diff --git a/examples/offline_inference/neuron_eagle.py b/examples/offline_inference/neuron_eagle.py
index a51caa2aec8b..5d7fb819d347 100644
--- a/examples/offline_inference/neuron_eagle.py
+++ b/examples/offline_inference/neuron_eagle.py
@@ -15,40 +15,46 @@ prompts = [
     "What is annapurna labs?",
 ]
 
-# Create a sampling params object.
-sampling_params = SamplingParams(top_k=1, max_tokens=500, ignore_eos=True)
 
-# Create an LLM.
-llm = LLM(
-    model="/home/ubuntu/model_hf/Meta-Llama-3.1-70B-Instruct",
-    speculative_config={
-        "model": "/home/ubuntu/model_hf/Llama-3.1-70B-Instruct-EAGLE-Draft",
-        "num_speculative_tokens": 5,
-        "max_model_len": 2048,
-    },
-    max_num_seqs=4,
-    # The max_model_len and block_size arguments are required to be same as
-    # max sequence length when targeting neuron device.
-    # Currently, this is a known limitation in continuous batching support
-    # in neuronx-distributed-inference.
-    max_model_len=2048,
-    block_size=2048,
-    # The device can be automatically detected when AWS Neuron SDK is installed.
-    # The device argument can be either unspecified for automated detection,
-    # or explicitly assigned.
-    device="neuron",
-    tensor_parallel_size=32,
-    override_neuron_config={
-        "enable_eagle_speculation": True,
-        "enable_fused_speculation": True,
-    },
-)
+def main():
+    # Create a sampling params object.
+    sampling_params = SamplingParams(top_k=1, max_tokens=500, ignore_eos=True)
 
-# Generate texts from the prompts. The output is a list of RequestOutput objects
-# that contain the prompt, generated text, and other information.
-outputs = llm.generate(prompts, sampling_params)
-# Print the outputs.
-for output in outputs:
-    prompt = output.prompt
-    generated_text = output.outputs[0].text
-    print(f"Prompt: {prompt!r}, \n\n\n\ Generated text: {generated_text!r}")
+    # Create an LLM.
+    llm = LLM(
+        model="/home/ubuntu/model_hf/Meta-Llama-3.1-70B-Instruct",
+        speculative_config={
+            "model": "/home/ubuntu/model_hf/Llama-3.1-70B-Instruct-EAGLE-Draft",
+            "num_speculative_tokens": 5,
+            "max_model_len": 2048,
+        },
+        max_num_seqs=4,
+        # The max_model_len and block_size arguments are required to be same as
+        # max sequence length when targeting neuron device.
+        # Currently, this is a known limitation in continuous batching support
+        # in neuronx-distributed-inference.
+        max_model_len=2048,
+        block_size=2048,
+        # The device can be automatically detected when AWS Neuron SDK is installed.
+        # The device argument can be either unspecified for automated detection,
+        # or explicitly assigned.
+        device="neuron",
+        tensor_parallel_size=32,
+        override_neuron_config={
+            "enable_eagle_speculation": True,
+            "enable_fused_speculation": True,
+        },
+    )
+
+    # Generate texts from the prompts. The output is a list of RequestOutput objects
+    # that contain the prompt, generated text, and other information.
+    outputs = llm.generate(prompts, sampling_params)
+    # Print the outputs.
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Prompt: {prompt!r}, \n\n\n\ Generated text: {generated_text!r}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/offline_inference/qwen2_5_omni/README.md b/examples/offline_inference/qwen2_5_omni/README.md
index c30541a598ce..16d44cbadbc9 100644
--- a/examples/offline_inference/qwen2_5_omni/README.md
+++ b/examples/offline_inference/qwen2_5_omni/README.md
@@ -6,14 +6,19 @@ This folder provides several example scripts on how to inference Qwen2.5-Omni of
 
 ```bash
 # Audio + image + video
-python examples/offline_inference/qwen2_5_omni/only_thinker.py -q mixed_modalities
+python examples/offline_inference/qwen2_5_omni/only_thinker.py \
+    -q mixed_modalities
 
 # Read vision and audio inputs from a single video file
 # NOTE: V1 engine does not support interleaved modalities yet.
-VLLM_USE_V1=0 python examples/offline_inference/qwen2_5_omni/only_thinker.py -q use_audio_in_video
+VLLM_USE_V1=0 \
+python examples/offline_inference/qwen2_5_omni/only_thinker.py \
+    -q use_audio_in_video
 
 # Multiple audios
-VLLM_USE_V1=0 python examples/offline_inference/qwen2_5_omni/only_thinker.py -q multi_audios
+VLLM_USE_V1=0 \
+python examples/offline_inference/qwen2_5_omni/only_thinker.py \
+    -q multi_audios
 ```
 
 This script will run the thinker part of Qwen2.5-Omni, and generate text response.
@@ -22,11 +27,16 @@ You can also test Qwen2.5-Omni on a single modality:
 
 ```bash
 # Process audio inputs
-python examples/offline_inference/audio_language.py --model-type qwen2_5_omni
+python examples/offline_inference/audio_language.py \
+    --model-type qwen2_5_omni
 
 # Process image inputs
-python examples/offline_inference/vision_language.py --modality image --model-type qwen2_5_omni
+python examples/offline_inference/vision_language.py \
+    --modality image \
+    --model-type qwen2_5_omni
 
 # Process video inputs
-python examples/offline_inference/vision_language.py --modality video --model-type qwen2_5_omni
+python examples/offline_inference/vision_language.py \
+    --modality video \
+    --model-type qwen2_5_omni
 ```