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vLLM

vLLM 是一个快速且易于使用的大型语言模型（LLM）推理和部署库，提供：

  • 最先进的服务吞吐量
  • 使用分页注意力（PagedAttention）高效管理注意力键和值的内存。
  • 连续批量处理传入请求
  • 优化的CUDA内核

本笔记本介绍了如何使用 LangChain 和 vLLM 与大型语言模型（LLM）。

要使用，您应该已经安装了 vllm Python 包。

%pip install --upgrade --quiet  vllm -q
from langchain_community.llms import VLLM

llm = VLLM(
    model="mosaicml/mpt-7b",
    trust_remote_code=True,  # mandatory for hf models
    max_new_tokens=128,
    top_k=10,
    top_p=0.95,
    temperature=0.8,
)

print(llm.invoke("What is the capital of France ?"))
API 参考：VLLM
INFO 08-06 11:37:33 llm_engine.py:70] Initializing an LLM engine with config: model='mosaicml/mpt-7b', tokenizer='mosaicml/mpt-7b', tokenizer_mode=auto, trust_remote_code=True, dtype=torch.bfloat16, use_dummy_weights=False, download_dir=None, use_np_weights=False, tensor_parallel_size=1, seed=0)
INFO 08-06 11:37:41 llm_engine.py:196] # GPU blocks: 861, # CPU blocks: 512
``````output
Processed prompts: 100%|██████████| 1/1 [00:00<00:00,  2.00it/s]
``````output

What is the capital of France ? The capital of France is Paris.

在LLMChain中集成模型​

from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate

template = """Question: {question}

Answer: Let's think step by step."""
prompt = PromptTemplate.from_template(template)

llm_chain = LLMChain(prompt=prompt, llm=llm)

question = "Who was the US president in the year the first Pokemon game was released?"

print(llm_chain.invoke(question))
API 参考：LLMChain | PromptTemplate
Processed prompts: 100%|██████████| 1/1 [00:01<00:00,  1.34s/it]
``````output


1. The first Pokemon game was released in 1996.
2. The president was Bill Clinton.
3. Clinton was president from 1993 to 2001.
4. The answer is Clinton.

分布式推理​

vLLM支持分布式张量并行推理和部署。

要使用 LLM 类进行多 GPU 推理，请将 tensor_parallel_size 参数设置为您想要使用的 GPU 数量。例如，要在 4 块 GPU 上运行推理

from langchain_community.llms import VLLM

llm = VLLM(
    model="mosaicml/mpt-30b",
    tensor_parallel_size=4,
    trust_remote_code=True,  # mandatory for hf models
)

llm.invoke("What is the future of AI?")
API 参考：VLLM

量化​

vLLM 支持 awq 量化。要启用它，请将 quantization 传递给 vllm_kwargs。

llm_q = VLLM(
    model="TheBloke/Llama-2-7b-Chat-AWQ",
    trust_remote_code=True,
    max_new_tokens=512,
    vllm_kwargs={"quantization": "awq"},
)

与OpenAI兼容的服务器​

vLLM 可以部署为一个模拟 OpenAI API 协议的服务器。这使得 vLLM 可以作为使用 OpenAI API 的应用程序的即插即用替代品。

该服务器可以以与 OpenAI API 相同的格式进行查询。

与OpenAI兼容的完成​

from langchain_community.llms import VLLMOpenAI

llm = VLLMOpenAI(
    openai_api_key="EMPTY",
    openai_api_base="http://localhost:8000/v1",
    model_name="tiiuae/falcon-7b",
    model_kwargs={"stop": ["."]},
)
print(llm.invoke("Rome is"))
API 参考：VLLMOpenAI
 a city that is filled with history, ancient buildings, and art around every corner

LoRA适集成​

LoRA适集成可以与任何实现 SupportsLoRA 的 vLLM 模型一起使用。

from langchain_community.llms import VLLM
from vllm.lora.request import LoRARequest

llm = VLLM(
    model="meta-llama/Llama-3.2-3B-Instruct",
    max_new_tokens=300,
    top_k=1,
    top_p=0.90,
    temperature=0.1,
    vllm_kwargs={
        "gpu_memory_utilization": 0.5,
        "enable_lora": True,
        "max_model_len": 350,
    },
)
LoRA_ADAPTER_PATH = "path/to/adapter"
lora_adapter = LoRARequest("lora_adapter", 1, LoRA_ADAPTER_PATH)

print(
    llm.invoke("What are some popular Korean street foods?", lora_request=lora_adapter)
)
API 参考：VLLM