Update README.md

README.md

@@ -104,8 +104,22 @@ vllm serve \
 
 #### SGLang
 
-Laguna M.1 is supported in SGLang via [sgl-project/sglang#28400](https://github.com/sgl-project/sglang/pull/28400). Quantization is detected automatically from `quantization_config`, so no extra flags are required. A full serving recipe will be added to the main [Laguna M.1 model card](https://huggingface.co/poolside/Laguna-M.1).
+The full SGLang recipe is on the [SGLang Cookbook](https://docs.sglang.io/cookbook/autoregressive/Poolside/Laguna-M.1). Quantization is detected automatically, so no extra flags are required. 
+```shell
+git clone https://github.com/sgl-project/sglang.git
+cd sglang
+pip install -e "python[all]"
 
+sglang serve \
+    --model-path poolside/Laguna-M.1-FP8 \
+    --trust-remote-code \
+    --reasoning-parser poolside_v1 \
+    --tool-call-parser poolside_v1 \
+    --tp 8 \
+    --host 0.0.0.0 \
+    --port 30000 \
+    --fp8-gemm-backend triton # Needed for SM100
+```
 #### TRT-LLM
 
 Laguna is supported in TensorRT-LLM thanks to the team at NVIDIA ([NVIDIA/TensorRT-LLM#13559](https://github.com/NVIDIA/TensorRT-LLM/pull/13559), with partial-RoPE fusion in [#15110](https://github.com/NVIDIA/TensorRT-LLM/pull/15110)). The full recipe is on the main [Laguna M.1 model card](https://huggingface.co/poolside/Laguna-M.1). Quantization is detected automatically from `quantization_config` in this checkpoint, so no extra flags are required.