update scripts

src/leaderboard/load_results.py

@@ -1,4 +1,25 @@
 import pandas as pd
+import re
+from huggingface_hub import HfApi
+api = HfApi()
+
+def get_model_size(model_name, precision: str = "BF16", revision: str = "main"):
+    model_info = api.model_info(repo_id=model_name, revision=revision)
+    # model_size = get_model_size(model_info=model_info, precision=precision)
+    size_pattern = size_pattern = re.compile(r"(\d\.)?\d+(b|m)")
+    try:
+        model_size = round(model_info.safetensors["total"] / 1e9, 3)
+    except (AttributeError, TypeError):
+        try:
+            size_match = re.search(size_pattern, model_info.modelId.lower())
+            model_size = size_match.group(0)
+            model_size = round(float(model_size[:-1]) if model_size[-1] == "b" else float(model_size[:-1]) / 1e3, 3)
+        except AttributeError:
+            return 0  # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in app.py
+
+    size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.modelId.lower()) else 1
+    model_size = size_factor * model_size
+    return model_size
 
 def load_data(data_path):
     df = pd.read_csv(data_path, skiprows=1, header=0).dropna()
@@ -43,4 +64,10 @@ def load_data(data_path):
     df_mmlu['type'] = df_mmlu['type'].map({'base': '🟢', 'chat': '🔶'})
     df_avg['type'] = df_avg['type'].map({'base': '🟢', 'chat': '🔶'})
 
-    return df_m3exam, df_mmlu, df_avg
+    return df_m3exam, df_mmlu, df_avg
+
+
+if __name__ == "__main__":
+    model_name = "SeaLLMs/SeaLLM-7B-v2"
+    model_size = get_model_size(model_name)
+    print(model_size)