[docs] fix note formatting in sglang-d documentation (#35761)
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@@ -369,19 +369,21 @@ curl -X POST http://localhost:30010/v1/set_lora \
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}'
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}'
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```
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```
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> [!NOTE]
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<Note>
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> When using multiple LoRAs:
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When using multiple LoRAs:
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> - All list parameters (`lora_nickname`, `lora_path`, `target`, `strength`) must have the same length
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- All list parameters (`lora_nickname`, `lora_path`, `target`, `strength`) must have the same length
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> - If `target` or `strength` is a single value, it will be applied to all LoRAs
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- If `target` or `strength` is a single value, it will be applied to all LoRAs
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> - Multiple LoRAs applied to the same target are applied in order
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- Multiple LoRAs applied to the same target are applied in order
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</Note>
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**Merge LoRA Weights**
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**Merge LoRA Weights**
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Manually merges the currently set LoRA weights into the base model.
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Manually merges the currently set LoRA weights into the base model.
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> [!NOTE]
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<Note>
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> 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.
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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.
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</Note>
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**Endpoint:** `POST /v1/merge_lora_weights`
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**Endpoint:** `POST /v1/merge_lora_weights`
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@@ -146,8 +146,9 @@ When `torch.compile` is enabled, `--offload-during-compile` stays on by default.
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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.
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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.
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> [!NOTE]
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<Note>
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> 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.
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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.
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</Note>
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Examples:
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Examples:
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