Update chronos-2 link

src/utils.py

@@ -36,12 +36,7 @@ MODEL_CONFIG = {
     "chronos_bolt_small": ("amazon/chronos-bolt-small", "AWS", True, "DL"),
     "chronos_bolt_base": ("amazon/chronos-bolt-base", "AWS", True, "DL"),
     "chronos-bolt": ("amazon/chronos-bolt-base", "AWS", True, "DL"),
-    "chronos-2": (
-        "https://github.com/amazon-science/chronos-forecasting",
-        "AWS",
-        True,
-        "DL",
-    ),
+    "chronos-2": ("amazon/chronos-2", "AWS", True, "DL"),
     # Moirai Models
     "moirai_large": ("Salesforce/moirai-1.1-R-large", "Salesforce", True, "DL"),
     "moirai_base": ("Salesforce/moirai-1.1-R-base", "Salesforce", True, "DL"),
@@ -146,9 +141,7 @@ def format_leaderboard(df: pd.DataFrame):
     df["zero_shot"] = df["model_name"].apply(get_zero_shot_status)
     # Format leakage column: convert to int for all models, 0 for non-zero-shot
     df["training_corpus_overlap"] = df.apply(
-        lambda row: int(round(row["training_corpus_overlap"] * 100))
-        if row["zero_shot"] == "✓"
-        else 0,
+        lambda row: int(round(row["training_corpus_overlap"] * 100)) if row["zero_shot"] == "✓" else 0,
         axis=1,
     )
     df["link"] = df["model_name"].apply(get_model_link)
@@ -170,9 +163,7 @@ def format_leaderboard(df: pd.DataFrame):
         df.style.map(highlight_model_type_color, subset=["model_name"])
         .map(lambda x: "font-weight: bold", subset=["zero_shot"])
         .apply(
-            lambda x: [
-                "background-color: #f8f9fa" if i % 2 == 1 else "" for i in range(len(x))
-            ],
+            lambda x: ["background-color: #f8f9fa" if i % 2 == 1 else "" for i in range(len(x))],
             axis=0,
         )
     )
@@ -237,9 +228,7 @@ def construct_pairwise_chart(df: pd.DataFrame, col: str, metric_name: str):
     for c in [col, f"{col}_lower", f"{col}_upper"]:
         df[c] *= 100
 
-    model_order = (
-        df.groupby("model_1")[col].mean().sort_values(ascending=False).index.tolist()
-    )
+    model_order = df.groupby("model_1")[col].mean().sort_values(ascending=False).index.tolist()
 
     tooltip = [
         alt.Tooltip("model_1:N", title="Model 1"),
@@ -297,9 +286,7 @@ def construct_pairwise_chart(df: pd.DataFrame, col: str, metric_name: str):
     )
 
 
-def construct_pivot_table_from_df(
-    errors: pd.DataFrame, metric_name: str
-) -> pd.io.formats.style.Styler:
+def construct_pivot_table_from_df(errors: pd.DataFrame, metric_name: str) -> pd.io.formats.style.Styler:
     """Construct styled pivot table from precomputed DataFrame."""
 
     def highlight_by_position(styler):
@@ -333,9 +320,7 @@ def construct_pivot_table(
     baseline_model: str,
     leakage_imputation_model: str,
 ) -> pd.io.formats.style.Styler:
-    errors = fev.pivot_table(
-        summaries=summaries, metric_column=metric_name, task_columns=["task_name"]
-    )
+    errors = fev.pivot_table(summaries=summaries, metric_column=metric_name, task_columns=["task_name"])
     train_overlap = (
         fev.pivot_table(
             summaries=summaries,
@@ -376,9 +361,7 @@ def construct_pivot_table(
                     style_parts.append(f"color: {COLORS['leakage_impute']}")
                 elif is_imputed_baseline.loc[row_idx, col_idx]:
                     style_parts.append(f"color: {COLORS['failure_impute']}")
-                elif not style_parts or (
-                    len(style_parts) == 1 and "font-weight" in style_parts[0]
-                ):
+                elif not style_parts or (len(style_parts) == 1 and "font-weight" in style_parts[0]):
                     style_parts.append(f"color: {COLORS['text_default']}")
 
                 if style_parts: