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vLLM Cookbook

cookbook/90_models/vllm/README.md

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vLLM Cookbook

vLLM is a fast and easy-to-use library for running LLM models locally.

Setup

1. Create and activate a virtual environment

shell
python3 -m venv ~/.venvs/aienv
source ~/.venvs/aienv/bin/activate

2. Install vLLM package

shell
uv pip install vllm

3. Serve a model (this downloads the model to your local machine the first time you run it)

shell
vllm serve Qwen/Qwen2.5-7B-Instruct \
    --enable-auto-tool-choice \
    --tool-call-parser hermes \
    --dtype float16 \
    --max-model-len 2048 \
    --gpu-memory-utilization 0.9

Using vLLM for Embeddings (Local Mode)

vLLM embedders can load and run embedding models locally without requiring a server.

Setup for Local Embeddings

  1. Install vLLM (if not already installed):

    bash
    uv pip install vllm

  2. Choose an embedding model:

    Recommended models:

    • intfloat/e5-mistral-7b-instruct (4096 dimensions, 7B parameters)
    • BAAI/bge-large-en-v1.5 (1024 dimensions, 335M parameters)
    • sentence-transformers/all-MiniLM-L6-v2 (384 dimensions, 22M parameters)

  3. GPU Requirements:

    • e5-mistral-7b-instruct: ~14GB VRAM
    • bge-large: ~2GB VRAM
    • all-MiniLM-L6-v2: ~500MB VRAM

  4. Usage:

    python
    from agno.knowledge.embedder.vllm import VLLMEmbedder

# Local mode (no server needed)
embedder = VLLMEmbedder(
    id="intfloat/e5-mistral-7b-instruct",
    dimensions=4096
)

# Get embeddings
embedding = embedder.get_embedding("Hello world")
print(f"Embedding dimension: {len(embedding)}")

  5. Examples:

    • Basic usage: cookbook/07_knowledge/embedders/vllm_embedder.py
    • With batching: cookbook/07_knowledge/embedders/vllm_embedder_batching.py

Local vs Remote Mode

Local Mode (no server):

  • Use VLLMEmbedder(id="model-name")
  • Model loads directly into GPU/CPU
  • No base_url needed
  • Best for: Development, single-machine deployment

Remote Mode (requires server):

  • Use VLLMEmbedder(base_url="http://localhost:8000/v1")
  • Connects to running vLLM server
  • Best for: Production, shared infrastructure

Performance Tips

  • Enable batching for multiple embeddings:

    python
    embedder = VLLMEmbedder(
    id="intfloat/e5-mistral-7b-instruct",
    enable_batch=True,
    batch_size=32  # Adjust based on GPU memory
)

  • Use smaller models for faster inference if precision isn't critical

  • For CPU-only: Use smaller models (bge-small, MiniLM)

Examples

shell
python cookbook/92_models/vllm/basic.py

Embeddings