vLLM 是一款专为大语言模型推理加速而设计的框架，实现了 KV 缓存内存几乎零浪费，解决了内存管理瓶颈问题。
更多 vLLM 中文文档及教程可访问 →https://go.hyper.ai/Wa62f
*在线运行 vLLM 入门教程：零基础分步指南
源码 examples/offline_inference/structured_outputs.py
from enum import Enum from pydantic import BaseModel from vllm import LLM, SamplingParams from vllm.sampling_params import GuidedDecodingParams llm = LLM(model="Qwen/Qwen2.5-3B-Instruct", max_model_len=100) # 使用候选选项列表的引导式解码 guided_decoding_params = GuidedDecodingParams(choice=["Positive", "Negative"]) sampling_params = SamplingParams(guided_decoding=guided_decoding_params) outputs = llm.generate( prompts="Classify this sentiment: vLLM is wonderful!", sampling_params=sampling_params, ) print(outputs[0].outputs[0].text) # 使用 Regex 的引导式解码 guided_decoding_params = GuidedDecodingParams(regex="\w+@\w+\.com\n") sampling_params = SamplingParams(guided_decoding=guided_decoding_params, stop=["\n"]) prompt = ("Generate an email address for Alan Turing, who works in Enigma." "End in .com and new line. Example result:" "alan.turing@enigma.com\n") outputs = llm.generate(prompts=prompt, sampling_params=sampling_params) print(outputs[0].outputs[0].text) # 使用 Pydantic 模式的 JSON 引导式解码 class CarType(str, Enum): sedan = "sedan" suv = "SUV" truck = "Truck" coupe = "Coupe" class CarDescription(BaseModel): brand: str model: str car_type: CarType json_schema = CarDescription.model_json_schema() guided_decoding_params = GuidedDecodingParams(json=json_schema) sampling_params = SamplingParams(guided_decoding=guided_decoding_params) prompt = ("Generate a JSON with the brand, model and car_type of" "the most iconic car from the 90's") outputs = llm.generate( prompts=prompt, sampling_params=sampling_params, ) print(outputs[0].outputs[0].text) # 使用 Grammar 的引导式解码 simplified_sql_grammar = """ ?start: select_statement ?select_statement: "SELECT " column_list " FROM " table_name ?column_list: column_name ("," column_name)* ?table_name: identifier ?column_name: identifier ?identifier: /[a-zA-Z_][a-zA-Z0-9_]*/ """ guided_decoding_params = GuidedDecodingParams(grammar=simplified_sql_grammar) sampling_params = SamplingParams(guided_decoding=guided_decoding_params) prompt = ("Generate an SQL query to show the 'username' and 'email'" "from the 'users' table.") outputs = llm.generate( prompts=prompt, sampling_params=sampling_params, ) print(outputs[0].outputs[0].text)
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