sglang/docs_new/docs/basic_usage/native_api.mdx

---
title: "SGLang Native APIs"
metatags:
    description: "SGLang native server APIs for text generation, embedding, reranking, model info, cache management, and more."
---
Apart from the OpenAI compatible APIs, the SGLang Runtime also provides its native server APIs. We introduce the following APIs:

- `/generate` (text generation model)
- `/get_model_info`
- `/server_info`
- `/health`
- `/health_generate`
- `/flush_cache`
- `/update_weights`
- `/encode`(embedding model)
- `/v1/rerank`(cross encoder rerank model)
- `/v1/score`(decoder-only scoring)
- `/classify`(reward model)
- `/start_expert_distribution_record`
- `/stop_expert_distribution_record`
- `/dump_expert_distribution_record`
- `/tokenize`
- `/detokenize`
- A full list of these APIs can be found at [http_server.py](https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/entrypoints/http_server.py)

We mainly use `requests` to test these APIs in the following examples. You can also use `curl`.

## Launch A Server

```python Example
from sglang.test.doc_patch import launch_server_cmd
from sglang.utils import wait_for_server, print_highlight, terminate_process

server_process, port = launch_server_cmd(
    "python3 -m sglang.launch_server --model-path qwen/qwen2.5-0.5b-instruct --host 0.0.0.0 --log-level warning"
)

wait_for_server(f"http://localhost:{port}", process=server_process)
```

## Generate (text generation model)
Generate completions. This is similar to the `/v1/completions` in OpenAI API. Detailed parameters can be found in the [sampling parameters](./sampling_params).

```python Example
import requests

url = f"http://localhost:{port}/generate"
data = {"text": "What is the capital of France?"}

response = requests.post(url, json=data)
print_highlight(response.json())
```

## Get Model Info

Get the information of the model.

- `model_path`: The path/name of the model.
- `is_generation`: Whether the model is used as generation model or embedding model.
- `tokenizer_path`: The path/name of the tokenizer.
- `preferred_sampling_params`: The default sampling params specified via `--preferred-sampling-params`. `None` is returned in this example as we did not explicitly configure it in server args.
- `weight_version`: This field contains the version of the model weights. This is often used to track changes or updates to the model’s trained parameters.
- `has_image_understanding`: Whether the model has image-understanding capability.
- `has_audio_understanding`: Whether the model has audio-understanding capability.
- `model_type`: The model type from the HuggingFace config (e.g., "qwen2", "llama").
- `architectures`: The model architectures from the HuggingFace config (e.g., ["Qwen2ForCausalLM"]).
- `embedding`: The resolved embedding-serving plan. It includes pooling, normalization, execution and attention style, Matryoshka dimensions, cache policy, and effective BCG prefill settings. This field is available when the model configuration exposes an embedding capability contract.

```python Example
url = f"http://localhost:{port}/get_model_info"

response = requests.get(url)
response_json = response.json()
print_highlight(response_json)
assert response_json["model_path"] == "qwen/qwen2.5-0.5b-instruct"
assert response_json["is_generation"] is True
assert response_json["tokenizer_path"] == "qwen/qwen2.5-0.5b-instruct"
assert response_json["preferred_sampling_params"] is None
assert response_json.keys() == {
    "model_path",
    "is_generation",
    "tokenizer_path",
    "preferred_sampling_params",
    "weight_version",
    "has_image_understanding",
    "has_audio_understanding",
    "model_type",
    "architectures",
    "embedding",
}
```

## Get Server Info
Gets the server information including CLI arguments, token limits, and memory pool sizes.
- Note: `get_server_info` merges the following deprecated endpoints:
  - `get_server_args`
  - `get_memory_pool_size`
  - `get_max_total_num_tokens`

```python Example
url = f"http://localhost:{port}/server_info"

response = requests.get(url)
print_highlight(response.text)
```

## Health Check
- `/health`: Check the health of the server.
- `/health_generate`: Check the health of the server by generating one token.

```python Example
url = f"http://localhost:{port}/health_generate"

response = requests.get(url)
print_highlight(response.text)
```

```python Example
url = f"http://localhost:{port}/health"

response = requests.get(url)
print_highlight(response.text)
```

## Flush Cache

Flush the radix cache. It will be automatically triggered when the model weights are updated by the `/update_weights` API.

Parameters:
- `timeout` (query, float, default `0`, unit: seconds): Wait time for idle state before flushing. `0` means fail fast if not idle. When HiCache async operations are in-flight, a non-zero timeout allows the server to wait until idle before flushing, avoiding unnecessary 400 errors.

```bash Command
# With timeout (wait up to 30s for idle state)
curl -s -X POST "http://127.0.0.1:30000/flush_cache?timeout=30"
```

```python Example
url = f"http://localhost:{port}/flush_cache"

response = requests.post(url)
print_highlight(response.text)
```

## Update Weights From Disk

Update model weights from disk without restarting the server. Only applicable for models with the same architecture and parameter size.

