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Rerank models

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SGLang offers comprehensive support for rerank models by incorporating optimized serving frameworks with a flexible programming interface. This setup enables efficient processing of cross-encoder reranking tasks, improving the accuracy and relevance of search result ordering. SGLang’s design ensures high throughput and low latency during reranker model deployment, making it ideal for semantic-based result refinement in large-scale retrieval systems.

<Warning> Rerank models in SGLang fall into two categories:
  • Cross-encoder rerank models: run with --is-embedding (embedding runner).
  • Decoder-only rerank models: run without --is-embedding and use next-token logprob scoring (yes/no).
    • Text-only (e.g. Qwen3-Reranker)
    • Multimodal (e.g. Qwen3-VL-Reranker): also supports image/video content

Some models may require --trust-remote-code. </Warning>

Supported rerank models

<table> <thead> <tr> <th>Model Family (Rerank)</th> <th>Example HuggingFace Identifier</th> <th>Chat Template</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><strong>BGE-Reranker (BgeRerankModel)</strong></td> <td><code>BAAI/bge-reranker-v2-m3</code></td> <td>N/A</td> <td>Currently only support <code>attention-backend</code> <code>triton</code> and <code>torch_native</code>. High-performance cross-encoder reranker model from BAAI. Suitable for reranking search results based on semantic relevance.</td> </tr> <tr> <td><strong>Qwen3-Reranker (decoder-only yes/no)</strong></td> <td><code>Qwen/Qwen3-Reranker-8B</code></td> <td><code>examples/chat_template/qwen3_reranker.jinja</code></td> <td>Decoder-only reranker using next-token logprob scoring for labels (yes/no). Launch <strong>without</strong> <code>--is-embedding</code>.</td> </tr> <tr> <td><strong>Qwen3-VL-Reranker (multimodal yes/no)</strong></td> <td><code>Qwen/Qwen3-VL-Reranker-2B</code></td> <td><code>examples/chat_template/qwen3_vl_reranker.jinja</code></td> <td>Multimodal decoder-only reranker supporting text, images, and videos. Uses yes/no logprob scoring. Launch <strong>without</strong> <code>--is-embedding</code>.</td> </tr> </tbody> </table>

Cross-Encoder Rerank (embedding runner)

Launch Command

shell
python3 -m sglang.launch_server \
  --model-path BAAI/bge-reranker-v2-m3 \
  --host 0.0.0.0 \
  --disable-radix-cache \
  --chunked-prefill-size -1 \
  --attention-backend triton \
  --is-embedding \
  --port 30000

Example Client Request

python
import requests

url = "http://127.0.0.1:30000/v1/rerank"

payload = {
    "model": "BAAI/bge-reranker-v2-m3",
    "query": "what is panda?",
    "documents": [
        "hi",
        "The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China."
    ],
    "top_n": 1,
    "return_documents": True
}

response = requests.post(url, json=payload)
response_json = response.json()

for item in response_json:
    if item.get("document"):
        print(f"Score: {item['score']:.2f} - Document: '{item['document']}'")
    else:
        print(f"Score: {item['score']:.2f} - Index: {item['index']}")

Request Parameters:

  • query (required): The query text to rank documents against
  • documents (required): List of documents to be ranked
  • model (required): Model to use for reranking
  • top_n (optional): Maximum number of documents to return. Defaults to returning all documents. If specified value is greater than the total number of documents, all documents will be returned.
  • return_documents (optional): Whether to return documents in the response. Defaults to True.

Qwen3-Reranker (decoder-only yes/no rerank)

Launch Command

shell
python3 -m sglang.launch_server \
  --model-path Qwen/Qwen3-Reranker-0.6B \
  --trust-remote-code \
  --disable-radix-cache \
  --host 0.0.0.0 \
  --port 8001 \
  --chat-template examples/chat_template/qwen3_reranker.jinja
<Note> Qwen3-Reranker uses decoder-only logprob scoring (yes/no). Do NOT launch it with `--is-embedding`. </Note>

Example Client Request (supports optional instruct, top_n, and return_documents)

shell
curl -X POST http://127.0.0.1:8001/v1/rerank \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen3-Reranker-0.6B",
    "query": "法国首都是哪里？",
    "documents": [
      "法国的首都是巴黎。",
      "德国的首都是柏林。",
      "香蕉是黄色的水果。"
    ],
    "instruct": "Given a web search query, retrieve relevant passages that answer the query.",
    "top_n": 2,
    "return_documents": true
  }'

Request Parameters:

  • query (required): The query text to rank documents against
  • documents (required): List of documents to be ranked
  • model (required): Model to use for reranking
  • instruct (optional): Instruction text for the reranker
  • top_n (optional): Maximum number of documents to return. Defaults to returning all documents. If specified value is greater than the total number of documents, all documents will be returned.
  • return_documents (optional): Whether to return documents in the response. Defaults to True.

Response Format

/v1/rerank returns a list of objects (sorted by descending score):

  • score: float, higher means more relevant
  • document: the original document string (only included when return_documents is true)
  • index: the original index in the input documents
  • meta_info: optional debug/usage info (may be present for some models)

The number of returned results is controlled by the top_n parameter. If top_n is not specified or is greater than the total number of documents, all documents are returned.

