Add a model to the SGLang Cookbook
Migrating an existing legacy-template page (one that imports a monolithic
…/autoregressive/<slug>-deployment.jsx generator)? Use the
cookbook-migrate-model skill instead — same target format, but the legacy
page (not the user) is the source of truth.
The cookbook is config-driven: two shared engines contain NO model-specific code — docs/src/snippets/deployment.jsx (the 5-dim deploy matrix) and playground.jsx (the diff-based override Playground). Adding a model = adding data: a per-model config (+ optional benchmarks) consumed by both engines, plus an MDX page that imports them. No engine edits.
Instantiate the model-agnostic template (NOT a clone of any live cookbook — the template is decoupled and covers all hardware + all axes):
templates/config.jsx.tmpl → docs/src/snippets/configs/<hf-org>/<model-slug>.jsx
templates/benchmarks.jsx.tmpl → …/<model-slug>-benchmarks.jsx (skip if no numbers)
templates/page.mdx.tmpl → docs/cookbook/<category>/<Vendor>/<ModelName>.mdx
The template uses explicit TOKEN placeholders; you fill them, prune what the model lacks, and replace the EXAMPLE cells with verified recipes. DeepSeek-V4 is a populated instance you can consult, but is not the template.
Deep references (read on demand, don't inline):
- [references/authoring-reference.md](references/authoring-reference.md) — field-by-field config / cells / playground / MDX contract.
- [references/mintlify-authoring.md](references/mintlify-authoring.md) — MDX rules (forbidden syntax, JSX tables, labeled fences) + invocation-example patterns. Read before writing §1–§3 prose.
- [references/engine-axis.md](references/engine-axis.md) — adding a new Playground feature axis (rare engine work).
- [references/vendor-logo.md](references/vendor-logo.md) — new-vendor card logo: ask the user for the brand logo, then generate the icon-only 940×525 RGBA PNG (spec + Pillow recipe +
git add -f).
- [references/diffusion-authoring.md](references/diffusion-authoring.md) — required opening, tag, command-picker, and feature-overlay contract for diffusion model pages. Read before editing any
docs/cookbook/diffusion/<Vendor>/<Model>.mdx page.
Architecture at a glance
┌─────────────────────────────────────────────────────────────────┐
│ cookbook/<category>/<Vendor>/<Model>.mdx │
│ import { Deployment } from "/src/snippets/_deployment.jsx"; │
│ import { Playground } from "/src/snippets/_playground.jsx"; │
│ import { config } from "/src/snippets/configs/.../X.jsx"; │
│ <Deployment config={config} /> <Playground config={config} />│
└─────────────────────────────────────────────────────────────────┘
│ (config passed as React prop)
▼
┌─────────────────────────────────────────────────────────────────┐
│ src/snippets/configs/<vendor>/<model>.jsx │
│ export const config = { │
│ supportedHardware, variants, quantizations, strategies, ... │
│ cells: [ { match:{hw,variant,quant,strategy,nodes}, │
│ env:[...], flags:[...] }, ... ], // 5-dim matrix │
│ playgroundFeatures: { attention, moe, parsers, ... }, │
│ }; │
└─────────────────────────────────────────────────────────────────┘
│ (consumed by BOTH engines — no model code in engines)
▼
┌──────────────────────────────────┬──────────────────────────────┐
│ _deployment.jsx │ _playground.jsx │
│ Renders the verified matrix; │ Renders override chips + │
│ one cell → its env/flags. │ diff against the cell. │
└──────────────────────────────────┴──────────────────────────────┘
The two widgets stay in sync via: the URL hash (deploy mirrors its selection; playground reads it), the sglang-deploy-sel custom event (deploy dispatches on every change; playground listens — replaceState doesn't fire hashchange), and the shared sglang-deploy-env localStorage key (HOST/PORT placeholders).
The main template is autoregressive. Diffusion pages use
templates/diffusion-page.mdx.tmpl plus
[references/diffusion-authoring.md](references/diffusion-authoring.md); do not force the
autoregressive deployment matrix on them. Omni pages follow their own category structure.
All categories still obey the Mintlify / NEW-tag / docs.json / category-card / validation
rules below.
Interactive, multi-step workflow. Collect inputs incrementally — don't ask for everything upfront. The real work is the verified cells[] recipes + measured benchmarks (Phases 2 + 4); everything else is filling the template.
Phase 1 — Collect inputs
- Model card — HuggingFace repo/URL. Fetch the page and extract description,
param count, architecture, context length, license. (Fetching guards factual bugs like an off-by-a-few-B param count.) If the model isn't public, ask the user.
- Variants / quantizations — keep separate: variants are size/mode (e.g. Flash/Pro,
Instruct/Thinking); quantizations come from the HF card / linked repos (BF16/FP8/FP4/…). Default to BF16 when a full-precision repo exists.
