> ## Documentation Index > Fetch the complete documentation index at: https://docs.sglang.io/llms.txt > Use this file to discover all available pages before exploring further. # Embedding models > 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. Native encoder embedding architectures and `google/embeddinggemma-300m` are detected automatically. Decoder-style embedding models require `--is-embedding`; add `--trust-remote-code` when the model requires it. ## Quick Start ### Launch Server ```bash theme={null} sglang serve \ --model-path Qwen/Qwen3-Embedding-4B \ --is-embedding ``` ### EmbeddingGemma EmbeddingGemma uses bidirectional attention and is auto-detected, so its best default command is simply: ```bash theme={null} sglang serve --model-path google/embeddinggemma-300m ``` On CUDA, SGLang automatically uses breakable CUDA graph (BCG) for its full encoder prefill and disables incompatible radix-cache and chunked-prefill behavior. Do not add the deprecated piecewise CUDA graph knobs. ### Client Request ```python theme={null} 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" # or "base64" for compact FP32 responses } 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: ```bash theme={null} sglang serve \ --model-path Alibaba-NLP/gme-Qwen2-VL-2B-Instruct \ --is-embedding \ --chat-template gme-qwen2-vl ``` ```python Example theme={null} 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 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: ```bash Command theme={null} sglang serve \ --model-path Qwen/Qwen3-Embedding-0.6B \ --is-embedding \ --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 ```python theme={null} 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
EmbeddingGemma `google/embeddinggemma-300m` N/A Bidirectional Gemma3 text encoder; auto-detected and served with BCG by default
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
Qwen3 (bare backbone) `microsoft/harrier-oss-v1-0.6b` N/A Bare Qwen3Model backbone (no LM head); served natively on SGLang's fused Qwen3 kernels and auto-classified as an embedding model
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