feat(sglang): wire engine_args, add cuda13 build, ship MTP gallery demos

[richiejp]

This allows all of SGLang's options to be used and adds some MTP models to the gallery.

I only successfully tested MiMo-7B. We need a new Transformers library to be released for Gemma 4 with MTB to work.

Also added CUDA13 support in the process.

---

Bring the sglang Python backend up to feature parity with vllm by adding
the same engine_args:-map plumbing the vLLM backend already has. Any
ServerArgs field (~380 in sglang 0.5.11) becomes settable from a model
YAML, including the speculative-decoding flags needed for Multi-Token
Prediction. Validation matches the vllm backend's: keys are checked
against dataclasses.fields(ServerArgs), unknown keys raise ValueError
with a difflib close-match suggestion at LoadModel time, and the typed
ModelOptions fields keep their existing meaning with engine_args
overriding them.

Backend code:

• backend/python/sglang/backend.py: add _apply_engine_args, import
  dataclasses/difflib/ServerArgs, call from LoadModel; rename Seed ->
  sampling_seed (sglang 0.5.11 renamed the SamplingParams field).
• backend/python/sglang/test.py + test.sh + Makefile: six unit tests
  exercising the helper directly (no engine load required).

Build / CI / backend gallery (cuda13 path is now first-class):

• backend/python/sglang/install.sh: add --prerelease=allow because
  sglang 0.5.11 hard-pins flash-attn-4 which only ships beta wheels;
  add --index-strategy=unsafe-best-match for cublas12 so the cu128
  torch index wins over default-PyPI's cu130.
• backend/python/sglang/requirements-cublas{12,13}{,-after}.txt: pin
  sglang>=0.5.11 (Gemma 4 floor); add cu130 torch index for cublas13
  (new files) and cu128 torch index for cublas12 (default PyPI now
  ships cu130 torch wheels by default and breaks cu12 hosts).
• backend/index.yaml: add cuda13-sglang and cuda13-sglang-development
  capability mappings + image entries pointing at
  quay.io/.../-gpu-nvidia-cuda-13-sglang.
• .github/workflows/backend.yml: new cublas13 sglang matrix entry,
  mirroring vllm's cuda13 build.

Model gallery + docs:

• gallery/sglang.yaml: base sglang config template, mirrors vllm.yaml.
• gallery/sglang-gemma-4-{e2b,e4b}-mtp.yaml: Gemma 4 MTP demos
  transcribed verbatim from the SGLang Gemma 4 cookbook MTP commands.
• gallery/sglang-mimo-7b-mtp.yaml: MiMo-7B-RL with built-in MTP heads
  • online fp8 weight quantization, verified end-to-end on a 16 GB
    RTX 5070 Ti at ~88 tok/s. Uses mem_fraction_static: 0.7 because the
    MTP draft worker's vocab embedding is loaded unquantised and OOMs
    the static reservation at sglang's 0.85 default.
• gallery/index.yaml: three new entries (gemma-4-e2b-it:sglang-mtp,
  gemma-4-e4b-it:sglang-mtp, mimo-7b-mtp:sglang).
• docs/content/features/text-generation.md: new SGLang section with
  setup, engine_args reference, MTP demos, version requirements.
• .agents/sglang-backend.md (new): agent one-pager covering the flat
  ServerArgs structure, the typed-vs-engine_args precedence, the
  speculative-decoding cheatsheet, and the mem_fraction_static gotcha
  documented above.
• AGENTS.md: index entry for the new agent doc.

Known limitation: the two Gemma 4 MTP gallery entries ship a recipe
that doesn't yet run on stock libraries. The drafter checkpoints
(google/gemma-4-{E2B,E4B}-it-assistant) declare
model_type: gemma4_assistant / Gemma4AssistantForCausalLM, which
neither transformers (<=5.6.0, including the SGLang cookbook's pinned
commit 91b1ab1f... and main HEAD) nor sglang's own model registry
(<=0.5.11) registers as of 2026-05-06. They will start working when
HF or sglang upstream registers the architecture -- no LocalAI
changes needed. The MiMo MTP demo and the non-MTP Gemma 4 paths work
today on this build (verified on RTX 5070 Ti, 16 GB).

Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Bash] [WebFetch] [WebSearch]
Signed-off-by: Richard Palethorpe io@richiejp.com

[richiejp]

OK, seems like it's good to go