docs: add --trust-remote-code to Laguna-M.1 / XS.2 cookbook configs (#28693)

2026-06-19 10:21:39 +08:00
parent c7397de571
commit d962d18f15
4 changed files with 22 additions and 4 deletions
@@ -10,7 +10,7 @@ tag: NEW

 <Accordion title="Install SGLang">

-Laguna-M.1 support is already on SGLang `main` — **softplus per-element attention-output gating** ([PR #28400](https://github.com/sgl-project/sglang/pull/28400)) and a **global-attention fix** ([PR #28604](https://github.com/sgl-project/sglang/pull/28604), since M.1 is full-attention `sliding_window: 0`) — but not yet in a tagged release. The two paths below match the **Python / Docker** toggle in the command panel: install from `main` (Python tab), or use the **Docker** image, which bundles the same build (CUDA 13, covers H200 + all Blackwell). The model loads natively, so no `--trust-remote-code` is needed.
+Laguna-M.1 support is already on SGLang `main` — **softplus per-element attention-output gating** ([PR #28400](https://github.com/sgl-project/sglang/pull/28400)) and a **global-attention fix** ([PR #28604](https://github.com/sgl-project/sglang/pull/28604), since M.1 is full-attention `sliding_window: 0`) — but not yet in a tagged release. The two paths below match the **Python / Docker** toggle in the command panel: install from `main` (Python tab), or use the **Docker** image, which bundles the same build (CUDA 13, covers H200 + all Blackwell). The model ships custom config code on the Hub, so `--trust-remote-code` is required (it is included in the launch commands).

 <Tabs>

@@ -109,6 +109,7 @@ import { Playground } from "/src/snippets/_playground.jsx";

 ## 2. Configuration Tips

+- **Trust remote code** (`--trust-remote-code`): Laguna-M.1 ships custom modeling/config code on the Hugging Face Hub, so this flag is required for the server to load the model.
 - **Long-context memory**: M.1 is global-attention (no sliding-window), so the 262,144-token KV cache is large. If you hit OOM at full context, lower `--mem-fraction-static` or cap `--context-length`.
 - **FP8**: On **Blackwell** the recipe adds `--fp8-gemm-backend triton` — the compressed-tensors block-FP8 weight scales aren't UE8M0-packed, so the default DeepGEMM path emits garbage on Blackwell (sm_100); the Triton backend is correct (~19% slower). Temporary workaround pending [PR #28662](https://github.com/sgl-project/sglang/pull/28662) (which fixes the scales and restores the DeepGEMM fast path). On **Hopper (H200)** FP8 uses DeepGEMM with no extra flag — pre-warm its multi-session JIT with `python3 -m sglang.compile_deep_gemm --model poolside/Laguna-M.1-FP8` to avoid paying it on each restart.
 - **Parsers** (`poolside_v1`): for agentic / tool-using deployments enable the **Reasoning Parser** and **Tool Call Parser** in the Playground above — they emit `--reasoning-parser poolside_v1` (thinking → `reasoning_content`) and `--tool-call-parser poolside_v1` (structured `tool_calls`).
@@ -81,6 +81,7 @@ import { LagunaXS2Deployment } from '/src/snippets/autoregressive/laguna-xs2-dep

 ### 3.2 Configuration Tips

+- **Trust remote code** (`--trust-remote-code`): Laguna-XS.2 ships custom modeling/config code on the Hugging Face Hub, so this flag is required for the server to load the model.
 - **Quantization**: NVFP4 requires Blackwell (B200 / B300); BF16 and FP8 run on either H200 or B200. FP8's first launch triggers a multi-session DeepGEMM JIT pre-compile (~10-20 min); pre-warm with `python3 -m sglang.compile_deep_gemm --model poolside/Laguna-XS.2-FP8` to avoid that cost on every restart.
 - **Reasoning parser** (`--reasoning-parser poolside_v1`): Splits `<think>...</think>` segments into `reasoning_content` so `content` holds only the final answer. Disable only if you want the raw `<think>` tags in `content`.
 - **Tool call parser** (`--tool-call-parser poolside_v1`): Required for OpenAI-compatible tool-call streaming. Disable only for chat-only deployments.