sglang_v0.5.2/sglang/docs/supported_models/rerank_models.md

Rerank Models

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.

They are executed with `--is-embedding` and some may require `--trust-remote-code`

Example Launch Command

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

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."
    ]
}

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

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

Supported rerank models

Model Family (Rerank)	Example HuggingFace Identifier	Chat Template	Description
BGE-Reranker (BgeRerankModel)	BAAI/bge-reranker-v2-m3	N/A	Currently only support attention-backend triton and torch_native. high-performance cross-encoder reranker model from BAAI. Suitable for reranking search results based on semantic relevance.