diff --git a/src/timesfm/configs.py b/src/timesfm/configs.py
index 88a63af..9a263de 100644
--- a/src/timesfm/configs.py
+++ b/src/timesfm/configs.py
@@ -23,11 +23,11 @@ class ForecastConfig:
   """Options for forecasting.
 
   Attributes:
-    max_context: The maximum context length. This is used by the complied decode
+    max_context: The maximum context length. This is used by the compiled decode
       function at inference time during batched inference. Any input time series
       with length less than max_context will be padded with zeros, and with
       length greater than max_context will be truncated.
-    max_horizon: The maximum horizon length. This is used by the complied decode
+    max_horizon: The maximum horizon length. This is used by the compiled decode
       function at inference time during batched inference. The compiled cached
       decoding function will by default forecast till max_horizon.
     normalize_inputs: Whether to normalize the inputs. This is useful when the
diff --git a/src/timesfm/timesfm_2p5/timesfm_2p5_torch.py b/src/timesfm/timesfm_2p5/timesfm_2p5_torch.py
index 9bbd771..3e7c9f1 100644
--- a/src/timesfm/timesfm_2p5/timesfm_2p5_torch.py
+++ b/src/timesfm/timesfm_2p5/timesfm_2p5_torch.py
@@ -85,7 +85,7 @@ class TimesFM_2p5_200M_torch_module(nn.Module):
     if "torch_compile" in kwargs:
       torch_compile = kwargs["torch_compile"]
     if torch_compile:
-      print("Compiling model...")
+      logging.info("Compiling model...")
       self = torch.compile(self)
 
     self.eval()