基础¶

LLM 类提供了用于离线推理的主要 Python 接口，即在不使用单独模型推理服务器的情况下与模型进行交互。

使用说明¶

本示例中的第一个脚本展示了vLLM最基本的用法。如果你是Python和vLLM的新手，应该从这里开始。

python examples/offline_inference/basic/basic.py

其余脚本包含一个参数解析器，您可以用它传递与LLM兼容的任何参数。尝试运行脚本时使用--help查看所有可用参数的列表。

python examples/offline_inference/basic/classify.py

python examples/offline_inference/basic/embed.py

python examples/offline_inference/basic/score.py

聊天和生成脚本也接受采样参数：max_tokens、temperature、top_p和top_k。

python examples/offline_inference/basic/chat.py

python examples/offline_inference/basic/generate.py

功能特性¶

在支持传递参数的脚本中，您可以尝试以下功能。

默认生成配置¶

--generation-config 参数指定了调用 LLM.get_default_sampling_params() 时生成配置的加载来源。如果设置为 'auto'，生成配置将从模型路径加载。如果设置为文件夹路径，生成配置将从指定的文件夹路径加载。如果未提供该参数，将使用 vLLM 的默认设置。

如果在生成配置中指定了max_new_tokens参数，那么它将为所有请求设置服务器范围内的输出令牌数量限制。

尝试使用以下参数自行体验：

--generation-config auto

量化¶

AQLM¶

vLLM支持使用AQLM量化的模型。

您可以通过将以下任一模型传递给--model参数来亲自尝试：

ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf
ISTA-DASLab/Llama-2-7b-AQLM-2Bit-2x8-hf
ISTA-DASLab/Llama-2-13b-AQLM-2Bit-1x16-hf
ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf
BlackSamorez/TinyLlama-1_1B-Chat-v1_0-AQLM-2Bit-1x16-hf

其中一些模型可能对于单个GPU来说过大。您可以通过设置--tensor-parallel-size参数为所需GPU数量，将模型拆分到多个GPU上运行。

GGUF¶

vLLM支持使用GGUF量化的模型。

您可以自行尝试，下载一个GUFF量化模型并使用以下参数：

from huggingface_hub import hf_hub_download
repo_id = "bartowski/Phi-3-medium-4k-instruct-GGUF"
filename = "Phi-3-medium-4k-instruct-IQ2_M.gguf"
print(hf_hub_download(repo_id, filename=filename))

--model {local-path-printed-above} --tokenizer microsoft/Phi-3-medium-4k-instruct

CPU卸载¶

--cpu-offload-gb参数可视为一种虚拟扩展GPU显存的方式。例如，若您拥有一块24GB显存的GPU并将该参数设为10，从虚拟角度可以将其视为34GB显存的GPU。这样您就能加载需要至少26GB显存的13B BF16权重模型。请注意，这需要高速的CPU-GPU互连，因为在每次模型前向传播时，部分模型权重会从CPU内存动态加载至GPU内存。

尝试使用以下参数自行体验：

--model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10

示例材料¶

basic.py

# SPDX-License-Identifier: Apache-2.0

from vllm import LLM, SamplingParams

# Sample prompts.
prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)


def main():
    # Create an LLM.
    llm = LLM(model="facebook/opt-125m")
    # Generate texts from the prompts.
    # The output is a list of RequestOutput objects
    # that contain the prompt, generated text, and other information.
    outputs = llm.generate(prompts, sampling_params)
    # Print the outputs.
    print("\nGenerated Outputs:\n" + "-" * 60)
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt:    {prompt!r}")
        print(f"Output:    {generated_text!r}")
        print("-" * 60)


if __name__ == "__main__":
    main()

