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Add examples for returning hidden states when using the server (#4074)

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Qiaolin Yu
2025-03-04 22:31:50 -05:00
committed by GitHub
parent 77a3954bf7
commit 4725e3f652
4 changed files with 71 additions and 2 deletions
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2
docs/backend/offline_engine_api.ipynb
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@@ -33,7 +33,7 @@
"source": [
"## Advanced Usage\n",
"\n",
"The engine supports [vlm inference](https://github.com/sgl-project/sglang/blob/main/examples/runtime/engine/offline_batch_inference_vlm.py) as well as [extracting hidden states](https://github.com/sgl-project/sglang/blob/main/examples/runtime/engine/hidden_states.py). \n",
"The engine supports [vlm inference](https://github.com/sgl-project/sglang/blob/main/examples/runtime/engine/offline_batch_inference_vlm.py) as well as [extracting hidden states](https://github.com/sgl-project/sglang/blob/main/examples/runtime/hidden_states). \n",
"\n",
"Please see [the examples](https://github.com/sgl-project/sglang/tree/main/examples/runtime/engine) for further use cases."
]

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docs/backend/sampling_params.md
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@@ -17,7 +17,7 @@ The `/generate` endpoint accepts the following parameters in JSON format. For in
* `stream: bool = False` Whether to stream the output.
* `lora_path: Optional[Union[List[Optional[str]], Optional[str]]] = None` Path to LoRA weights.
* `custom_logit_processor: Optional[Union[List[Optional[str]], str]] = None` Custom logit processor for advanced sampling control. For usage see below.
* `return_hidden_states: bool = False` Whether to return hidden states of the model. Note that each time it changes, the cuda graph will be recaptured, which might lead to a performance hit. See the [examples](https://github.com/sgl-project/sglang/blob/main/examples/runtime/engine/hidden_states.py) for more information.
* `return_hidden_states: bool = False` Whether to return hidden states of the model. Note that each time it changes, the cuda graph will be recaptured, which might lead to a performance hit. See the [examples](https://github.com/sgl-project/sglang/blob/main/examples/runtime/hidden_states) for more information.
## Sampling params