Embedding models

SGLang provides robust support for embedding models by integrating efficient serving mechanisms with its flexible programming interface. This integration allows for streamlined handling of embedding tasks, facilitating faster and more accurate retrieval and semantic search operations. SGLang’s architecture enables better resource utilization and reduced latency in embedding model deployment.

Embedding models are executed with --is-embedding flag and some may require --trust-remote-code

Quick Start

Launch Server

python3 -m sglang.launch_server \
  --model-path Qwen/Qwen3-Embedding-4B \
  --is-embedding \
  --host 0.0.0.0 \
  --port 30000

Client Request

import requests

url = "http://127.0.0.1:30000"

payload = {
    "model": "Qwen/Qwen3-Embedding-4B",
    "input": "What is the capital of France?",
    "encoding_format": "float"
}

response = requests.post(url + "/v1/embeddings", json=payload).json()
print("Embedding:", response["data"][0]["embedding"])

Multimodal Embedding Example

For multimodal models like GME that support both text and images:

python3 -m sglang.launch_server \
  --model-path Alibaba-NLP/gme-Qwen2-VL-2B-Instruct \
  --is-embedding \
  --chat-template gme-qwen2-vl \
  --host 0.0.0.0 \
  --port 30000

Example

import requests

url = "http://127.0.0.1:30000"

text_input = "Represent this image in embedding space."
image_path = "https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild/resolve/main/images/023.jpg"

payload = {
    "model": "gme-qwen2-vl",
    "input": [
        {
            "text": text_input
        },
        {
            "image": image_path
        }
    ],
}

response = requests.post(url + "/v1/embeddings", json=payload).json()

print("Embeddings:", [x.get("embedding") for x in response.get("data", [])])

Matryoshka Embedding Example

Matryoshka Embeddings or Matryoshka Representation Learning (MRL) is a technique used in training embedding models. It allows user to trade off between performance and cost.

1. Launch a Matryoshka‑capable model

If the model config already includes matryoshka_dimensions or is_matryoshka then no override is needed. Otherwise, you can use --json-model-override-args as below:

Command

python3 -m sglang.launch_server \
    --model-path Qwen/Qwen3-Embedding-0.6B \
    --is-embedding \
    --host 0.0.0.0 \
    --port 30000 \
    --json-model-override-args '{"matryoshka_dimensions": [128, 256, 512, 1024, 1536]}'

Setting "is_matryoshka": true allows truncating to any dimension. Otherwise, the server will validate that the specified dimension in the request is one of matryoshka_dimensions.
Omitting dimensions in a request returns the full vector.

2. Make requests with different output dimensions

import requests

url = "http://127.0.0.1:30000"

# Request a truncated (Matryoshka) embedding by specifying a supported dimension.
payload = {
    "model": "Qwen/Qwen3-Embedding-0.6B",
    "input": "Explain diffusion models simply.",
    "dimensions": 512  # change to 128 / 1024 / omit for full size
}

response = requests.post(url + "/v1/embeddings", json=payload).json()
print("Embedding:", response["data"][0]["embedding"])

Supported Models

Model Family	Example Model	Chat template	Description
E5 (Llama/Mistral based)	`intfloat/e5-mistral-7b-instruct`	N/A	High-quality text embeddings based on Mistral/Llama architectures
GTE-Qwen2	`Alibaba-NLP/gte-Qwen2-7B-instruct`	N/A	Alibaba’s text embedding model with multilingual support
Qwen3-Embedding	`Qwen/Qwen3-Embedding-4B`	N/A	Latest Qwen3-based text embedding model for semantic representation
BGE	`BAAI/bge-large-en-v1.5`	N/A	BAAI’s text embeddings (requires `attention-backend` triton/torch_native)
GME (Multimodal)	`Alibaba-NLP/gme-Qwen2-VL-2B-Instruct`	`gme-qwen2-vl`	Multimodal embedding for text and image cross-modal tasks
CLIP	`openai/clip-vit-large-patch14-336`	N/A	OpenAI’s CLIP for image and text embeddings

Basic Usage

Advanced Features

Supported Models

Developer Guide

References

Quick Start

Launch Server

Client Request

Multimodal Embedding Example

Matryoshka Embedding Example

1. Launch a Matryoshka‑capable model

2. Make requests with different output dimensions

Supported Models

​Quick Start

​Launch Server

​Client Request

​Multimodal Embedding Example

​Matryoshka Embedding Example

​1. Launch a Matryoshka‑capable model

​2. Make requests with different output dimensions

​Supported Models

Quick Start

Launch Server

Client Request

Multimodal Embedding Example

Matryoshka Embedding Example

1. Launch a Matryoshka‑capable model

2. Make requests with different output dimensions

Supported Models