# Sampling Parameters This doc describes the sampling parameters of the SGLang Runtime. It is the low-level endpoint of the runtime. If you want a high-level endpoint that can automatically handle chat templates, consider using the [OpenAI Compatible API](openai_api_completions.ipynb). ## `/generate` Endpoint The `/generate` endpoint accepts the following parameters in JSON format. For detailed usage, see the [native API doc](native_api.ipynb). The object is defined at `io_struct.py::GenerateReqInput`. You can also read the source code to find more arguments and docs. | Argument | Type/Default | Description | |----------------------------|------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------| | text | `Optional[Union[List[str], str]] = None` | The input prompt. Can be a single prompt or a batch of prompts. | | input_ids | `Optional[Union[List[List[int]], List[int]]] = None` | The token IDs for text; one can specify either text or input_ids. | | input_embeds | `Optional[Union[List[List[List[float]]], List[List[float]]]] = None` | The embeddings for input_ids; one can specify either text, input_ids, or input_embeds. | | image_data | `Optional[Union[List[List[ImageDataItem]], List[ImageDataItem], ImageDataItem]] = None` | The image input. Supports three formats: (1) **Raw images**: PIL Image, file path, URL, or base64 string; (2) **Processor output**: Dict with `format: "processor_output"` containing HuggingFace processor outputs; (3) **Precomputed embeddings**: Dict with `format: "precomputed_embedding"` and `feature` containing pre-calculated visual embeddings. Can be a single image, list of images, or list of lists of images. See [Multimodal Input Formats](#multimodal-input-formats) for details. | | audio_data | `Optional[Union[List[AudioDataItem], AudioDataItem]] = None` | The audio input. Can be a file name, URL, or base64 encoded string. | | sampling_params | `Optional[Union[List[Dict], Dict]] = None` | The sampling parameters as described in the sections below. | | rid | `Optional[Union[List[str], str]] = None` | The request ID. | | return_logprob | `Optional[Union[List[bool], bool]] = None` | Whether to return log probabilities for tokens. | | logprob_start_len | `Optional[Union[List[int], int]] = None` | If return_logprob, the start location in the prompt for returning logprobs. Default is "-1", which returns logprobs for output tokens only. | | top_logprobs_num | `Optional[Union[List[int], int]] = None` | If return_logprob, the number of top logprobs to return at each position. | | token_ids_logprob | `Optional[Union[List[List[int]], List[int]]] = None` | If return_logprob, the token IDs to return logprob for. | | return_text_in_logprobs | `bool = False` | Whether to detokenize tokens in text in the returned logprobs. | | stream | `bool = False` | Whether to stream output. | | lora_path | `Optional[Union[List[Optional[str]], Optional[str]]] = None` | The path to the LoRA. | | custom_logit_processor | `Optional[Union[List[Optional[str]], str]] = None` | Custom logit processor for advanced sampling control. Must be a serialized instance of `CustomLogitProcessor` using its `to_str()` method. For usage see below. | | return_hidden_states | `Union[List[bool], bool] = False` | Whether to return hidden states. | | return_routed_experts | `bool = False` | Whether to return routed experts for MoE models. Requires `--enable-return-routed-experts` server flag. Returns base64-encoded int32 expert IDs as a flattened array with logical shape `[num_tokens, num_layers, top_k]`. | ## Sampling parameters The object is defined at `sampling_params.py::SamplingParams`. You can also read the source code to find more arguments and docs. ### Note on defaults By default, SGLang initializes several sampling parameters from the model's `generation_config.json` (when the server is launched with `--sampling-defaults model`, which is the default). To use SGLang/OpenAI constant defaults instead, start the server with `--sampling-defaults openai`. You can always override any parameter per request via `sampling_params`. ```bash # Use model-provided defaults from generation_config.json (default behavior) python -m sglang.launch_server --model-path --sampling-defaults model # Use SGLang/OpenAI constant defaults instead python -m sglang.launch_server --model-path --sampling-defaults openai ``` ### Core parameters | Argument | Type/Default | Description | |-----------------|----------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------| | max_new_tokens | `int = 128` | The maximum output length measured in tokens. | | stop | `Optional[Union[str, List[str]]] = None` | One or multiple [stop words](https://platform.openai.com/docs/api-reference/chat/create#chat-create-stop). Generation will stop if one of these words is sampled. | | stop_token_ids | `Optional[List[int]] = None` | Provide stop words in the form of token IDs. Generation will stop if one of these token IDs is sampled. | | stop_regex | `Optional[Union[str, List[str]]] = None` | Stop