diff --git a/examples/offline_inference/context_extension.py b/examples/offline_inference/context_extension.py
index 8d7666418559f..df39e4c25d5c8 100644
--- a/examples/offline_inference/context_extension.py
+++ b/examples/offline_inference/context_extension.py
@@ -1,37 +1,51 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+This script demonstrates how to extend the context length
+of a Qwen model using the YARN method (rope_scaling)
+and run a simple chat example.
+
+Usage:
+    python examples/offline_inference/context_extension.py
+"""
 
 from vllm import LLM, SamplingParams
 
-rope_theta = 1000000
-original_max_position_embeddings = 32768
-factor = 4.0
 
-# Use yarn to extend context
-hf_overrides = {
-    "rope_theta": rope_theta,
-    "rope_scaling": {
-        "rope_type": "yarn",
-        "factor": factor,
-        "original_max_position_embeddings": original_max_position_embeddings,
-    },
-    "max_model_len": int(original_max_position_embeddings * factor),
-}
+def create_llm():
+    rope_theta = 1000000
+    original_max_position_embeddings = 32768
+    factor = 4.0
 
-llm = LLM(model="Qwen/Qwen3-0.6B", hf_overrides=hf_overrides)
+    # Use yarn to extend context
+    hf_overrides = {
+        "rope_theta": rope_theta,
+        "rope_scaling": {
+            "rope_type": "yarn",
+            "factor": factor,
+            "original_max_position_embeddings": original_max_position_embeddings,
+        },
+        "max_model_len": int(original_max_position_embeddings * factor),
+    }
 
-sampling_params = SamplingParams(
-    temperature=0.8,
-    top_p=0.95,
-    max_tokens=128,
-)
+    llm = LLM(model="Qwen/Qwen3-0.6B", hf_overrides=hf_overrides)
+    return llm
 
-conversation = [
-    {"role": "system", "content": "You are a helpful assistant"},
-    {"role": "user", "content": "Hello"},
-    {"role": "assistant", "content": "Hello! How can I assist you today?"},
-]
-outputs = llm.chat(conversation, sampling_params, use_tqdm=False)
+
+def run_llm_chat(llm):
+    sampling_params = SamplingParams(
+        temperature=0.8,
+        top_p=0.95,
+        max_tokens=128,
+    )
+
+    conversation = [
+        {"role": "system", "content": "You are a helpful assistant"},
+        {"role": "user", "content": "Hello"},
+        {"role": "assistant", "content": "Hello! How can I assist you today?"},
+    ]
+    outputs = llm.chat(conversation, sampling_params, use_tqdm=False)
+    return outputs
 
 
 def print_outputs(outputs):
@@ -44,4 +58,11 @@ def print_outputs(outputs):
         print("-" * 80)
 
 
-print_outputs(outputs)
+def main():
+    llm = create_llm()
+    outputs = run_llm_chat(llm)
+    print_outputs(outputs)
+
+
+if __name__ == "__main__":
+    main()