forked from EngineX-Cambricon/enginex-mlu370-vllm
26 lines
1016 B
Python
26 lines
1016 B
Python
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import os
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os.environ['CONTEXT_PARALLEL_EN'] = "True"
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from vllm import LLM, SamplingParams
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if __name__ == '__main__':
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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, max_tokens=16)
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# Create an LLM.
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llm = LLM(model="/data/AE/llm/models/Llama-2-7b-hf/", enforce_eager=True, tensor_parallel_size = 2, context_parallel_size = 2, distributed_executor_backend='ray')
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# Generate texts from the prompts. The output is a list of RequestOutput objects
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# that contain the prompt, generated text, and other information.
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outputs = llm.generate(prompts, sampling_params)
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# Print the outputs.
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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}, Generated text: {generated_text!r}")
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