Implementation:LLMBook zh LLMBook zh github io VLLM LLM Generate

Knowledge Sources	LLMBook-zh vLLM Documentation
Domains	NLP, Inference, Systems
Last Updated	2026-02-08 00:00 GMT

Overview

Concrete tool for high-throughput LLM inference using vLLM's batch generation engine, as used in the LLMBook repository.

Description

vllm.LLM initializes the inference engine, and LLM.generate performs batch generation with configurable SamplingParams. The repository demonstrates using vLLM with LLaMA-2 Chat format prompts, greedy decoding (temperature=0), and a 2048-token maximum output.

This is a Wrapper Doc documenting how the LLMBook repository uses the vLLM library.

Usage

Use this for batch inference with LLMs when you need high throughput and efficient memory management.

Code Reference

Source Location

Repository: LLMBook-zh
File: code/9.1 vLLM实践.py
Lines: 1-24

Signature

# Initialize vLLM engine
model = vllm.LLM(model: str)

# Configure sampling
sampling_params = vllm.SamplingParams(
    temperature: float = 0,
    max_tokens: int = 2048,
    presence_penalty: float = 0,
    frequency_penalty: float = 0,
)

# Generate
outputs = model.generate(
    prompts: list[str],
    sampling_params: SamplingParams
) -> list[RequestOutput]

Import

import vllm

External Reference

vLLM Documentation

I/O Contract

Inputs

Name	Type	Required	Description
model	str	Yes	HuggingFace model ID (e.g., "meta-llama/Llama-2-7b-chat-hf")
prompts	list[str]	Yes	List of prompt strings
sampling_params	SamplingParams	Yes	Decoding configuration

Outputs

Name	Type	Description
outputs	list[RequestOutput]	Generated outputs; text via output.outputs[0].text

Usage Examples

import vllm

# Initialize
model = vllm.LLM(model="meta-llama/Llama-2-7b-chat-hf")

# Configure greedy decoding
sampling_params = vllm.SamplingParams(
    temperature=0,
    max_tokens=2048,
)

# Generate
prompts = [
    "[INST] How are you? [/INST]",
    "[INST] 1 + 1 = ? [/INST]",
]
outputs = model.generate(prompts, sampling_params=sampling_params)

for prompt, output in zip(prompts, outputs):
    print(f"Input: {prompt}")
    print(f"Output: {output.outputs[0].text}")

Related Pages

Implements Principle

Principle:LLMBook_zh_LLMBook_zh_github_io_High_Throughput_LLM_Inference

Requires Environment

Uses Heuristic

Heuristic:LLMBook_zh_LLMBook_zh_github_io_Greedy_Decoding_Temperature_Zero

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Principle

Implementation

Heuristic

Environment