diff --git a/src/chronos/chronos2/pipeline.py b/src/chronos/chronos2/pipeline.py
index 1f882c3..7ffafee 100644
--- a/src/chronos/chronos2/pipeline.py
+++ b/src/chronos/chronos2/pipeline.py
@@ -40,10 +40,10 @@ class Chronos2Pipeline(BaseChronosPipeline):
     forecast_type: ForecastType = ForecastType.QUANTILES
     default_context_length: int = 2048
 
-    def __init__(self, model: Chronos2Model):
+    def __init__(self, model: Chronos2Model, revision: str | None = None):
         super().__init__(inner_model=model)
         self.model = model
-
+        self.revision = revision
     @staticmethod
     def _get_prob_mass_per_quantile_level(quantile_levels: torch.Tensor) -> torch.Tensor:
         """
@@ -195,7 +195,7 @@ class Chronos2Pipeline(BaseChronosPipeline):
         model.load_state_dict(self.model.state_dict())
 
         if finetune_mode == "lora":
-            lora_revision = getattr(self.model.config, "_source_revision", None)
+            lora_revision = self.revision
             if lora_config is None:
                 lora_config = LoraConfig(
                     r=8,
@@ -210,7 +210,6 @@ class Chronos2Pipeline(BaseChronosPipeline):
                     revision=lora_revision,
                 )
             elif isinstance(lora_config, dict):
-                lora_config.setdefault("revision", lora_revision)
                 lora_config = LoraConfig(**lora_config)
             else:
                 assert isinstance(lora_config, LoraConfig), (
@@ -1182,11 +1181,7 @@ class Chronos2Pipeline(BaseChronosPipeline):
 
             model = AutoPeftModel.from_pretrained(pretrained_model_name_or_path, *args, **kwargs)
             model = model.merge_and_unload()
-
-            if revision:
-                model.config._source_revision = revision
-
-            return cls(model=model)
+            return cls(model=model, revision=revision)
 
         # Handle the case for the base model
         config = AutoConfig.from_pretrained(pretrained_model_name_or_path, *args, **kwargs)
@@ -1200,11 +1195,7 @@ class Chronos2Pipeline(BaseChronosPipeline):
             class_ = Chronos2Model
 
         model = class_.from_pretrained(pretrained_model_name_or_path, *args, **kwargs)
-
-        if revision:
-            model.config._source_revision = revision
-
-        return cls(model=model)
+        return cls(model=model, revision=revision)
 
     def save_pretrained(self, save_directory: str | Path, *args, **kwargs):
         """
diff --git a/test/test_chronos2.py b/test/test_chronos2.py
index a570ad3..14f8179 100644
--- a/test/test_chronos2.py
+++ b/test/test_chronos2.py
@@ -1139,18 +1139,13 @@ def test_lora_config_uses_source_revision_from_instantiation(
     pipeline: Chronos2Pipeline, tmpdir, source_revision
 ):
     """
-    Test that fit in 'lora' mode correctly uses the '_source_revision'
-    that was (notionally) stored in the model's config during instantiation.
+    Test that fit in 'lora' mode correctly uses the 'revision'
+    stored in the pipeline instance.
     """
     output_dir = Path(tmpdir)
     dummy_inputs = [torch.rand(100)]
 
-    # Manually set the source revision on the config to simulate
-    # it being set during from_pretrained
-    if source_revision:
-        pipeline.model.config._source_revision = source_revision
-    elif hasattr(pipeline.model.config, "_source_revision"):
-        delattr(pipeline.model.config, "_source_revision")
+    pipeline.revision = source_revision
 
     pipeline.fit(
         inputs=dummy_inputs,