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https://github.com/vllm-project/vllm.git
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54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from vllm import LLM, RequestOutput, SamplingParams
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# Sample prompts.
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prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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]
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# Create a sampling params object.
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
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def print_prompts_and_outputs(outputs: list[RequestOutput]) -> None:
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print("-" * 60)
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}")
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print(f"Output: {generated_text!r}")
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print("-" * 60)
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def main():
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# Create an LLM without loading real weights
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llm = LLM(
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model="Qwen/Qwen3-0.6B",
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load_format="dummy",
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enforce_eager=True,
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tensor_parallel_size=4,
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)
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outputs = llm.generate(prompts, sampling_params)
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print("\nOutputs do not make sense:")
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print_prompts_and_outputs(outputs)
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# Update load format from `dummy` to `auto`
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llm.collective_rpc(
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"update_config", args=({"load_config": {"load_format": "auto"}},)
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)
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# Now reload real weights inplace
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llm.collective_rpc("reload_weights")
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# Check outputs make sense
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outputs = llm.generate(prompts, sampling_params)
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print("\nOutputs make sense after loading real weights:")
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print_prompts_and_outputs(outputs)
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if __name__ == "__main__":
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main()
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