Sync from v0.13

examples/offline_inference/basic/generate.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from vllm import LLM, EngineArgs
+from vllm.utils.argparse_utils import FlexibleArgumentParser
+
+
+def create_parser():
+    parser = FlexibleArgumentParser()
+    # Add engine args
+    EngineArgs.add_cli_args(parser)
+    parser.set_defaults(model="meta-llama/Llama-3.2-1B-Instruct")
+    # Add sampling params
+    sampling_group = parser.add_argument_group("Sampling parameters")
+    sampling_group.add_argument("--max-tokens", type=int)
+    sampling_group.add_argument("--temperature", type=float)
+    sampling_group.add_argument("--top-p", type=float)
+    sampling_group.add_argument("--top-k", type=int)
+
+    return parser
+
+
+def main(args: dict):
+    # Pop arguments not used by LLM
+    max_tokens = args.pop("max_tokens")
+    temperature = args.pop("temperature")
+    top_p = args.pop("top_p")
+    top_k = args.pop("top_k")
+
+    # Create an LLM
+    llm = LLM(**args)
+
+    # Create a sampling params object
+    sampling_params = llm.get_default_sampling_params()
+    if max_tokens is not None:
+        sampling_params.max_tokens = max_tokens
+    if temperature is not None:
+        sampling_params.temperature = temperature
+    if top_p is not None:
+        sampling_params.top_p = top_p
+    if top_k is not None:
+        sampling_params.top_k = top_k
+
+    # Generate texts from the prompts. The output is a list of RequestOutput
+    # objects that contain the prompt, generated text, and other information.
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+    outputs = llm.generate(prompts, sampling_params)
+    # Print the outputs.
+    print("-" * 50)
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Prompt: {prompt!r}\nGenerated text: {generated_text!r}")
+        print("-" * 50)
+
+
+if __name__ == "__main__":
+    parser = create_parser()
+    args: dict = vars(parser.parse_args())
+    main(args)