diff --git a/src/chronos/chronos2/pipeline.py b/src/chronos/chronos2/pipeline.py
index 3eddcd7f..d0a89d8d 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:
         """
@@ -195,6 +195,7 @@ def fit(
         model.load_state_dict(self.model.state_dict())
 
         if finetune_mode == "lora":
+            lora_revision = self.revision
             if lora_config is None:
                 lora_config = LoraConfig(
                     r=8,
@@ -206,8 +207,10 @@ def fit(
                         "self_attention.o",
                         "output_patch_embedding.output_layer",
                     ],
+                    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), (
@@ -1161,6 +1164,7 @@ def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
         Load the model, either from a local path, S3 prefix or from the HuggingFace Hub.
         Supports the same arguments as ``AutoConfig`` and ``AutoModel`` from ``transformers``.
         """
+        revision = kwargs.get("revision")
 
         # Check if the model is on S3 and cache it locally first
         # NOTE: Only base models (not LoRA adapters) are supported via S3
@@ -1178,7 +1182,7 @@ def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
 
             model = AutoPeftModel.from_pretrained(pretrained_model_name_or_path, *args, **kwargs)
             model = model.merge_and_unload()
-            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)
@@ -1192,7 +1196,7 @@ def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
             class_ = Chronos2Model
 
         model = class_.from_pretrained(pretrained_model_name_or_path, *args, **kwargs)
-        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 3fac7261..14f81796 100644
--- a/test/test_chronos2.py
+++ b/test/test_chronos2.py
@@ -1132,3 +1132,38 @@ def test_eager_and_sdpa_produce_identical_outputs(pipeline):
     for out_eager, out_sdpa in zip(outputs_eager_grouped, outputs_sdpa_grouped):
         # Should match exactly or very close (numerical precision)
         assert torch.allclose(out_eager, out_sdpa, atol=1e-5, rtol=1e-4)
+
+
+@pytest.mark.parametrize("source_revision", ["my-test-branch", None])
+def test_lora_config_uses_source_revision_from_instantiation(
+    pipeline: Chronos2Pipeline, tmpdir, source_revision
+):
+    """
+    Test that fit in 'lora' mode correctly uses the 'revision'
+    stored in the pipeline instance.
+    """
+    output_dir = Path(tmpdir)
+    dummy_inputs = [torch.rand(100)]
+
+    pipeline.revision = source_revision
+
+    pipeline.fit(
+        inputs=dummy_inputs,
+        prediction_length=10,
+        finetune_mode="lora",
+        output_dir=output_dir,
+        num_steps=1,  # Keep it fast
+        batch_size=32,
+    )
+
+    adapter_config_path = output_dir / "finetuned-ckpt" / "adapter_config.json"
+    assert adapter_config_path.exists(), "adapter_config.json was not created"
+
+    with open(adapter_config_path, "r") as f:
+        adapter_config = json.load(f)
+
+    if source_revision is not None:
+        assert "revision" in adapter_config
+        assert adapter_config["revision"] == source_revision
+    else:
+        assert "revision" not in adapter_config or adapter_config["revision"] is None