diff --git a/examples/meter-detection/README.md b/examples/meter-detection/README.md
index e9e8f6c..8ea8e6c 100644
--- a/examples/meter-detection/README.md
+++ b/examples/meter-detection/README.md
@@ -189,8 +189,7 @@ aihub:
   output_dir: build/qai-hub/meter-detection
 ```
 
-Use the same image size configured in `sagemaker.training.hyperparameters.imgsz`. For example, a smoke-test model
-trained with `imgsz: 320` requires `images: [[1, 3, 320, 320], float32]`.
+Use the same image size configured in `sagemaker.training.hyperparameters.imgsz`. For example, a smoke-test model trained with `imgsz: 320` requires `images: [[1, 3, 320, 320], float32]`.
 
 ## 7. Prepare AI Hub Inputs
 
@@ -207,8 +206,7 @@ examples/meter-detection/data/aihub_calibration/*.npy
 examples/meter-detection/data/inputs.npz
 ```
 
-The script applies the preprocessing expected by the exported YOLO model: aspect-ratio-preserving letterboxing,
-RGB channel order, channel-first layout, and pixel values normalized to `[0, 1]`.
+The script applies the preprocessing expected by the exported YOLO model: aspect-ratio-preserving letterboxing, RGB channel order, channel-first layout, and pixel values normalized to `[0, 1]`.
 
 Set `--image-size` to the training `imgsz` value when it is not `640`.
 
@@ -223,15 +221,11 @@ qc-cli ai-hub upload \
   --from-job qc-cli-YYYYMMDD-HHMMSS
 ```
 
-The command downloads the job's `model.tar.gz`, finds `model.onnx`, uploads it to AI Hub, and runs quantization,
-compilation, validation, and profiling. The uploaded source model uses the configured
-`aihub.model_name`.
+The command downloads the job's `model.tar.gz`, finds `model.onnx`, uploads it to AI Hub, and runs quantization, compilation, validation, and profiling. The uploaded source model uses the configured `aihub.model_name`.
 
-The training example sanitizes the Ultralytics ONNX export before saving `model.onnx`. This removes graph input or
-output names, such as `output0`, that are duplicated in the ONNX `value_info` metadata and rejected by AI Hub.
+The training example sanitizes the Ultralytics ONNX export before saving `model.onnx`. This removes graph input or output names, such as `output0`, that are duplicated in the ONNX `value_info` metadata and rejected by AI Hub.
 
-For a model already downloaded by a failed upload attempt, sanitize the extracted ONNX file and retry using the local
-model. Replace the job name in both paths:
+For a model already downloaded by a failed upload attempt, sanitize the extracted ONNX file and retry using the local model. Replace the job name in both paths:
 
 ```bash
 uv run --with onnx python examples/meter-detection/source/sanitize_onnx.py \
@@ -244,8 +238,7 @@ qc-cli ai-hub upload \
   --onnx-path build/qai-hub/meter-detection/model.aihub.onnx
 ```
 
-If the meter-detection job is still the last training job in `.qc-cli.json`, `--from-job` can be omitted. Keeping it
-explicit prevents accidentally uploading an artifact from a different training run.
+If the meter-detection job is still the last training job in `.qc-cli.json`, `--from-job` can be omitted. Keeping it explicit prevents accidentally uploading an artifact from a different training run.
 
 To resume after a completed step, use one of: