feat: enhance serving functionality and add comprehensive examples

- Improve API server with better error handling and response formatting
- Enhance CLI with additional options for DiT stages and configuration
- Add comprehensive examples in examples/basic/ including:
  - API client with health checks and text generation
  - Docker setup and usage examples
  - Simple usage patterns for different scenarios
- Add utility scripts for model downloading and Docker setup
- Update documentation with implementation details and testing guidelines
- Fix configuration validation issues in OmniLLM
- Improve stage configuration handling for AR and DiT stages
- Add proper error handling and fallback mechanisms

Tested with Qwen3-0.6B model:
- Server starts successfully on port 8000
- Health and info endpoints working correctly
- Text generation with various parameters functioning
- API client examples working as expected
- CLI help and configuration options working properly

examples/basic/simple_usage.py

@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+"""
+Simple vLLM-omni usage example.
+
+This example demonstrates how to use vLLM-omni for basic text generation
+with a single AR (Autoregressive) stage.
+"""
+
+import asyncio
+from vllm_omni.core.omni_llm import OmniLLM, AsyncOmniLLM
+from vllm_omni.config import create_ar_stage_config
+
+
+def sync_example():
+    """Synchronous usage example."""
+    print("=== Synchronous vLLM-omni Example ===")
+    
+    # Create a simple AR stage configuration
+    stage_config = create_ar_stage_config(
+        stage_id=0,
+        model_path="Qwen/Qwen3-0.6B",
+        input_modalities=["text"],
+        output_modalities=["text"]
+    )
+    
+    # Initialize OmniLLM
+    omni_llm = OmniLLM([stage_config])
+    
+    # Prepare stage arguments
+    stage_args = [{
+        "prompt": "Hello, how are you today?",
+        "max_tokens": 50,
+        "temperature": 0.7
+    }]
+    
+    # Generate text
+    print(f"Input: {stage_args[0]['prompt']}")
+    outputs = omni_llm.generate(stage_args)
+    
+    # Display results
+    for i, output in enumerate(outputs):
+        print(f"Output {i}:")
+        if hasattr(output, 'outputs') and output.outputs:
+            for completion in output.outputs:
+                print(f"  Text: {completion.text}")
+                print(f"  Finished: {completion.finish_reason != 'length'}")
+                print(f"  Tokens: {len(completion.token_ids)} tokens")
+
+
+async def async_example():
+    """Asynchronous usage example."""
+    print("\n=== Asynchronous vLLM-omni Example ===")
+    
+    # Create a simple AR stage configuration
+    stage_config = create_ar_stage_config(
+        stage_id=0,
+        model_path="Qwen/Qwen3-0.6B",
+        input_modalities=["text"],
+        output_modalities=["text"]
+    )
+    
+    # Initialize AsyncOmniLLM
+    omni_llm = AsyncOmniLLM([stage_config])
+    
+    # Prepare stage arguments
+    stage_args = [{
+        "prompt": "What is artificial intelligence?",
+        "max_tokens": 100,
+        "temperature": 0.8
+    }]
+    
+    # Generate text asynchronously
+    print(f"Input: {stage_args[0]['prompt']}")
+    outputs = await omni_llm.generate_async(stage_args)
+    
+    # Display results
+    for i, output in enumerate(outputs):
+        print(f"Output {i}:")
+        if hasattr(output, 'outputs') and output.outputs:
+            for completion in output.outputs:
+                print(f"  Text: {completion.text}")
+                print(f"  Finished: {completion.finish_reason != 'length'}")
+                print(f"  Tokens: {len(completion.token_ids)} tokens")
+
+
+def multi_prompt_example():
+    """Example with multiple prompts."""
+    print("\n=== Multi-Prompt Example ===")
+    
+    # Create stage configuration
+    stage_config = create_ar_stage_config(
+        stage_id=0,
+        model_path="Qwen/Qwen3-0.6B",
+        input_modalities=["text"],
+        output_modalities=["text"]
+    )
+    
+    # Initialize OmniLLM
+    omni_llm = OmniLLM([stage_config])
+    
+    # Multiple prompts
+    prompts = [
+        "Tell me a joke",
+        "What's the weather like?",
+        "Explain quantum computing"
+    ]
+    
+    for prompt in prompts:
+        print(f"\nInput: {prompt}")
+        stage_args = [{
+            "prompt": prompt,
+            "max_tokens": 30,
+            "temperature": 0.9
+        }]
+        
+        outputs = omni_llm.generate(stage_args)
+        
+        if outputs and hasattr(outputs[0], 'outputs') and outputs[0].outputs:
+            completion = outputs[0].outputs[0]
+            print(f"Output: {completion.text}")
+
+
+if __name__ == "__main__":
+    print("vLLM-omni Simple Usage Examples")
+    print("=" * 40)
+    
+    # Run synchronous example
+    sync_example()
+    
+    # Run asynchronous example
+    asyncio.run(async_example())
+    
+    # Run multi-prompt example
+    multi_prompt_example()
+    
+    print("\n" + "=" * 40)
+    print("Examples completed!")