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

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:

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]}'

1. 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.
2. 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