--- title: Embedding models description: Dense and sparse embedding models with FlashInfer acceleration and SGLang's batching infrastructure. --- 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 must be launched with the `--is-embedding` flag. Some models may also require `--trust-remote-code`. ## Quick start 1. **Launch the server** ```bash python3 -m sglang.launch_server \ --model-path Qwen/Qwen3-Embedding-4B \ --is-embedding \ --host 0.0.0.0 \ --port 30000 ``` 2. **Send a client request** ```python 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: 1. **Launch the server with a multimodal model** ```bash 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 ``` 2. **Send a multimodal request** ```python 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](https://sbert.net/examples/sentence_transformer/training/matryoshka/README.html#matryoshka-embeddings) or [Matryoshka Representation Learning (MRL)](https://arxiv.org/abs/2205.13147) is a technique used in training embedding models. It allows users 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, use `--json-model-override-args` as below: ```bash 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 validates 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** ```python 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	Example HF model	Chat template	Notes
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` or `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