- Tested hardware + parallelism — which platforms are actually tested, and TP/EP/DP
for each. List only tested hw (unlisted greys out).
- Verified launch recipes — the full
sglang serve flags per
(hw × variant × quant × strategy × nodes) combo → these become cells[]. Rewrite any python -m sglang.launch_server to sglang serve form.
- sglang version / image tag — ask which sglang build the recipes + benchmarks ran on
(a release like 0.5.x, or main/nightly). Never guess or hallucinate it. This one tag fills dockerImages and the benchmarks' sglang_version; when the user is unsure, default the image to lmsysorg/sglang:dev (nightly) rather than inventing a release.
- Pre-flight:
gh pr list --repo sgl-project/sglang --search "<model>" (dup check).
Hardware reference (the shared HARDWARECATALOG in deployment.jsx). A GPU not in this table (RTX PRO 6000, GH200, future chips) goes in the model's own config.hardware ({id,label,vram,vendor}) — the engine merges it in; don't edit the engine catalog:
| Platform |
Vendor |
VRAM |
Docker image |
| H100 |
NVIDIA |
80GB |
lmsysorg/sglang:<ver> |
| H200 |
NVIDIA |
141GB |
lmsysorg/sglang:<ver> |
| B200 |
NVIDIA |
192GB |
lmsysorg/sglang:<ver> |
| B300 |
NVIDIA |
288GB |
lmsysorg/sglang:<ver> (or -cu130 when required) |
| GB200 |
NVIDIA |
192GB |
lmsysorg/sglang:<ver> (or -cu130) |
| GB300 |
NVIDIA |
288GB |
lmsysorg/sglang:<ver> (or -cu130) |
| DGX Spark |
NVIDIA |
128GB (unified) |
lmsysorg/sglang:<ver> — needs a CUDA 13 build |
| MI300X |
AMD |
192GB |
lmsysorg/sglang:<ver>-rocm720-mi30x |
| MI325X |
AMD |
256GB |
lmsysorg/sglang:<ver>-rocm720-mi30x |
| MI350X |
AMD |
288GB |
lmsysorg/sglang:<ver>-rocm720-mi35x |
| MI355X |
AMD |
288GB |
lmsysorg/sglang:<ver>-rocm720-mi35x |
- Image tag (
<ver>): don't guess — ask the user for the tag the recipes ran on, or
default to dev (nightly). The same tag goes in dockerImages and benchmarks' sglang_version; the engine falls back to lmsysorg/sglang:dev for any unmapped hw.
- TP sizing (sanity-check recipes):
weightGB / gpumem, round up to a power of 2,
~20–30% headroom. BF16 ≈ params×2 GB, FP8 ≈ ×1, FP4 ≈ ×0.5. MoE → total weight, not active params. FP4 is Blackwell-only (B200/B300/GB200/GB300/DGX Spark). GB200/GB300 single-node hosts are typically 4 GPUs (TP=4 ceiling); a DGX Spark is 1 GPU, so its only multi-GPU topology is TP=2 across 2 nodes.
- Platform flags: Blackwell may need
--attention-backend trtllm_mha; AMD typically
needs --attention-backend triton + env SGLANGUSEAITER=1 / SGLANGROCMFUSEDDECODEMLA=0 (check AITER TP constraints, e.g. headspergpu % 16 == 0).
- EP (MoE): 8-GPU NVIDIA
--tp 8 --ep 8; AMD EP = TP; small NVIDIA (TP≤4) omit
--ep unless benchmarked. (The template's AMD example cell shows these.)
Phase 2 — Instantiate the template
For a diffusion model, instantiate templates/diffusion-page.mdx.tmpl and keep the shared DiffusionModelTags component, plus templates/diffusion-config.jsx.tmpl for the opt-in scoped command builder. Put the compact install command and builder in §1 Quick start. The first two paragraphs in §2 Model capabilities are not generic filler: they must state the model's capability range, strongest differentiator, when to choose it, and at least one real deployment or capability boundary. Put orthogonal runtime features in scope: "serve" or scope: "request", not in the base recipe; use the schema from references/diffusion-authoring.md.
- Copy the three template files to their target paths (above). Note the two
vendor-folder conventions: under configs/ the folder is the HuggingFace org (deepseek-ai); under cookbook/ it's the display vendor (DeepSeek).