SGLang support `update_weights_from_disk` API for continuous evaluation during training (save checkpoint to disk and update weights from disk).

```python Example
# successful update with same architecture and size

url = f"http://localhost:{port}/update_weights_from_disk"
data = {"model_path": "qwen/qwen2.5-0.5b-instruct"}

response = requests.post(url, json=data)
print_highlight(response.text)
assert response.json()["success"] is True
assert response.json()["message"] == "Succeeded to update model weights."
```

```python Example
# failed update with different parameter size or wrong name

url = f"http://localhost:{port}/update_weights_from_disk"
data = {"model_path": "qwen/qwen2.5-0.5b-instruct-wrong"}

response = requests.post(url, json=data)
response_json = response.json()
print_highlight(response_json)
assert response_json["success"] is False
assert response_json["message"] == (
    "Failed to get weights iterator: "
    "qwen/qwen2.5-0.5b-instruct-wrong"
    " (repository not found)."
)
```

```python Example
terminate_process(server_process)
```

## Encode (embedding model)

Encode text into embeddings. Note that this API is only available for [embedding models](./openai_api_embeddings) and will raise an error for generation models.
Therefore, we launch a new server to server an embedding model.

```python Example
embedding_process, port = launch_server_cmd("""
python3 -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-1.5B-instruct \
    --host 0.0.0.0 --is-embedding --log-level warning
""")

wait_for_server(f"http://localhost:{port}", process=embedding_process)
```

```python Example
# successful encode for embedding model

url = f"http://localhost:{port}/encode"
data = {"model": "Alibaba-NLP/gte-Qwen2-1.5B-instruct", "text": "Once upon a time"}

response = requests.post(url, json=data)
response_json = response.json()
print_highlight(f"Text embedding (first 10): {response_json['embedding'][:10]}")
```

```python Example
terminate_process(embedding_process)
```

## v1/rerank (cross encoder rerank model)
Rerank a list of documents given a query using a cross-encoder model. Note that this API is only available for cross encoder model like [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) with `attention-backend` `triton` and `torch_native`.

```python Example
reranker_process, port = launch_server_cmd("""
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 --log-level warning
""")

wait_for_server(f"http://localhost:{port}", process=reranker_process)
```

```python Example
# compute rerank scores for query and documents

url = f"http://localhost:{port}/v1/rerank"
data = {
    "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=data)
response_json = response.json()
for item in response_json:
    print_highlight(f"Score: {item['score']:.2f} - Document: '{item['document']}'")
```

```python Example
terminate_process(reranker_process)
```

## v1/score (decoder-only scoring)

Compute token probabilities for specified tokens given a query and items. This is useful for classification tasks, scoring responses, or computing log-probabilities.

Parameters:
- `query`: Query text
- `items`: Item text(s) to score
- `label_token_ids`: Token IDs to compute probabilities for
- `apply_softmax`: Whether to apply softmax to get normalized probabilities (default: False)
- `item_first`: Whether items come first in concatenation order (default: False)
- `model`: Model name

The response contains `scores` - a list of probability lists, one per item, each in the order of `label_token_ids`.

```python Example
score_process, port = launch_server_cmd("""
python3 -m sglang.launch_server --model-path qwen/qwen2.5-0.5b-instruct \
    --host 0.0.0.0 --log-level warning
""")

wait_for_server(f"http://localhost:{port}", process=score_process)
```

```python Example
# Score the probability of different completions given a query
query = "The capital of France is"
items = ["Paris", "London", "Berlin"]

url = f"http://localhost:{port}/v1/score"
data = {
    "model": "qwen/qwen2.5-0.5b-instruct",
    "query": query,
    "items": items,
    "label_token_ids": [9454, 2753],  # e.g. "Yes" and "No" token ids
    "apply_softmax": True,  # Normalize probabilities to sum to 1
}

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

# Display scores for each item
for item, scores in zip(items, response_json["scores"]):
    print_highlight(f"Item '{item}': probabilities = {[f'{s:.4f}' for s in scores]}")
```

```python Example
terminate_process(score_process)
```

