change sglang serve section location

README.md

@@ -921,20 +921,7 @@ Example output:
 
 ### SGLang
 
-[SGLang Diffusion](https://sgl-project.github.io/diffusion) can serve `nvidia/Cosmos3-Nano` through OpenAI-compatible image and video generation endpoints.
-
-For complete serving instructions and request examples, see the [Cosmos3 SGLang cookbook](https://lmsysorg.mintlify.app/cookbook/diffusion/Cosmos/Cosmos3).
-
-## Limitations
-
-Cosmos3 may produce imperfect outputs in challenging scenarios. Generation artifacts include temporal inconsistency, unstable camera or object motion, imprecise physical interactions, inaccurate audio-video synchronization, and action-state drift — especially in long-horizon or high-resolution outputs. Reasoning may also be incorrect: object states, causal relationships, spatial geometry, temporal ordering, agent intent, and future outcomes can be misinferred, and complex or long-context inputs may yield hallucinated entities, inconsistent interpretations, or implausible predictions. Because the model lacks an explicit physics simulator, 3D geometry, 4D space-time evolution, object permanence, contact dynamics, and physical laws are only approximated — producing artifacts such as disappearing or morphing objects, unrealistic collisions, and physically implausible motions. Quality further degrades in out-of-distribution environments, safety-critical edge cases, and domains underrepresented in training.
-
-Cosmos3 outputs should not be treated as physically accurate simulation, reliable ground-truth reasoning, or safety-certified decision making. Applications involving robotics control, autonomous systems, scientific simulation, or safety-critical planning require additional validation, external constraints, system-level safety analysis, and domain-specific guardrails before deployment.
-
-
-## SGLang Serve
-
-[SGLang Diffusion](https://github.com/sgl-project/sglang) can serve Cosmos3-Nano through OpenAI-compatible image and video endpoints. Install SGLang from the main branch with diffusion dependencies, then start a server:
+[SGLang Diffusion](https://sgl-project.github.io/diffusion) can serve `nvidia/Cosmos3-Nano` through OpenAI-compatible image and video generation endpoints. Install SGLang from the main branch with diffusion dependencies, then start a server:
 
 ```shell
 git clone --branch main https://github.com/sgl-project/sglang.git
@@ -993,6 +980,14 @@ curl -sS -L "http://localhost:30000/v1/videos/${job_id}/content" \
 
 SGLang accepts Cosmos 3 request options including `max_sequence_length`, `flow_shift`, `extra_params.guardrails`, `extra_params.use_resolution_template`, and `extra_params.use_duration_template`. Video-to-video, video-with-sound, and action generation are not supported by SGLang yet.
 
+For complete serving instructions and request examples, see the [Cosmos3 SGLang cookbook](https://lmsysorg.mintlify.app/cookbook/diffusion/Cosmos/Cosmos3).
+
+## Limitations
+
+Cosmos3 may produce imperfect outputs in challenging scenarios. Generation artifacts include temporal inconsistency, unstable camera or object motion, imprecise physical interactions, inaccurate audio-video synchronization, and action-state drift — especially in long-horizon or high-resolution outputs. Reasoning may also be incorrect: object states, causal relationships, spatial geometry, temporal ordering, agent intent, and future outcomes can be misinferred, and complex or long-context inputs may yield hallucinated entities, inconsistent interpretations, or implausible predictions. Because the model lacks an explicit physics simulator, 3D geometry, 4D space-time evolution, object permanence, contact dynamics, and physical laws are only approximated — producing artifacts such as disappearing or morphing objects, unrealistic collisions, and physically implausible motions. Quality further degrades in out-of-distribution environments, safety-critical edge cases, and domains underrepresented in training.
+
+Cosmos3 outputs should not be treated as physically accurate simulation, reliable ground-truth reasoning, or safety-certified decision making. Applications involving robotics control, autonomous systems, scientific simulation, or safety-critical planning require additional validation, external constraints, system-level safety analysis, and domain-specific guardrails before deployment.
+
 ## Inference
 
 **Acceleration Engine:** [PyTorch](https://pytorch.org/), [vLLM](https://github.com/vllm-project/vllm), [vLLM-Omni](https://github.com/vllm-project/vllm-omni), [Hugging Face Diffusers](https://github.com/huggingface/diffusers), [SGLang](https://sgl-project.github.io/), [SGLang Diffusion](https://sgl-project.github.io/diffusion)