vLLM Integration Guide

Overview

vLLM is a high-performance and easy-to-use library for LLM inference and serving. This guide explains how to integrate vLLM with Swarms for efficient, production-grade language model deployment.

Installation

Prerequisites

Before you begin, make sure you have Python 3.8+ installed on your system.

pippoetry

pip install -U vllm swarms

poetry add vllm swarms

Basic Usage

Here's a simple example of how to use vLLM with Swarms:

basic_usage.py

from swarms.utils.vllm_wrapper import VLLMWrapper

# Initialize the vLLM wrapper
vllm = VLLMWrapper(
    model_name="meta-llama/Llama-2-7b-chat-hf",
    system_prompt="You are a helpful assistant.",
    temperature=0.7,
    max_tokens=4000
)

# Run inference
response = vllm.run("What is the capital of France?")
print(response)

VLLMWrapper Class

Class Overview

The VLLMWrapper class provides a convenient interface for working with vLLM models.

Key Parameters

Parameter	Type	Description	Default
model_name	str	Name of the model to use	"meta-llama/Llama-2-7b-chat-hf"
system_prompt	str	System prompt to use	None
stream	bool	Whether to stream the output	False
temperature	float	Sampling temperature	0.5
max_tokens	int	Maximum number of tokens to generate	4000

Example with Custom Parameters

custom_parameters.py

vllm = VLLMWrapper(
    model_name="meta-llama/Llama-2-13b-chat-hf",
    system_prompt="You are an expert in artificial intelligence.",
    temperature=0.8,
    max_tokens=2000
)

Integration with Agents

You can easily integrate vLLM with Swarms agents for more complex workflows:

agent_integration.py

from swarms import Agent
from swarms.utils.vllm_wrapper import VLLMWrapper

# Initialize vLLM
vllm = VLLMWrapper(
    model_name="meta-llama/Llama-2-7b-chat-hf",
    system_prompt="You are a helpful assistant."
)

# Create an agent with vLLM
agent = Agent(
    agent_name="Research-Agent",
    agent_description="Expert in conducting research and analysis",
    system_prompt="""You are an expert research agent. Your tasks include:
    1. Analyzing complex topics
    2. Providing detailed summaries
    3. Making data-driven recommendations""",
    llm=vllm,
    max_loops=1
)

# Run the agent
response = agent.run("Research the impact of AI on healthcare")

Advanced Features

Batch Processing

Performance Optimization

Use batch processing for efficient handling of multiple tasks simultaneously.

batch_processing.py

tasks = [
    "What is machine learning?",
    "Explain neural networks",
    "Describe deep learning"
]

results = vllm.batched_run(tasks, batch_size=3)

Error Handling

Error Management

Always implement proper error handling in production environments.

error_handling.py

from loguru import logger

try:
    response = vllm.run("Complex task")
except Exception as error:
    logger.error(f"Error occurred: {error}")

Best Practices

Recommended Practices

Model SelectionSystem ResourcesPrompt EngineeringError HandlingPerformance

• Choose appropriate model sizes based on your requirements
• Consider the trade-off between model size and inference speed

• Ensure sufficient GPU memory for your chosen model
• Monitor resource usage during batch processing

• Use clear and specific system prompts
• Structure user prompts for optimal results

• Implement proper error handling and logging
• Set up monitoring for production deployments

• Use batch processing for multiple tasks
• Adjust max_tokens based on your use case
• Fine-tune temperature for optimal output quality

Example: Multi-Agent System

Here's an example of creating a multi-agent system using vLLM:

multi_agent_system.py

from swarms import Agent, ConcurrentWorkflow
from swarms.utils.vllm_wrapper import VLLMWrapper

# Initialize vLLM
vllm = VLLMWrapper(
    model_name="meta-llama/Llama-2-7b-chat-hf",
    system_prompt="You are a helpful assistant."
)

# Create specialized agents
research_agent = Agent(
    agent_name="Research-Agent",
    agent_description="Expert in research",
    system_prompt="You are a research expert.",
    llm=vllm
)

analysis_agent = Agent(
    agent_name="Analysis-Agent",
    agent_description="Expert in analysis",
    system_prompt="You are an analysis expert.",
    llm=vllm
)

# Create a workflow
agents = [research_agent, analysis_agent]
workflow = ConcurrentWorkflow(
    name="Research-Analysis-Workflow",
    description="Comprehensive research and analysis workflow",
    agents=agents
)

# Run the workflow
result = workflow.run("Analyze the impact of renewable energy")