when hitting any of the regex patterns in this list | | temperature | `float (model default; fallback 1.0)` | [Temperature](https://platform.openai.com/docs/api-reference/chat/create#chat-create-temperature) when sampling the next token. `temperature = 0` corresponds to greedy sampling, a higher temperature leads to more diversity. | | top_p | `float (model default; fallback 1.0)` | [Top-p](https://platform.openai.com/docs/api-reference/chat/create#chat-create-top_p) selects tokens from the smallest sorted set whose cumulative probability exceeds `top_p`. When `top_p = 1`, this reduces to unrestricted sampling from all tokens. | | top_k | `int (model default; fallback -1)` | [Top-k](https://developer.nvidia.com/blog/how-to-get-better-outputs-from-your-large-language-model/#predictability_vs_creativity) randomly selects from the `k` highest-probability tokens. | | min_p | `float (model default; fallback 0.0)` | [Min-p](https://github.com/huggingface/transformers/issues/27670) samples from tokens with probability larger than `min_p * highest_token_probability`. | ### Penalizers | Argument | Type/Default | Description | |--------------------|------------------------|------------------------------------------------------------------------------------------------------------------------------------------------| | frequency_penalty | `float = 0.0` | Penalizes tokens based on their frequency in generation so far. Must be between `-2` and `2` where negative numbers encourage repeatment of tokens and positive number encourages sampling of new tokens. The scaling of penalization grows linearly with each appearance of a token. | | presence_penalty | `float = 0.0` | Penalizes tokens if they appeared in the generation so far. Must be between `-2` and `2` where negative numbers encourage repeatment of tokens and positive number encourages sampling of new tokens. The scaling of the penalization is constant if a token occurred. | | repetition_penalty | `float = 1.0` | Scales the logits of previously generated tokens to discourage (values > 1) or encourage (values < 1) repetition. Valid range is `[0, 2]`; `1.0` leaves probabilities unchanged. | | min_new_tokens | `int = 0` | Forces the model to generate at least `min_new_tokens` until a stop word or EOS token is sampled. Note that this might lead to unintended behavior, for example, if the distribution is highly skewed towards these tokens. | ### Constrained decoding Please refer to our dedicated guide on [constrained decoding](../advanced_features/structured_outputs.ipynb) for the following parameters. | Argument | Type/Default | Description | |-----------------|---------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------| | json_schema | `Optional[str] = None` | JSON schema for structured outputs. | | regex | `Optional[str] = None` | Regex for structured outputs. | | ebnf | `Optional[str] = None` | EBNF for structured outputs. | | structural_tag | `Optional[str] = None` | The structural tag for structured outputs. | ### Other options | Argument | Type/Default | Description | |-------------------------------|---------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------| | n | `int = 1` | Specifies the number of output sequences to generate per request. (Generating multiple outputs in one request (n > 1) is discouraged; repeating the same prompts several times offers better control and efficiency.) | | ignore_eos | `bool = False` | Don't stop generation when EOS token is sampled. | | skip_special_tokens | `bool = True` | Remove special tokens during decoding. | | spaces_between_special_tokens | `bool = True` | Whether or not to add spaces between special tokens during detokenization. | | no_stop_trim | `bool = False` | Don't trim stop words or EOS token from the generated text. | | custom_params | `Optional[List[Optional[Dict[str, Any]]]] = None` | Used when employing `CustomLogitProcessor`. For usage, see below. | ## Examples ### Normal Launch a server: ```bash python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --port 30000 ``` Send a request: ```python import requests response = requests.post( "http://localhost:30000/generate", json={ "text": "The capital of France is", "sampling_params": { "temperature": 0, "max_new_tokens": 32, }, }, ) print(response.json()) ``` Detailed example in [send request](./send_request.ipynb). ### Streaming Send a request and stream the output: ```python import requests, json response = requests.post( "http://localhost:30000/generate", json={ "text": "The capital of France is", "sampling_params": { "temperature": 0, "max_new_tokens": 32, }, "stream": True, }, stream=True, ) prev = 0 for chunk in response.iter_lines(decode_unicode=False): chunk = chunk.decode("utf-8") if chunk and chunk.startswith("data:"): if chunk == "data: [DONE]": break data = json.loads(chunk[5:].strip("\n")) output = data["text"].strip() print(output[prev:], end="", flush=True) prev = len(output) print("") ``` Detailed example in [openai compatible api](openai_api_completions.ipynb). ### Multimodal Launch a server: ```bash python3 -m sglang.launch_server --model-path lmms-lab/llava-onevision-qwen2-7b-ov ``` Download an image: ```bash curl -o example_image.png -L https://github.com/sgl-project/sglang/blob/main/examples/assets/example_image.png?raw=true ``` Send a request: ```python import requests response = requests.post( "http://localhost:30000/generate", json={ "text": "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n" "<|im_start|>user\n\nDescribe this image in a very short sentence.