Embedding Models

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.

Important

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
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