Example (with return_documents: true):

json
[
  {"score": 0.99, "document": "法国的首都是巴黎。", "index": 0},
  {"score": 0.01, "document": "德国的首都是柏林。", "index": 1},
  {"score": 0.00, "document": "香蕉是黄色的水果。", "index": 2}
]

Example (with return_documents: false):

json
[
  {"score": 0.99, "index": 0},
  {"score": 0.01, "index": 1},
  {"score": 0.00, "index": 2}
]

Example (with top_n: 2):

json
[
  {"score": 0.99, "document": "法国的首都是巴黎。", "index": 0},
  {"score": 0.01, "document": "德国的首都是柏林。", "index": 1}
]

Common Pitfalls

  • --chat-template is required. Without --chat-template examples/chat_template/qwen3_reranker.jinja, the server does not recognize the model as a decoder-only reranker and returns a 400 error: "This model does not appear to be an embedding model by default. Please add --is-embedding...". The fix is to add the chat template flag, NOT --is-embedding.
  • If you launch Qwen3-Reranker with --is-embedding, /v1/rerank cannot compute yes/no logprob scores. Relaunch without --is-embedding.
  • If you see a validation error like "score should be a valid number" and the backend returned a list, upgrade to a version that coerces embedding[0] into score for rerank responses.

Qwen3-VL-Reranker (multimodal decoder-only rerank)

Qwen3-VL-Reranker extends the Qwen3-Reranker to support multimodal content, allowing reranking of documents containing text, images, and videos.

Launch Command

shell
python3 -m sglang.launch_server \
  --model-path Qwen/Qwen3-VL-Reranker-2B \
  --trust-remote-code \
  --disable-radix-cache \
  --host 0.0.0.0 \
  --port 30000 \
  --chat-template examples/chat_template/qwen3_vl_reranker.jinja
<Note> Qwen3-VL-Reranker uses decoder-only logprob scoring (yes/no) like Qwen3-Reranker. Do NOT launch it with `--is-embedding`. </Note>

Text-Only Reranking (backward compatible)

python
import requests

url = "http://127.0.0.1:30000/v1/rerank"

payload = {
    "model": "Qwen3-VL-Reranker-2B",
    "query": "What is machine learning?",
    "documents": [
        "Machine learning is a branch of artificial intelligence that enables computers to learn from data.",
        "The weather in Paris is usually mild with occasional rain.",
        "Deep learning is a subset of machine learning using neural networks with many layers.",
    ],
    "instruct": "Retrieve passages that answer the question.",
    "return_documents": True
}

response = requests.post(url, json=payload)
results = response.json()

for item in results:
    print(f"Score: {item['score']:.4f} - {item['document'][:60]}...")

Image Reranking (text query, image/mixed documents)

python
import requests

url = "http://127.0.0.1:30000/v1/rerank"

payload = {
    "query": "A woman playing with her dog on a beach at sunset.",
    "documents": [
        # Document 1: Text description
        "A woman shares a joyful moment with her golden retriever on a sun-drenched beach at sunset.",
        # Document 2: Image URL
        [
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://example.com/beach_dog.jpeg"
                }
            }
        ],
        # Document 3: Text + Image (mixed)
        [
            {"type": "text", "text": "A joyful scene at the beach:"},
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://example.com/beach_dog.jpeg"
                }
            }
        ]
    ],
    "instruct": "Retrieve images or text relevant to the user's query.",
    "return_documents": False
}

response = requests.post(url, json=payload)
results = response.json()

for item in results:
    print(f"Index: {item['index']}, Score: {item['score']:.4f}")

Multimodal Query Reranking (query with image)

python
import requests

url = "http://127.0.0.1:30000/v1/rerank"

payload = {
    # Query with text and image
    "query": [
        {"type": "text", "text": "Find similar images to this:"},
        {
            "type": "image_url",
            "image_url": {
                "url": "https://example.com/reference_image.jpeg"
            }
        }
    ],
    "documents": [
        "A cat sleeping on a couch.",
        "A woman and her dog enjoying the sunset at the beach.",
        "A busy city street with cars and pedestrians.",
        [
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://example.com/similar_image.jpeg"
                }
            }
        ]
    ],
    "instruct": "Find images or descriptions similar to the query image."
}

response = requests.post(url, json=payload)
results = response.json()

for item in results:
    print(f"Index: {item['index']}, Score: {item['score']:.4f}")

Request Parameters (Multimodal)

  • query (required): Can be a string (text-only) or a list of content parts:
    • &#123;"type": "text", "text": "..."&#125; for text
    • &#123;"type": "image_url", "image_url": &#123;"url": "..."&#125;&#125; for images
    • &#123;"type": "video_url", "video_url": &#123;"url": "..."&#125;&#125; for videos
  • documents (required): List where each document can be a string or list of content parts (same format as query)
  • instruct (optional): Instruction text for the reranker
  • top_n (optional): Maximum number of documents to return
  • return_documents (optional): Whether to return documents in the response (default: false)

Common Pitfalls

  • Always use --chat-template examples/chat_template/qwen3_vl_reranker.jinja for Qwen3-VL-Reranker.
  • Do NOT launch with --is-embedding.
  • For best results, use --disable-radix-cache to avoid caching issues with multimodal content.
  • Note: Currently only Qwen3-VL-Reranker-2B is tested and supported. The 8B model may have different behavior and is not guaranteed to work with this template.