@@ -30,6 +30,18 @@ The `/generate` endpoint accepts the following parameters in JSON format. For de
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 <MODEL> --sampling-defaults model
@@ -37,10 +49,10 @@ The object is defined at `sampling_params.py::SamplingParams`. You can also read
| 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. |
| temperature | `float = 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 = 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 = -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 = 0.0` | [Min-p](https://github.com/huggingface/transformers/issues/27670) samples from tokens with probability larger than `min_p * highest_token_probability`. |
| 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`. |