<|im_end|>\n" "<|im_start|>assistant\n", "image_data": "example_image.png", "sampling_params": { "temperature": 0, "max_new_tokens": 32, }, }, ) print(response.json()) ``` The `image_data` can be a file name, a URL, or a base64 encoded string. See also `python/sglang/srt/utils.py:load_image`. Streaming is supported in a similar manner as [above](#streaming). Detailed example in [OpenAI API Vision](openai_api_vision.ipynb). ### Structured Outputs (JSON, Regex, EBNF) You can specify a JSON schema, regular expression or [EBNF](https://en.wikipedia.org/wiki/Extended_Backus%E2%80%93Naur_form) to constrain the model output. The model output will be guaranteed to follow the given constraints. Only one constraint parameter (`json_schema`, `regex`, or `ebnf`) can be specified for a request. SGLang supports two grammar backends: - [XGrammar](https://github.com/mlc-ai/xgrammar) (default): Supports JSON schema, regular expression, and EBNF constraints. - XGrammar currently uses the [GGML BNF format](https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md). - [Outlines](https://github.com/dottxt-ai/outlines): Supports JSON schema and regular expression constraints. If instead you want to initialize the Outlines backend, you can use `--grammar-backend outlines` flag: ```bash python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct \ --port 30000 --host 0.0.0.0 --grammar-backend [xgrammar|outlines] # xgrammar or outlines (default: xgrammar) ``` ```python import json import requests json_schema = json.dumps({ "type": "object", "properties": { "name": {"type": "string", "pattern": "^[\\w]+$"}, "population": {"type": "integer"}, }, "required": ["name", "population"], }) # JSON (works with both Outlines and XGrammar) response = requests.post( "http://localhost:30000/generate", json={ "text": "Here is the information of the capital of France in the JSON format.\n", "sampling_params": { "temperature": 0, "max_new_tokens": 64, "json_schema": json_schema, }, }, ) print(response.json()) # Regular expression (Outlines backend only) response = requests.post( "http://localhost:30000/generate", json={ "text": "Paris is the capital of", "sampling_params": { "temperature": 0, "max_new_tokens": 64, "regex": "(France|England)", }, }, ) print(response.json()) # EBNF (XGrammar backend only) response = requests.post( "http://localhost:30000/generate", json={ "text": "Write a greeting.", "sampling_params": { "temperature": 0, "max_new_tokens": 64, "ebnf": 'root ::= "Hello" | "Hi" | "Hey"', }, }, ) print(response.json()) ``` Detailed example in [structured outputs](../advanced_features/structured_outputs.ipynb). ### Custom logit processor Launch a server with `--enable-custom-logit-processor` flag on. ```bash python -m sglang.launch_server \ --model-path meta-llama/Meta-Llama-3-8B-Instruct \ --port 30000 \ --enable-custom-logit-processor ``` Define a custom logit processor that will always sample a specific token id. ```python from sglang.srt.sampling.custom_logit_processor import CustomLogitProcessor class DeterministicLogitProcessor(CustomLogitProcessor): """A dummy logit processor that changes the logits to always sample the given token id. """ def __call__(self, logits, custom_param_list): # Check that the number of logits matches the number of custom parameters assert logits.shape[0] == len(custom_param_list) key = "token_id" for i, param_dict in enumerate(custom_param_list): # Mask all other tokens logits[i, :] = -float("inf") # Assign highest probability to the specified token logits[i, param_dict[key]] = 0.0 return logits ``` Send a request: ```python import requests response = requests.post( "http://localhost:30000/generate", json={ "text": "The capital of France is", "custom_logit_processor": DeterministicLogitProcessor().to_str(), "sampling_params": { "temperature": 0.0, "max_new_tokens": 32, "custom_params": {"token_id": 5}, }, }, ) print(response.json()) ``` Send an OpenAI chat completion request: ```python import openai from sglang.utils import print_highlight client = openai.Client(base_url="http://127.0.0.1:30000/v1", api_key="None") response = client.chat.completions.create( model="meta-llama/Meta-Llama-3-8B-Instruct", messages=[ {"role": "user", "content": "List 3 countries and their capitals."}, ], temperature=0.0, max_tokens=32, extra_body={ "custom_logit_processor": DeterministicLogitProcessor().to_str(), "custom_params": {"token_id": 5}, }, ) print_highlight(f"Response: {response}") ```