chat.py

# SPDX-License-Identifier: Apache-2.0

from vllm import LLM, EngineArgs
from vllm.utils import FlexibleArgumentParser


def create_parser():
    parser = FlexibleArgumentParser()
    # Add engine args
    EngineArgs.add_cli_args(parser)
    parser.set_defaults(model="meta-llama/Llama-3.2-1B-Instruct")
    # Add sampling params
    sampling_group = parser.add_argument_group("Sampling parameters")
    sampling_group.add_argument("--max-tokens", type=int)
    sampling_group.add_argument("--temperature", type=float)
    sampling_group.add_argument("--top-p", type=float)
    sampling_group.add_argument("--top-k", type=int)
    # Add example params
    parser.add_argument("--chat-template-path", type=str)

    return parser


def main(args: dict):
    # Pop arguments not used by LLM
    max_tokens = args.pop("max_tokens")
    temperature = args.pop("temperature")
    top_p = args.pop("top_p")
    top_k = args.pop("top_k")
    chat_template_path = args.pop("chat_template_path")

    # Create an LLM
    llm = LLM(**args)

    # Create sampling params object
    sampling_params = llm.get_default_sampling_params()
    if max_tokens is not None:
        sampling_params.max_tokens = max_tokens
    if temperature is not None:
        sampling_params.temperature = temperature
    if top_p is not None:
        sampling_params.top_p = top_p
    if top_k is not None:
        sampling_params.top_k = top_k

    def print_outputs(outputs):
        print("\nGenerated Outputs:\n" + "-" * 80)
        for output in outputs:
            prompt = output.prompt
            generated_text = output.outputs[0].text
            print(f"Prompt: {prompt!r}\n")
            print(f"Generated text: {generated_text!r}")
            print("-" * 80)

    print("=" * 80)

    # In this script, we demonstrate how to pass input to the chat method:
    conversation = [
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "Hello"},
        {"role": "assistant", "content": "Hello! How can I assist you today?"},
        {
            "role": "user",
            "content": "Write an essay about the importance of higher education.",
        },
    ]
    outputs = llm.chat(conversation, sampling_params, use_tqdm=False)
    print_outputs(outputs)

    # You can run batch inference with llm.chat API
    conversations = [conversation for _ in range(10)]

    # We turn on tqdm progress bar to verify it's indeed running batch inference
    outputs = llm.chat(conversations, sampling_params, use_tqdm=True)
    print_outputs(outputs)

    # A chat template can be optionally supplied.
    # If not, the model will use its default chat template.
    if chat_template_path is not None:
        with open(chat_template_path) as f:
            chat_template = f.read()

        outputs = llm.chat(
            conversations,
            sampling_params,
            use_tqdm=False,
            chat_template=chat_template,
        )


if __name__ == "__main__":
    parser = create_parser()
    args: dict = vars(parser.parse_args())
    main(args)

classify.py

# SPDX-License-Identifier: Apache-2.0

from argparse import Namespace

from vllm import LLM, EngineArgs
from vllm.utils import FlexibleArgumentParser


def parse_args():
    parser = FlexibleArgumentParser()
    parser = EngineArgs.add_cli_args(parser)
    # Set example specific arguments
    parser.set_defaults(
        model="jason9693/Qwen2.5-1.5B-apeach", task="classify", enforce_eager=True
    )
    return parser.parse_args()


def main(args: Namespace):
    # Sample prompts.
    prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
    ]

    # Create an LLM.
    # You should pass task="classify" for classification models
    model = LLM(**vars(args))

    # Generate logits. The output is a list of ClassificationRequestOutputs.
    outputs = model.classify(prompts)

    # Print the outputs.
    print("\nGenerated Outputs:\n" + "-" * 60)
    for prompt, output in zip(prompts, outputs):
        probs = output.outputs.probs
        probs_trimmed = (str(probs[:16])[:-1] + ", ...]") if len(probs) > 16 else probs
        print(
            f"Prompt: {prompt!r} \n"
            f"Class Probabilities: {probs_trimmed} (size={len(probs)})"
        )
        print("-" * 60)


if __name__ == "__main__":
    args = parse_args()
    main(args)

embed.py

# SPDX-License-Identifier: Apache-2.0

from argparse import Namespace

from vllm import LLM, EngineArgs
from vllm.utils import FlexibleArgumentParser


def parse_args():
    parser = FlexibleArgumentParser()
    parser = EngineArgs.add_cli_args(parser)
    # Set example specific arguments
    parser.set_defaults(
        model="intfloat/e5-mistral-7b-instruct", task="embed", enforce_eager=True
    )
    return parser.parse_args()


def main(args: Namespace):
    # Sample prompts.
    prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
    ]

