diff --git a/MaxText/configs/base.yml b/MaxText/configs/base.yml
index 81ec1d544e..b01a874549 100644
--- a/MaxText/configs/base.yml
+++ b/MaxText/configs/base.yml
@@ -438,6 +438,11 @@ hf_eval_split: ''
 hf_eval_files: ''
 hf_access_token: ''
 # for Grain input pipeline (dataset_type=grain)
+# Path to grain data files. Can be a single pattern or multiple patterns with weights.
+# For multiple patterns, use semicolon (;) to separate and colon (:) to specify weights.
+# Example: "path/to/data1.array_record*:0.3;path/to/data2.array_record*:0.7"
+# Note: When using multiple files (separated by ';'), only ArrayRecord format is supported.
+# For more details, see https://github.com/google/maxtext/blob/main/getting_started/Data_Input_Pipeline.md#grain-input-pipeline
 grain_train_files: ''
 grain_eval_files: ''
 grain_file_type: 'arrayrecord' # arrayrecord or parquet
diff --git a/MaxText/input_pipeline/_grain_data_processing.py b/MaxText/input_pipeline/_grain_data_processing.py
index dd055ed988..47b8e0539c 100644
--- a/MaxText/input_pipeline/_grain_data_processing.py
+++ b/MaxText/input_pipeline/_grain_data_processing.py
@@ -33,6 +33,13 @@
 from MaxText import tokenizer
 
 
+def find_data_files(data_file_pattern):
+  data_files = glob.glob(str(Path(data_file_pattern).expanduser().resolve()))
+  assert len(data_files) > 0, f"No file found with pattern {data_file_pattern}."
+  max_logging.log(f"Found {len(data_files)} files for train/eval with grain")
+  return data_files
+
+
 def get_datasets(
     data_file_pattern,
     data_file_type,
@@ -44,17 +51,26 @@ def get_datasets(
     grain_worker_count,
 ):
   """Load dataset from array_record files for using with grain"""
-  data_files = glob.glob(str(Path(data_file_pattern).expanduser().resolve()))
-  assert len(data_files) > 0, f"No file found with pattern {data_file_pattern}."
-  max_logging.log(f"Found {len(data_files)} files for train/eval with grain")
   if data_file_type == "arrayrecord":
-    dataset = grain.MapDataset.source(grain.ArrayRecordDataSource(data_files))
+    if ";" in data_file_pattern:
+      data_file_patterns, weights = zip(*[pattern.split(":") for pattern in data_file_pattern.split(";")])
+      assert len(data_file_patterns) == len(weights), "Number of data file patterns and weights must match"
+      weights = [float(weight) for weight in weights]
+      weights = [round(weight / sum(weights), 4) for weight in weights]
+      dataset_list = [
+          grain.MapDataset.source(grain.ArrayRecordDataSource(find_data_files(pattern))) for pattern in data_file_patterns
+      ]
+      dataset = grain.MapDataset.mix(dataset_list, weights)
+    else:
+      data_files = find_data_files(data_file_pattern)
+      dataset = grain.MapDataset.source(grain.ArrayRecordDataSource(data_files))
     if shuffle:
       dataset = dataset.shuffle(seed=shuffle_seed)
     dataset = dataset.repeat(num_epoch)
     dataset = dataset[dataloading_host_index::dataloading_host_count]  # sharding
     dataset = dataset.to_iter_dataset()
   elif data_file_type == "parquet":
+    data_files = find_data_files(data_file_pattern)
     dataset = grain.MapDataset.source(data_files)
     if shuffle:
       dataset = dataset.shuffle(seed=shuffle_seed)
diff --git a/MaxText/input_pipeline/_input_pipeline_utils.py b/MaxText/input_pipeline/_input_pipeline_utils.py
index d4e7ecb58c..7e08541a5c 100644
--- a/MaxText/input_pipeline/_input_pipeline_utils.py
+++ b/MaxText/input_pipeline/_input_pipeline_utils.py
@@ -332,7 +332,7 @@ def map(self, element):
 
 @dataclasses.dataclass
 class Rekey(grain.MapTransform):
-  """Rname keys according to a mappign dict"""
+  """Rename keys according to a mapping dict"""
 
   def __init__(self, mapping_dict, keep_old_keys=False):
     self.mapping_dict = mapping_dict
diff --git a/MaxText/tests/grain_data_processing_test.py b/MaxText/tests/grain_data_processing_test.py
index 262c4d8413..87f3f8088c 100644
--- a/MaxText/tests/grain_data_processing_test.py
+++ b/MaxText/tests/grain_data_processing_test.py
@@ -105,6 +105,42 @@ def get_first_batch(iterator):
     self.assertTrue((train_batch1["targets"] == train_batch2["targets"]).all())
 
