diff --git a/python/python/lance/dataset.py b/python/python/lance/dataset.py
index 8956587d78a..cb67d62d13f 100644
--- a/python/python/lance/dataset.py
+++ b/python/python/lance/dataset.py
@@ -41,7 +41,6 @@
 
 from .blob import BlobFile
 from .dependencies import (
-    _check_for_hugging_face,
     _check_for_numpy,
     torch,
 )
@@ -5259,15 +5258,6 @@ def write_dataset(
         else:
             data_storage_version = "stable"
 
-    if _check_for_hugging_face(data_obj):
-        # Huggingface datasets
-        from .dependencies import datasets
-
-        if isinstance(data_obj, datasets.Dataset):
-            if schema is None:
-                schema = data_obj.features.arrow_schema
-            data_obj = data_obj.data.to_batches()
-
     reader = _coerce_reader(data_obj, schema)
     _validate_schema(reader.schema)
     # TODO add support for passing in LanceDataset and LanceScanner here
diff --git a/python/python/lance/types.py b/python/python/lance/types.py
index deafd59af51..9fcefec9a83 100644
--- a/python/python/lance/types.py
+++ b/python/python/lance/types.py
@@ -9,7 +9,7 @@
 from pyarrow import RecordBatch
 
 from . import dataset
-from .dependencies import _check_for_pandas
+from .dependencies import _check_for_hugging_face, _check_for_pandas
 from .dependencies import pandas as pd
 
 if TYPE_CHECKING:
@@ -74,6 +74,37 @@ def _coerce_reader(
         and data_obj.__class__.__name__ == "DataFrame"
     ):
         return data_obj.to_arrow().to_reader()
+    elif _check_for_hugging_face(data_obj):
+        from .dependencies import datasets as hf_datasets
+
+        if isinstance(data_obj, hf_datasets.Dataset):
+            if schema is None:
+                schema = data_obj.features.arrow_schema
+            return data_obj.data.to_reader()
+        elif isinstance(data_obj, hf_datasets.DatasetDict):
+            raise ValueError(
+                "DatasetDict is not yet supported. For now please "
+                "iterate through the DatasetDict and pass in single "
+                "Dataset instances (e.g., from dataset_dict.data) to "
+                "`write_dataset`. "
+            )
+        elif isinstance(data_obj, hf_datasets.IterableDataset):
+            if schema is None:
+                schema = data_obj.features.arrow_schema
+
+            def batch_iter():
+                # Try to provide a reasonable batch size. If the user needs to
+                # override this, they can do the conversion to a reader themselves.
+                for dict_batch in data_obj.iter(batch_size=1000):
+                    yield pa.RecordBatch.from_pydict(dict_batch, schema=schema)
+
+            return pa.RecordBatchReader.from_batches(schema, batch_iter())
+        else:
+            raise TypeError(
+                f"Unknown HuggingFace dataset type: {type(data_obj)}. "
+                "Please provide a single Dataset or DatasetDict."
+            )
+
     elif isinstance(data_obj, dict):
         batch = pa.RecordBatch.from_pydict(data_obj, schema=schema)
         return pa.RecordBatchReader.from_batches(batch.schema, [batch])
diff --git a/python/python/tests/test_huggingface.py b/python/python/tests/test_huggingface.py
index e4f460d7116..24dca5c8174 100644
--- a/python/python/tests/test_huggingface.py
+++ b/python/python/tests/test_huggingface.py
@@ -5,6 +5,7 @@
 
 import lance
 import numpy as np
+import pyarrow as pa
 import pytest
 
 datasets = pytest.importorskip("datasets")
@@ -45,3 +46,31 @@ def test_image_hf_dataset(tmp_path: Path):
         (isinstance(img, pil.Image.Image) and np.all(np.array(img) == 0))
         for img in batch
     )
+
+
+def test_iterable_dataset(tmp_path: Path):
+    # IterableDataset yields dict of arrays
+
+    def gen():
+        yield {"text": "Good", "label": 0}
+        yield {"text": "Bad", "label": 1}
+
+    arrow_schema = pa.schema([("text", pa.string()), ("label", pa.int64())])
+    features = datasets.Features.from_arrow_schema(arrow_schema)
+
+    iter_ds = datasets.IterableDataset.from_generator(gen, features=features)
+    # streaming batch size is controlled by max_rows_per_group
+    ds1 = lance.write_dataset(iter_ds, tmp_path / "ds1.lance")
+    assert ds1.count_rows() == 2
+    assert ds1.schema == iter_ds.features.arrow_schema
+
+    # to manually control streaming batch size
+    ds2 = lance.write_dataset(
+        pa.Table.from_arrays([[], []], schema=arrow_schema), tmp_path / "ds2.lance"
+    )
+    for batch in iter_ds.iter(batch_size=1):
+        # shouldn't fail
+        ds2 = lance.write_dataset(batch, tmp_path / "ds2.lance", mode="append")
+
+    assert len(ds1) == len(ds2)
+    assert ds1.schema == ds2.schema