    # Create an LLM.
    # You should pass task="embed" for embedding models
    model = LLM(**vars(args))

    # Generate embedding. The output is a list of EmbeddingRequestOutputs.
    outputs = model.embed(prompts)

    # Print the outputs.
    print("\nGenerated Outputs:\n" + "-" * 60)
    for prompt, output in zip(prompts, outputs):
        embeds = output.outputs.embedding
        embeds_trimmed = (
            (str(embeds[:16])[:-1] + ", ...]") if len(embeds) > 16 else embeds
        )
        print(f"Prompt: {prompt!r} \nEmbeddings: {embeds_trimmed} (size={len(embeds)})")
        print("-" * 60)


if __name__ == "__main__":
    args = parse_args()
    main(args)

generate.py

# SPDX-License-Identifier: Apache-2.0

from vllm import LLM, EngineArgs
from vllm.utils import FlexibleArgumentParser


def create_parser():
    parser = FlexibleArgumentParser()
    # Add engine args
    EngineArgs.add_cli_args(parser)
    parser.set_defaults(model="meta-llama/Llama-3.2-1B-Instruct")
    # Add sampling params
    sampling_group = parser.add_argument_group("Sampling parameters")
    sampling_group.add_argument("--max-tokens", type=int)
    sampling_group.add_argument("--temperature", type=float)
    sampling_group.add_argument("--top-p", type=float)
    sampling_group.add_argument("--top-k", type=int)

    return parser


def main(args: dict):
    # Pop arguments not used by LLM
    max_tokens = args.pop("max_tokens")
    temperature = args.pop("temperature")
    top_p = args.pop("top_p")
    top_k = args.pop("top_k")

    # Create an LLM
    llm = LLM(**args)

    # Create a sampling params object
    sampling_params = llm.get_default_sampling_params()
    if max_tokens is not None:
        sampling_params.max_tokens = max_tokens
    if temperature is not None:
        sampling_params.temperature = temperature
    if top_p is not None:
        sampling_params.top_p = top_p
    if top_k is not None:
        sampling_params.top_k = top_k

    # Generate texts from the prompts. The output is a list of RequestOutput
    # objects that contain the prompt, generated text, and other information.
    prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
    ]
    outputs = llm.generate(prompts, sampling_params)
    # Print the outputs.
    print("-" * 50)
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt: {prompt!r}\nGenerated text: {generated_text!r}")
        print("-" * 50)


if __name__ == "__main__":
    parser = create_parser()
    args: dict = vars(parser.parse_args())
    main(args)

score.py

# SPDX-License-Identifier: Apache-2.0

from argparse import Namespace

from vllm import LLM, EngineArgs
from vllm.utils import FlexibleArgumentParser


def parse_args():
    parser = FlexibleArgumentParser()
    parser = EngineArgs.add_cli_args(parser)
    # Set example specific arguments
    parser.set_defaults(
        model="BAAI/bge-reranker-v2-m3", task="score", enforce_eager=True
    )
    return parser.parse_args()


def main(args: Namespace):
    # Sample prompts.
    text_1 = "What is the capital of France?"
    texts_2 = [
        "The capital of Brazil is Brasilia.",
        "The capital of France is Paris.",
    ]

    # Create an LLM.
    # You should pass task="score" for cross-encoder models
    model = LLM(**vars(args))

    # Generate scores. The output is a list of ScoringRequestOutputs.
    outputs = model.score(text_1, texts_2)

    # Print the outputs.
    print("\nGenerated Outputs:\n" + "-" * 60)
    for text_2, output in zip(texts_2, outputs):
        score = output.outputs.score
        print(f"Pair: {[text_1, text_2]!r} \nScore: {score}")
        print("-" * 60)


if __name__ == "__main__":
    args = parse_args()
    main(args)