diff --git a/llmfoundry/data/finetuning/dataloader.py b/llmfoundry/data/finetuning/dataloader.py
index ec7e8fb37a..004870d7b4 100644
--- a/llmfoundry/data/finetuning/dataloader.py
+++ b/llmfoundry/data/finetuning/dataloader.py
@@ -1,15 +1,15 @@
 # Copyright 2022 MosaicML LLM Foundry authors
 # SPDX-License-Identifier: Apache-2.0
-from typing import Union
 import logging
 import os
+from typing import Union
 
+import datasets as hf_datasets
 import torch
 from composer.utils import dist, get_file, parse_uri
 from omegaconf import DictConfig
 from torch.utils.data import DataLoader
 from transformers import PreTrainedTokenizerBase
-import datasets as hf_datasets
 
 from llmfoundry.data.finetuning.collator import Seq2SeqFinetuningCollator
 from llmfoundry.data.finetuning.tasks import dataset_constructor
@@ -255,8 +255,10 @@ def _validate_config(dataset_cfg: DictConfig):
         )
 
 
-def _build_hf_dataset_from_remote(cfg: DictConfig,
-                                  tokenizer: PreTrainedTokenizerBase) -> Union[hf_datasets.DatasetDict, hf_datasets.Dataset, hf_datasets.IterableDatasetDict, hf_datasets.IterableDataset]:
+def _build_hf_dataset_from_remote(
+    cfg: DictConfig, tokenizer: PreTrainedTokenizerBase
+) -> Union[hf_datasets.DatasetDict, hf_datasets.Dataset,
+           hf_datasets.IterableDatasetDict, hf_datasets.IterableDataset]:
     """Builds a dataset from a remote object store.
 
     This function supports 'jsonl', 'csv', and 'parquet' file formats for the dataset. It will attempt to download
diff --git a/llmfoundry/data/finetuning/tasks.py b/llmfoundry/data/finetuning/tasks.py
index 54163844eb..59b62413d4 100644
--- a/llmfoundry/data/finetuning/tasks.py
+++ b/llmfoundry/data/finetuning/tasks.py
@@ -254,8 +254,11 @@ def get_preprocessing_fn_from_str(self,
 
         return preprocessing_fn
 
-    def build_from_hf(self, cfg: DictConfig, max_seq_len: int,
-                      tokenizer: PreTrainedTokenizerBase) -> Union[hf_datasets.DatasetDict, hf_datasets.Dataset, hf_datasets.IterableDatasetDict, hf_datasets.IterableDataset]:
+    def build_from_hf(
+        self, cfg: DictConfig, max_seq_len: int,
+        tokenizer: PreTrainedTokenizerBase
+    ) -> Union[hf_datasets.DatasetDict, hf_datasets.Dataset,
+               hf_datasets.IterableDatasetDict, hf_datasets.IterableDataset]:
         """Load a HuggingFace Datasets, preprocess, and tokenize.
 
         Note: This function will drop examples where the prompt is longer than the max_seq_len