updated model param number reader

src/auto_leaderboard/get_model_metadata.py

@@ -1,4 +1,5 @@
 import re
+import os
 from typing import List
 
 from src.utils_display import AutoEvalColumn
@@ -6,7 +7,7 @@ from src.auto_leaderboard.model_metadata_type import get_model_type
 
 from huggingface_hub import HfApi
 import huggingface_hub
-api = HfApi()
+api = HfApi(token=os.environ.get("H4_TOKEN", None))
 
 
 def get_model_infos_from_hub(leaderboard_data: List[dict]):
@@ -15,9 +16,10 @@ def get_model_infos_from_hub(leaderboard_data: List[dict]):
         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] = None
             model_data[AutoEvalColumn.likes.name] = None
-            model_data[AutoEvalColumn.params.name] = None
+            model_data[AutoEvalColumn.params.name] = get_model_size(model_name, None)
             continue
 
         model_data[AutoEvalColumn.license.name] = get_model_license(model_info)
@@ -41,14 +43,12 @@ def get_model_size(model_name, model_info):
     try:
         return round(model_info.safetensors["total"] / 1e9, 3) 
     except AttributeError:
-        #print(f"Repository {model_id} does not have safetensors weights")
-        pass
-    try:
-        size_match = re.search(size_pattern, model_name.lower())
-        size = size_match.group(0)
-        return round(int(size[:-1]) if size[-1] == "b" else int(size[:-1]) / 1e3, 3)
-    except AttributeError:
-        return None
+        try:
+            size_match = re.search(size_pattern, model_name.lower())
+            size = size_match.group(0)
+            return round(int(size[:-1]) if size[-1] == "b" else int(size[:-1]) / 1e3, 3)
+        except AttributeError:
+            return None
 
 
 def apply_metadata(leaderboard_data: List[dict]):