diff --git a/src/chronos/chronos2/pipeline.py b/src/chronos/chronos2/pipeline.py
index 30785ef..125d1d9 100644
--- a/src/chronos/chronos2/pipeline.py
+++ b/src/chronos/chronos2/pipeline.py
@@ -19,6 +19,7 @@ from transformers import AutoConfig
 from transformers.utils.import_utils import is_peft_available
 from transformers.utils.peft_utils import find_adapter_config_file
 
+
 import chronos.chronos2
 from chronos.base import BaseChronosPipeline, ForecastType
 from chronos.chronos2 import Chronos2Model
@@ -31,6 +32,7 @@ if TYPE_CHECKING:
     import fev
     import pandas as pd
     from peft import LoraConfig
+    from transformers.trainer_callback import TrainerCallback
 
 logger = logging.getLogger(__name__)
 
@@ -111,6 +113,7 @@ class Chronos2Pipeline(BaseChronosPipeline):
         output_dir: Path | str | None = None,
         min_past: int | None = None,
         finetuned_ckpt_name: str = "finetuned-ckpt",
+        callbacks: list["TrainerCallback"] | None = None,
         **extra_trainer_kwargs,
     ) -> "Chronos2Pipeline":
         """
@@ -151,6 +154,8 @@ class Chronos2Pipeline(BaseChronosPipeline):
             are filtered out, by default set equal to prediction_length
         finetuned_ckpt_name
             The name of the directory inside `output_dir` in which the final fine-tuned checkpoint will be saved, by default "finetuned-ckpt"
+        callbacks
+            A list of `TrainerCallback`s which will be forwarded to the HuggingFace `Trainer`
         **extra_trainer_kwargs
             Extra kwargs are directly forwarded to `TrainingArguments`
 
@@ -276,7 +281,7 @@ class Chronos2Pipeline(BaseChronosPipeline):
         )
 
         eval_dataset = None
-        callbacks = []
+        callbacks = callbacks or []
         if validation_inputs is not None:
             # construct validation dataset
             eval_dataset = Chronos2Dataset.convert_inputs(