feat: Add vLLM NPU offline inference demo script.

demo.py

@@ -0,0 +1,69 @@
+"""
+Quick offline inference demo — no server needed.
+
+Usage:
+    python demo.py
+    python demo.py --model /path/to/model
+    python demo.py --prompt "What is AI?"
+"""
+
+import argparse
+
+from vllm import LLM, SamplingParams
+
+
+def main():
+    parser = argparse.ArgumentParser(description="vLLM NPU offline demo")
+    parser.add_argument(
+        "--model",
+        default="/workspace/mnt/vllm_ascend/Qwen2.5-7B-Instruct",
+        help="Path to the model",
+    )
+    parser.add_argument(
+        "--prompt",
+        default="你好，请简单介绍一下自己",
+        help="User prompt",
+    )
+    parser.add_argument("--max-tokens", type=int, default=128)
+    parser.add_argument("--max-model-len", type=int, default=512)
+    parser.add_argument("--dtype", default="float16")
+    parser.add_argument("--block-size", type=int, default=128)
+    args = parser.parse_args()
+
+    print(f"Loading model: {args.model}")
+    llm = LLM(
+        model=args.model,
+        dtype=args.dtype,
+        max_model_len=args.max_model_len,
+        block_size=args.block_size,
+        trust_remote_code=True,
+        enforce_eager=True,  # skip graph capture for debugging
+    )
+
+    # Build chat-format prompt for Qwen2.5
+    messages = [{"role": "user", "content": args.prompt}]
+    tokenizer = llm.get_tokenizer()
+    prompt = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+
+    sampling_params = SamplingParams(
+        temperature=0.7,
+        top_p=0.9,
+        max_tokens=args.max_tokens,
+    )
+
+    print(f"\nPrompt: {args.prompt}")
+    print("-" * 60)
+
+    outputs = llm.generate([prompt], sampling_params)
+
+    for output in outputs:
+        generated = output.outputs[0].text
+        print(f"Response:\n{generated}")
+        print("-" * 60)
+        print(f"Tokens generated: {len(output.outputs[0].token_ids)}")
+
+
+if __name__ == "__main__":
+    main()