- Replace every
TOKEN: __MODELDISPLAY, MODELSLUG, HF_ORG__,
__HFREPO, REASONINGPARSER, TOOLCALLPARSER, ONELINER. Verify none remain: grep -rn '[A-Z_]*__' <new files>.
- Prune to what the model supports (delete, don't stub) — using
[references/authoring-reference.md](references/authoring-reference.md): - supportedHardware + the EXAMPLE cells: keep your tested families; **delete the mi* ids + AMD example cell if no AMD recipe**, etc. A GPU not in the shared catalog (e.g. RTX PRO 6000) → declare it in config.hardware and add its id here. - playgroundFeatures axes: remove the megamoe backend option + the megamoeQuant block from the moe axis (non-Blackwell-MoE), delete hisparse (non-DSA), pdDisagg/router (no PD), the parsers axis (no parsers), etc. - quantizations / variants: drop what the model doesn't ship; collapse variants to single default if there's no variant axis (then drop the variant half of modelNames/defaultAccuracy keys).
- Fill
cells[] with the verified recipes from Phase 1 (replace every EXAMPLE cell;
set verified: true only on tested combos), and modelNames with real HF slugs, dockerImages for your hw (use the Phase-1 tag, or default lmsysorg/sglang:dev — never a guessed release; key by hw, or a more specific key — hw|variant|quant, variant|quant, hw|quant|strategy, hw|quant, resolved in that order before hw — when one variant/quant/strategy needs its own image), multiNodeHints only for fabric-specific hw (e.g. gb200).
Site-wiring (do all three)
docs/docs.json — add the page under Cookbook → <category> → <Vendor>, at
the top of that vendor's pages (root-relative, no .mdx: cookbook/<category>/<Vendor>/<Model>). New vendor group → insert in the section's local ordering.
- NEW-tag hygiene — the new page keeps
tag: NEW (from the template). Scan the
vendor dir for existing NEW and strip it from siblings; verify ≤1: grep -rn 'tag: NEW' docs/cookbook/<category>/<Vendor>/ → at most one result. (Scan files; don't assume the first docs.json entry holds NEW.)
- Homepage card —
docs/cookbook/<category>/intro.mdx: if the org already has a
<Card>, update only its href (keep img). If the org is new, add a <Card> (title = nav-group name; keep card order aligned with docs.json) and create its logo: ask the user for the brand logo, then generate the conforming icon-only 940×525 RGBA transparent PNG → docs/cards/logos/<org-slug>.png per [references/vendor-logo.md](references/vendor-logo.md) (track with git add -f — *.png is gitignored repo-wide). Never invent or copy a logo.
Phase 3 — Validate
cd docs
mint validate # frontmatter, missing nav entries, MDX/JSX errors
mint broken-links
mint dev # visual smoke test at http://localhost:3000/cookbook/<category>/<Vendor>/<Model>
Spot-check: cells render sensible commands; URL-hash nav persists across reload; the Playground inherits the Deploy selection live; each axis toggle produces the expected diff; Docker mode wraps in docker run with the right image; multi-node cells emit the hints + --nnodes N; cURL resolves the model name; the NEW badge shows on the new page and is gone from same-vendor siblings; the homepage card points to the new model.
Phase 4 — Interactive testing
The user deploys each cell, runs the benches, and pastes results; you fill the data:
- mark each tested
cells[] entry verified: true (absent = yellow/unverified badge);
- fill the
<model>-benchmarks.jsx entries (one per cell match) with measured
speed/accuracy + the sglang_version the user reports (don't invent one — the template's 0.0.0 is a deliberate TODO); set model-level defaultAccuracy per variant. Leave a cell's entry as a bare match stub if it has no numbers yet (the card shows "pending").
Phase 5 — Prose & config tips
Read [references/mintlify-authoring.md](references/mintlify-authoring.md) first (it carries the parser-output-shape / thinking-mode / Output-Example / no-hardcoded-sampling rules + the Mintlify forbidden-syntax list). Then rewrite the MDX prose from the HF card + user notes: §1 Model Introduction (description, links, params, license, variants table), §2 Configuration Tips (hw-specific tuning, caveats), §3 Advanced Usage (Reasoning / Tool-Calling / HiCache — keep only what applies; match the reasoning example to the parser's output shape; each runnable block gets an Output Example:).
For diffusion pages, follow the category-specific Quick start and capability contract in references/diffusion-authoring.md. Run node docs/scripts/checkcookbookconfigs.mjs to verify the tag widget and introduction structure before rendering the page.
Phase 6 — Review
/cookbook-review-pr <PR number>
Git workflow
Always branch — never commit to main directly.
git checkout -b add-<model>-cookbook
git add docs/src/snippets/configs/<hf-org>/<slug>.jsx \
docs/src/snippets/configs/<hf-org>/<slug>-benchmarks.jsx \
docs/cookbook/<category>/<Vendor>/<Model>.mdx \
docs/docs.json docs/cookbook/<category>/intro.mdx
git commit -m "Add <Display-Name> cookbook"
git push -u origin add-<model>-cookbook
gh pr create --title "Add <Display-Name> cookbook" --body "..."