diff --git a/llmfoundry/data/finetuning/dataloader.py b/llmfoundry/data/finetuning/dataloader.py
index 639beba6f0..bbde8c4629 100644
--- a/llmfoundry/data/finetuning/dataloader.py
+++ b/llmfoundry/data/finetuning/dataloader.py
@@ -233,6 +233,7 @@ def build_finetuning_dataloader(
             max_seq_len=dataset_cfg['max_seq_len'],
             allow_unsafe_types=dataset_cfg.get('allow_unsafe_types', False),
             replication=replication_factor,
+            packing_ratio=dataloader_batch_size / dataset_batch_size,
         )
 
     else:
diff --git a/llmfoundry/data/finetuning/tasks.py b/llmfoundry/data/finetuning/tasks.py
index 40f178fb6e..67102200cb 100644
--- a/llmfoundry/data/finetuning/tasks.py
+++ b/llmfoundry/data/finetuning/tasks.py
@@ -592,6 +592,7 @@ def __init__(
         max_seq_len: int = 2048,
         allow_unsafe_types: bool = False,
         replication: Optional[int] = None,
+        packing_ratio: Optional[float] = None,
         **kwargs: Any,
     ):
 
@@ -644,6 +645,7 @@ def __init__(
 
         self.tokenizer = tokenizer
         self.max_seq_len = max_seq_len
+        self.packing_ratio = packing_ratio
 
     # How to process a sample
     def __getitem__(self, idx: int) -> Dict[str, Any]:
@@ -675,6 +677,15 @@ def __getitem__(self, idx: int) -> Dict[str, Any]:
             return {'turns': [sample]}
         return tokenize_formatted_example(sample, tokenizer=self.tokenizer)
 
+    def state_dict(self, num_samples: int,
+                   from_beginning: bool) -> Dict[str, Any]:
+        if self.packing_ratio is not None:
+            num_samples = self.packing_ratio * num_samples
+
+        return super().state_dict(
+            num_samples=num_samples, from_beginning=from_beginning
+        )
+
 
 class DatasetConstructor: