Skip to content

quickstart.md

Latest commit

 

History

History
133 lines (98 loc) · 3.25 KB

quickstart.md

File metadata and controls

133 lines (98 loc) · 3.25 KB

Quick Start

Option 1: OpenAI-Compatible Server

Start the server:

# Simple mode - maximum throughput for single user
vllm-mlx serve mlx-community/Llama-3.2-3B-Instruct-4bit --port 8000

# Continuous batching - for multiple concurrent users
vllm-mlx serve mlx-community/Llama-3.2-3B-Instruct-4bit --port 8000 --continuous-batching

Use with OpenAI Python SDK:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")

response = client.chat.completions.create(
    model="mlx-community/Llama-3.2-3B-Instruct-4bit",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

Or with curl:

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "default", "messages": [{"role": "user", "content": "Hello!"}]}'

Option 2: Direct Python API

from vllm_mlx.models import MLXLanguageModel

model = MLXLanguageModel("mlx-community/Llama-3.2-3B-Instruct-4bit")
model.load()

# Generate text
output = model.generate("What is the capital of France?", max_tokens=100)
print(output.text)

# Streaming
for chunk in model.stream_generate("Tell me a story"):
    print(chunk.text, end="", flush=True)

Option 3: Gradio Chat UI

vllm-mlx-chat --model mlx-community/Llama-3.2-3B-Instruct-4bit

Opens a web interface at http://localhost:7860

Multimodal Models

For image/video understanding, use a VLM model:

vllm-mlx serve mlx-community/Qwen3-VL-4B-Instruct-3bit --port 8000
response = client.chat.completions.create(
    model="default",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What's in this image?"},
            {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
        ]
    }],
    max_tokens=256
)

Reasoning Models

Separate the model's thinking process from the final answer:

vllm-mlx serve mlx-community/Qwen3-8B-4bit --reasoning-parser qwen3
response = client.chat.completions.create(
    model="default",
    messages=[{"role": "user", "content": "What is 17 × 23?"}]
)
print(response.choices[0].message.content)  # Final answer

Embeddings

Generate text embeddings for semantic search and RAG:

vllm-mlx serve mlx-community/Qwen3-4B-4bit --embedding-model mlx-community/multilingual-e5-small-mlx
response = client.embeddings.create(
    model="mlx-community/multilingual-e5-small-mlx",
    input="Hello world"
)

Tool Calling

Enable function calling with any supported model:

vllm-mlx serve mlx-community/Devstral-Small-2507-4bit \
  --enable-auto-tool-choice --tool-call-parser mistral

Next Steps