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64 lines
2.9 KiB
64 lines
2.9 KiB
import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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import time
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from vllm import LLM, SamplingParams
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# prompts = [
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# "生成论文小标题内容#\n问：论文题目是《我国基层税务机关的税务风险管理研究——以Z市为例》，目录是“ 一、绪论\n1.1 研究背景和意义\n1.2 国内外相关研究综述\n1.3 研究内容和方法\n\n二、Z市税务风险管理现状分析\n2.1 Z市基层税务机关概况\n2.2 Z市税务风险管理现状\n2.3 Z市税务风险管理存在的问题\n\n三、Z市税务风险管理优化策略\n3.1 完善税务风险管理制度\n3.2 加强税务风险管理人员培训\n3.3 建立健全税务风险管理评估体系\n\n四、Z市税务风险管理效果评价\n4.1 数据收集和整理\n4.2 评价指标体系构建\n4.3 评价结果分析\n\n五、Z市税务风险管理改进建议\n5.1 改进策略和措施\n5.2 实施计划和步骤\n5.3 预期效果和成效评价\n\n六、结论与展望\n6.1 研究结论\n6.2 研究不足和展望\n”，请把其中的小标题“1.1 研究背景和意义”的内容补充完整，补充内容字数在1295字左右\n答：\n"
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# ] * 50
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# prompts = [
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# "改写句子#\n问：改写这句话“它的外在影响因素是由环境和载荷功能影响的。”答：\n",
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# "改写句子#\n问：改写这句话“它的外在影响因素是由环境和载荷功能影响的。”答：\n"
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# ] * 100
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prompts = []
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with open("data/测试.txt") as f:
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data = f.readlines()
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for i in data:
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data_dan = i.strip("\n")
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if 10 < len(data_dan) < 250 :
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prompts.append("改写句子#\n问：改写这句话“" + data_dan + "”答：\n")
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print(prompts)
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sampling_params = SamplingParams(temperature=0.95, top_p=0.7,presence_penalty=0.9,stop="</s>", max_tokens=2048)
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models_path = "/home/majiahui/project/models-llm/openbuddy-llama-7b-finetune"
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llm = LLM(model=models_path, tokenizer_mode="slow")
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t1 = time.time()
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outputs = llm.generate(prompts, sampling_params)
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# Print the outputs.
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zishu = 0
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# t2 = time.time()
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generated_text_list = [i for i in prompts]
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# for i, output in enumerate(outputs):
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# index = output.request_id
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# generated_text = output.outputs[0].text
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# generated_text_list[int(index)] = generated_text
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for i,output in enumerate(outputs):
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generated_text = output.outputs[0].text
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index = output.request_id
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zishu += len(generated_text)
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generated_text_list[int(index)] += "\n==============================\n" + generated_text
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for i in generated_text_list:
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print(i)
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print("\n@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n")
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t2 = time.time()
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time_cost = t2-t1
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print(time_cost)
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print("speed", zishu/time_cost)
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#
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zishu_one = zishu/time_cost
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print(f"speed: {zishu_one} tokens/s")
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# # from vllm import LLM
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# #
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# # llm = LLM(model="/home/majiahui/models-LLM/openbuddy-llama-7b-v1.4-fp16") # Name or path of your model
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# # output = llm.generate("Hello, my name is")
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# # print(output)
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