## Classify (reward model)

SGLang Runtime also supports reward models. Here we use a reward model to classify the quality of pairwise generations.

```python Example
# Note that SGLang now treats embedding models and reward models as the same type of models.
# This will be updated in the future.

reward_process, port = launch_server_cmd("""
python3 -m sglang.launch_server --model-path Skywork/Skywork-Reward-Llama-3.1-8B-v0.2 --host 0.0.0.0 --is-embedding --log-level warning
""")

wait_for_server(f"http://localhost:{port}", process=reward_process)
```

```python Example
from transformers import AutoTokenizer

PROMPT = (
    "What is the range of the numeric output of a sigmoid node in a neural network?"
)

RESPONSE1 = "The output of a sigmoid node is bounded between -1 and 1."
RESPONSE2 = "The output of a sigmoid node is bounded between 0 and 1."

CONVS = [
    [{"role": "user", "content": PROMPT}, {"role": "assistant", "content": RESPONSE1}],
    [{"role": "user", "content": PROMPT}, {"role": "assistant", "content": RESPONSE2}],
]

tokenizer = AutoTokenizer.from_pretrained("Skywork/Skywork-Reward-Llama-3.1-8B-v0.2")
prompts = tokenizer.apply_chat_template(CONVS, tokenize=False, return_dict=False)

url = f"http://localhost:{port}/classify"
data = {"model": "Skywork/Skywork-Reward-Llama-3.1-8B-v0.2", "text": prompts}

responses = requests.post(url, json=data).json()
for response in responses:
    print_highlight(f"reward: {response['embedding'][0]}")
```

```python Example
terminate_process(reward_process)
```

## Capture expert selection distribution in MoE models

SGLang Runtime supports recording the number of times an expert is selected in a MoE model run for each expert in the model. This is useful when analyzing the throughput of the model and plan for optimization.

*Note: We only print out the first 10 lines of the csv below for better readability. Please adjust accordingly if you want to analyze the results more deeply.*

```python Example
expert_record_server_process, port = launch_server_cmd(
    "python3 -m sglang.launch_server --model-path Qwen/Qwen1.5-MoE-A2.7B --host 0.0.0.0 --expert-distribution-recorder-mode stat --log-level warning"
)

wait_for_server(f"http://localhost:{port}", process=expert_record_server_process)
```

```python Example
response = requests.post(f"http://localhost:{port}/start_expert_distribution_record")
print_highlight(response)

url = f"http://localhost:{port}/generate"
data = {"text": "What is the capital of France?"}

response = requests.post(url, json=data)
print_highlight(response.json())

response = requests.post(f"http://localhost:{port}/stop_expert_distribution_record")
print_highlight(response)

response = requests.post(f"http://localhost:{port}/dump_expert_distribution_record")
print_highlight(response)
```

```python Example
terminate_process(expert_record_server_process)
```

## Tokenize/Detokenize Example (Round Trip)

This example demonstrates how to use the /tokenize and /detokenize endpoints together. We first tokenize a string, then detokenize the resulting IDs to reconstruct the original text. This workflow is useful when you need to handle tokenization externally but still leverage the server for detokenization.

```python Example
tokenizer_free_server_process, port = launch_server_cmd("""
python3 -m sglang.launch_server --model-path qwen/qwen2.5-0.5b-instruct
""")

wait_for_server(f"http://localhost:{port}", process=tokenizer_free_server_process)
```

```python Example
import requests
from sglang.utils import print_highlight

base_url = f"http://localhost:{port}"
tokenize_url = f"{base_url}/tokenize"
detokenize_url = f"{base_url}/detokenize"

model_name = "qwen/qwen2.5-0.5b-instruct"
input_text = "SGLang provides efficient tokenization endpoints."
print_highlight(f"Original Input Text:\n'{input_text}'")

# --- tokenize the input text ---
tokenize_payload = {
    "model": model_name,
    "prompt": input_text,
    "add_special_tokens": False,
}
try:
    tokenize_response = requests.post(tokenize_url, json=tokenize_payload)
    tokenize_response.raise_for_status()
    tokenization_result = tokenize_response.json()
    token_ids = tokenization_result.get("tokens")

    if not token_ids:
        raise ValueError("Tokenization returned empty tokens.")

    print_highlight(f"\nTokenized Output (IDs):\n{token_ids}")
    print_highlight(f"Token Count: {tokenization_result.get('count')}")
    print_highlight(f"Max Model Length: {tokenization_result.get('max_model_len')}")

    # --- detokenize the obtained token IDs ---
    detokenize_payload = {
        "model": model_name,
        "tokens": token_ids,
        "skip_special_tokens": True,
    }

    detokenize_response = requests.post(detokenize_url, json=detokenize_payload)
    detokenize_response.raise_for_status()
    detokenization_result = detokenize_response.json()
    reconstructed_text = detokenization_result.get("text")

    print_highlight(f"\nDetokenized Output (Text):\n'{reconstructed_text}'")

    if input_text == reconstructed_text:
        print_highlight(
            "\nRound Trip Successful: Original and reconstructed text match."
        )
    else:
        print_highlight(
            "\nRound Trip Mismatch: Original and reconstructed text differ."
        )

except requests.exceptions.RequestException as e:
    print_highlight(f"\nHTTP Request Error: {e}")
except Exception as e:
    print_highlight(f"\nAn error occurred: {e}")
```

```python Example
terminate_process(tokenizer_free_server_process)
```