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60 lines
1.6 KiB
Python
60 lines
1.6 KiB
Python
import argparse
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from vllm import LLM, SamplingParams
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parser = argparse.ArgumentParser()
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parser.add_argument("--model_path", type=str, default="")
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args = parser.parse_args()
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# Sample prompts.
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prompts = [
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"北京烤鸭真好吃，正好有一家北京烤鸭店，我想去吃北京烤鸭",
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"def reverse_list(list):",
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"Beijing is the capital of",
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"1 + 1 = ",
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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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'''0123456789'''*810 + " what is next number?",
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"您好，三加二等于多少？",
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"我是",
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""
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]
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params_dict = {
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"n": 1,
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"best_of": None,
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"presence_penalty": 0.0,
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"frequency_penalty": 0.0,
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"temperature": 1,
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"top_p": 0.9,
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"top_k": -1,
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"use_beam_search": False,
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"length_penalty": 1.0,
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"early_stopping": False,
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"stop": None,
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"stop_token_ids": None,
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"ignore_eos": False,
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"max_tokens": 100,
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"logprobs": None,
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"prompt_logprobs": None,
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"skip_special_tokens": True,
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}
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# Create a sampling params object.
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sampling_params = SamplingParams(**params_dict)
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# Create an LLM.
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llm = LLM(model=args.model_path, tensor_parallel_size=1, dtype='bfloat16')
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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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