diff --git a/modelopt/torch/utils/dataset_utils.py b/modelopt/torch/utils/dataset_utils.py
index 16bff49c2..9a576a770 100644
--- a/modelopt/torch/utils/dataset_utils.py
+++ b/modelopt/torch/utils/dataset_utils.py
@@ -110,6 +110,69 @@
 ]
 
 
+def _third_party_get_dataset_samples(
+    dataset_name: str, num_samples: int, tokenizer: "PreTrainedTokenizerBase | None"
+) -> list[str]:
+    """Load a third-party dataset with the given name and number of samples.
+
+    for messages: apply_chat_template is applied as needed.
+    for text: no tokenization is done and plain text is still returned.
+    """
+    warn(
+        f"Loading third-party dataset {dataset_name} with the split `train`, as the dataset is not registered in {get_supported_datasets()}."
+    )
+    from datasets import load_dataset
+
+    dataset = load_dataset(
+        dataset_name,
+        streaming=True,
+        split="train",
+    )
+    dataset = dataset.shuffle(seed=42, buffer_size=10000).take(num_samples)
+    texts = []
+    if "messages" in dataset.column_names:
+        if tokenizer is None:
+            raise ValueError(
+                f"Your dataset {dataset_name} has a `messages` column, but no tokenizer was provided. Are you sure you are using a tokenizer that supports chat templates?"
+            )
+        if not hasattr(tokenizer, "apply_chat_template"):
+            raise ValueError(
+                f"Your dataset {dataset_name} has a `messages` column, but the tokenizer does not have an `apply_chat_template` method. Are you sure you are using a tokenizer that supports chat templates?"
+            )
+        texts = []
+        print(
+            f"Using dataset with columns of {dataset_name}: messages and tools to apply chat template."
+        )
+        for i, sample in enumerate(dataset):
+            messages = sample.get("messages", [])
+            kwargs = {}
+            tools = sample.get("tools", [])
+            if tools:
+                kwargs["tools"] = tools
+            if not messages:
+                raise ValueError(
+                    f"Row {i} in dataset {dataset_name} has no messages, or a empty messages."
+                )
+            text: str = tokenizer.apply_chat_template(messages, **kwargs, tokenize=False)
+            if len(text) == 0:
+                raise ValueError(
+                    f"Row {i} in dataset {dataset_name} has empty text after applying chat template."
+                )
+            texts.append(text)
+    elif "prompt" in dataset.column_names:
+        texts = [sample["prompt"] for sample in dataset]
+    elif "text" in dataset.column_names:
+        texts = [sample["text"] for sample in dataset]
+    else:
+        raise NotImplementedError(
+            f"Dataset {dataset_name} is not supported. Please use one of the following: {get_supported_datasets()}. "
+            " For supporting third-party datasets, your dataset must have either a `messages` or `prompt` column, and a `train` split."
+            " For example the `baseten/quant_calibration_dataset_v1` dataset has a `messages` column and a `train` split."
+        )
+
+    return texts
+
+
 def get_dataset_samples(
     dataset_name: str,
     num_samples: int,
@@ -131,10 +194,12 @@ def get_dataset_samples(
     """
     # Load the dataset
     if dataset_name not in SUPPORTED_DATASET_CONFIG:
-        raise NotImplementedError(
+        warn(
             f"dataset {dataset_name} is not supported. Please use one of the following:"
             f" {get_supported_datasets()}."
+            " Trying to set up via third-party datasets."
         )
+        return _third_party_get_dataset_samples(dataset_name, num_samples, tokenizer=tokenizer)
 
     from datasets import load_dataset
 
@@ -244,7 +309,7 @@ def get_dataset_dataloader(
 
     all_samples = []
     for ds_name, num_sample in zip(dataset_name, num_samples):
-        samples = get_dataset_samples(ds_name, num_sample)
+        samples = get_dataset_samples(ds_name, num_sample, tokenizer=tokenizer)
         all_samples.extend(samples)
 
     batch_encoded = tokenizer.batch_encode_plus(