diff --git a/docs/docs/sglang-diffusion/api/openai_api.mdx b/docs/docs/sglang-diffusion/api/openai_api.mdx
index 069b4bfbd..0638d2b26 100644
--- a/docs/docs/sglang-diffusion/api/openai_api.mdx
+++ b/docs/docs/sglang-diffusion/api/openai_api.mdx
@@ -369,19 +369,21 @@ curl -X POST http://localhost:30010/v1/set_lora \
       }'
 ```
 
-> [!NOTE]
-> When using multiple LoRAs:
-> - All list parameters (`lora_nickname`, `lora_path`, `target`, `strength`) must have the same length
-> - If `target` or `strength` is a single value, it will be applied to all LoRAs
-> - Multiple LoRAs applied to the same target are applied in order
+<Note>
+When using multiple LoRAs:
+- All list parameters (`lora_nickname`, `lora_path`, `target`, `strength`) must have the same length
+- If `target` or `strength` is a single value, it will be applied to all LoRAs
+- Multiple LoRAs applied to the same target are applied in order
+</Note>
 
 
 **Merge LoRA Weights**
 
 Manually merges the currently set LoRA weights into the base model.
 
-> [!NOTE]
-> With FSDP-sharded weights, manual merge may require a full-gather and can OOM. Use `set_lora` with `merge_mode="auto"` or `"dynamic"` for the lower-peak path.
+<Note>
+With FSDP-sharded weights, manual merge may require a full-gather and can OOM. Use `set_lora` with `merge_mode="auto"` or `"dynamic"` for the lower-peak path.
+</Note>
 
 **Endpoint:** `POST /v1/merge_lora_weights`
 
diff --git a/docs/docs/sglang-diffusion/deployment_cookbook.mdx b/docs/docs/sglang-diffusion/deployment_cookbook.mdx
index 624ea1c08..c917bfb5c 100644
--- a/docs/docs/sglang-diffusion/deployment_cookbook.mdx
+++ b/docs/docs/sglang-diffusion/deployment_cookbook.mdx
@@ -146,8 +146,9 @@ When `torch.compile` is enabled, `--offload-during-compile` stays on by default.
 
 Breakable CUDA graph is a separate manual opt-in for supported image pipelines. If you enable `--enable-breakable-cuda-graph`, declare every served resolution in `--warmup-resolutions` so warmup captures matching graph signatures.
 
-> [!NOTE]
-> The preset is intentionally coarse. A future continuous value such as `0.0` to `1.0` could express the speed-memory tradeoff more precisely, but it would need model-specific memory models and clearer user expectations. Until then, use the preset plus explicit flags for overrides.
+<Note>
+The preset is intentionally coarse. A future continuous value such as `0.0` to `1.0` could express the speed-memory tradeoff more precisely, but it would need model-specific memory models and clearer user expectations. Until then, use the preset plus explicit flags for overrides.
+</Note>
 
 Examples: