
 
     def from_str(precision):
@@ -105,14 +105,14 @@ class Precision(Enum):
             return Precision.float16
         if precision in ["torch.bfloat16", "bfloat16"]:
             return Precision.bfloat16
-        if precision in ["float32"]:
-            return Precision.float32
-        #if precision in ["8bit"]:
-        #    return Precision.qt_8bit
-        #if precision in ["4bit"]:
-        #    return Precision.qt_4bit
-        #if precision in ["GPTQ", "None"]:
-        #    return Precision.qt_GPTQ
+        # if precision in ["float32"]:
+        #     return Precision.float32
+        if precision in ["8bit"]:
+           return Precision.qt_8bit
+        if precision in ["4bit"]:
+           return Precision.qt_4bit
+        if precision in ["GPTQ", "None"]:
+           return Precision.qt_GPTQ
         return Precision.Unknown
 
 # Column selection

src/populate.py

@@ -13,8 +13,9 @@ def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchm
     all_data_json = [v.to_dict() for v in raw_data]
 
     df = pd.DataFrame.from_records(all_data_json)
-    df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
-    df = df[cols].round(decimals=2)
+    if df.shape[0] > 0:
+        df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
+        df = df[cols].round(decimals=2)
 
     # filter out if any of the benchmarks have not been produced
     df = df[has_no_nan_values(df, benchmark_cols)]