 
+class GrainArrayRecordProcessingWithMultiSourceBlendingTest(GrainArrayRecordProcessingTest):
+
+  def setUp(self):
+    super().setUp()
+    temp_dir = tempfile.gettempdir()
+    # We use the same dataset for testing, but you can use different datasets by changing the file patterns.
+    grain_train_files = [
+        f"{temp_dir}/gcsfuse/array-record/c4/en/3.0.1/c4-train.array_record*:0.3",
+        f"{temp_dir}/gcsfuse/array-record/c4/en/3.0.1/c4-train.array_record*:0.7",
+    ]
+    grain_train_files = ";".join(grain_train_files)
+    self.config = pyconfig.initialize(
+        [sys.argv[0], os.path.join(PKG_DIR, "configs", "base.yml")],
+        per_device_batch_size=1,
+        run_name="test",
+        mesh_axes=["data"],
+        logical_axis_rules=[["batch", "data"]],
+        data_sharding=["data"],
+        base_output_directory="gs://max-experiments/",
+        dataset_type="grain",
+        grain_train_files=grain_train_files,
+        tokenizer_path=os.path.join(os.path.dirname(PKG_DIR), "assets", "tokenizer"),
+        enable_checkpointing=False,
+    )
+    self.mesh_shape_1d = (len(jax.devices()),)
+    self.mesh = Mesh(mesh_utils.create_device_mesh(self.mesh_shape_1d), self.config.mesh_axes)
+    self.process_indices = input_pipeline_interface.get_process_loading_real_data(
+        self.config.data_sharding,
+        self.config.global_batch_size_to_load,
+        self.config.global_batch_size_to_train_on,
+        self.config.max_target_length,
+        self.mesh,
+    )
+    self.train_iter = _grain_data_processing.make_grain_train_iterator(self.config, self.mesh, self.process_indices)
+
+
 class GrainParquetProcessingTest(unittest.TestCase):
 
   @classmethod
diff --git a/getting_started/Data_Input_Pipeline.md b/getting_started/Data_Input_Pipeline.md
index fc4c13c674..a9140c4e4d 100644
--- a/getting_started/Data_Input_Pipeline.md
+++ b/getting_started/Data_Input_Pipeline.md
@@ -102,7 +102,18 @@ bash setup_gcsfuse.sh DATASET_GCS_BUCKET=$BUCKET_NAME MOUNT_PATH=$MOUNT_PATH [FI
 ```
 3. Set `dataset_type=grain` and set `grain_train_files` to match the ArrayRecord files via a local path since the bucket has been mounted.
 4. Tune `grain_worker_count` for performance. This parameter controls the number of child process used by Grain (more details in [behind_the_scene](https://github.com/google/grain/blob/main/docs/behind_the_scenes.md), [code](https://github.com/google/grain/blob/main/grain/_src/python/grain_pool.py)). If you use a large number of workers, please check your config for gcsfuse in [setup_gcsfuse.sh](https://github.com/google/maxtext/blob/main/setup_gcsfuse.sh) to avoid gcsfuse throttling.
-5. Example command:
+
+5. For multi-source blending, you can specify multiple data sources with their respective weights using semicolon (;) as separator and colon (:) for weights. The weights will be automatically normalized to sum to 1.0. For example:
+```
+# Blend two data sources with 30% from first source and 70% from second source
+grain_train_files=/tmp/gcsfuse/dataset1.array_record*:0.3;/tmp/gcsfuse/dataset2.array_record*:0.7
+
+# Blend three data sources with equal weights (will be normalized to 0.33 each)
+grain_train_files=/tmp/gcsfuse/dataset1.array_record*:1;/tmp/gcsfuse/dataset2.array_record*:1;/tmp/gcsfuse/dataset3.array_record*:1
+```
+Note: When using multiple data sources, only ArrayRecord format is supported.
+
+6. Example command:
 ```
 bash setup_gcsfuse.sh \
 DATASET_GCS_BUCKET=maxtext-dataset \
@@ -114,7 +125,7 @@ grain_file_type=arrayrecord \
 grain_train_files=/tmp/gcsfuse/array-record/c4/en/3.0.1/c4-train.array_record* \
 grain_worker_count=2
 ```
-6. Using validation set for eval
+7. Using validation set for eval
 When setting eval_interval > 0, eval will be run with a specified eval dataset. Example config:
 ```
 eval_interval: 10000