pre-commit formatting

Signed-off-by: Michael Clifford <[email protected]>

README.md

@@ -415,6 +415,7 @@ run_training(
     train_args=training_args
 )
 ```
+
 If the machine's above have shared storage, users can preprocess the training dataset a single time so that it can then distributed to each machine with the following update:
 
 ```python
@@ -426,7 +427,7 @@ from instructlab.training import (
     DataProcessArgs,
     data_process as dp
 )
-
+ 
 ...
 
 data_process_args = DataProcessArgs(

src/instructlab/training/__init__.py

@@ -28,9 +28,13 @@
 
 
 # defer import of main_ds
-def run_training(torch_args: TorchrunArgs, train_args: TrainingArgs, process_data: bool = True) -> None:
+def run_training(
+    torch_args: TorchrunArgs, train_args: TrainingArgs, process_data: bool = True
+) -> None:
     """Wrapper around the main training job that calls torchrun."""
     # Local
     from .main_ds import run_training
 
-    return run_training(torch_args=torch_args, train_args=train_args, process_data=process_data)
+    return run_training(
+        torch_args=torch_args, train_args=train_args, process_data=process_data
+    )

src/instructlab/training/data_process.py

@@ -221,10 +221,8 @@ def get_masked_and_orig_text(sample):
 
 
 def main(args: DataProcessArgs):
-
     if not os.path.exists(args.data_output_path):
         os.makedirs(args.data_output_path, exist_ok=True)
-
     print("\033[92m data arguments are:\033[0m")
     print("\033[36m" + args.model_dump_json() + "\033[0m")
     NUM_PROC = args.num_cpu_procs

src/instructlab/training/main_ds.py

@@ -618,7 +618,9 @@ def main(args):
 
 
 # public API
-def run_training(torch_args: TorchrunArgs, train_args: TrainingArgs, process_data: bool = True) -> None:
+def run_training(
+    torch_args: TorchrunArgs, train_args: TrainingArgs, process_data: bool = True
+) -> None:
     """
     Wrapper around the main training job that calls torchrun.
     """
@@ -627,7 +629,7 @@ def run_training(torch_args: TorchrunArgs, train_args: TrainingArgs, process_dat
         raise ValueError(
             f"the `max_batch_len` cannot be less than `max_seq_len`: {train_args.max_batch_len=} < {train_args.max_seq_len=}"
         )
-    
+
     if process_data:
         dp.main(
             DataProcessArgs(