diff --git a/experiments/foundation-time-series-arena/eval-chronos.py b/experiments/foundation-time-series-arena/eval-chronos.py
new file mode 100644
index 00000000..09f0e44e
--- /dev/null
+++ b/experiments/foundation-time-series-arena/eval-chronos.py
@@ -0,0 +1,34 @@
+import fire
+import transformers
+from xiuhmolpilli.arena import FoundationalTimeSeriesArena
+from xiuhmolpilli.models.foundational import Chronos
+
+
+if __name__ == "__main__":
+    transformers.set_seed(42)  # for reproducibility
+
+    frequencies = ["Hourly", "Daily", "Weekly", "Monthly"]
+    files = [
+        f"./nixtla-foundational-time-series/data/{freq}.parquet" for freq in frequencies
+    ]
+    arena = FoundationalTimeSeriesArena(
+        models=[
+            Chronos(
+                repo_id="amazon/chronos-t5-large", batch_size=16, alias="Chronos-Large"
+            ),
+            Chronos(
+                repo_id="amazon/chronos-t5-base", batch_size=40, alias="Chronos-Base"
+            ),
+            Chronos(
+                repo_id="amazon/chronos-t5-small", batch_size=64, alias="Chronos-Small"
+            ),
+            Chronos(
+                repo_id="amazon/chronos-t5-mini", batch_size=128, alias="Chronos-Mini"
+            ),
+            Chronos(
+                repo_id="amazon/chronos-t5-tiny", batch_size=256, alias="Chronos-Tiny"
+            ),
+        ],
+        parquet_data_paths=files,
+    )
+    fire.Fire(arena.compete)
diff --git a/experiments/foundation-time-series-arena/xiuhmolpilli/models/foundational/chronos.py b/experiments/foundation-time-series-arena/xiuhmolpilli/models/foundational/chronos.py
index 20a39b0a..9768dc1f 100644
--- a/experiments/foundation-time-series-arena/xiuhmolpilli/models/foundational/chronos.py
+++ b/experiments/foundation-time-series-arena/xiuhmolpilli/models/foundational/chronos.py
@@ -1,4 +1,4 @@
-from typing import Iterable, List
+from typing import Iterable
 
 import numpy as np
 import pandas as pd
@@ -29,23 +29,13 @@ def __init__(
 
     @classmethod
     def from_df(cls, df: pd.DataFrame, batch_size: int):
-        num_unique_ids = df["unique_id"].nunique()
-        max_series_length = df["unique_id"].value_counts().max()
-        padded_tensor = torch.full(
-            size=(num_unique_ids, max_series_length),
-            fill_value=torch.nan,
-            dtype=torch.bfloat16,
-        )  # type: ignore
+        tensors = []
         df_sorted = df.sort_values(by=["unique_id", "ds"])
-        for idx, (_, group) in enumerate(df_sorted.groupby("unique_id")):
-            series_length = len(group)
-            padded_tensor[idx, -series_length:] = torch.tensor(
-                group["y"].values,
-                dtype=torch.bfloat16,
-            )
+        for _, group in df_sorted.groupby("unique_id"):
+            tensors.append(torch.tensor(group["y"].values))
         uids = df_sorted["unique_id"].unique()
         last_times = df_sorted.groupby("unique_id")["ds"].tail(1)
-        return cls(padded_tensor, uids, last_times, batch_size)
+        return cls(tensors, uids, last_times, batch_size)
 
     def __len__(self):
         return self.n_batches