Qwen3-style chat templates append the opening <think> tag to the *prompt*
when thinking is enabled. The model therefore never generates it and emits
only the reasoning text plus the closing </think>.
sglang's ReasoningParser keys off the opening tag:
in_reasoning = self._in_reasoning or self.think_start_token in text
if not in_reasoning:
return StreamingParseResult(normal_text=text)
so with such a template the entire completion — reasoning and answer, the
raw </think> in between — is returned as content and reasoning_content
stays empty, no matter how reasoning_parser is configured.
sglang's own OpenAI server handles this via
force_reasoning = (self.template_manager.force_reasoning
or self._get_reasoning_from_request(request))
This backend has no template manager, so derive the same signal from the
rendered prompt: if it ends with the detector's think_start_token, the tag
was prefilled and the parser is constructed with force_reasoning=True.
Structured decoding is the exception, and it matters: a grammar applies
from the first token, so the model cannot emit the closing tag even though
the template opened the block. The whole completion is schema output and
belongs in content — forcing there files it as reasoning and returns an
empty answer. Measured against a JSON-schema code audit: 10107 characters
of "reasoning", zero content. sglang's own server keeps the two apart for
the same reason; its grammar backend owns the reasoning prefix when a
reasoning parser is configured.
force_reasoning is only passed when it is meant to be True, so detector
defaults (DeepSeek-R1 already defaults to True) are untouched, and a
prompt without a prefilled tag behaves exactly as before — which matters,
because forcing unconditionally makes an answer generated with thinking
off disappear into reasoning_content.
The construction is factored into _new_reasoning_parser() so the streaming
and non-streaming paths, which previously built the parser separately,
cannot drift apart.
Signed-off-by: pos-ei-don <1822533+pos-ei-don@users.noreply.github.com>
#11772 exempted Temperature from the zero-filter in both backend adapters,
because proto3 has no field presence and an explicit 0 is indistinguishable
from "unset". Seed has exactly the same property and is still filtered:
if proto_field != "Temperature" and value in (None, 0, 0.0, [], False, ""):
continue
A caller pinning `"seed": 0` for a reproducible run therefore gets a random
seed instead, with no error and no log line — the one case where the failure
is invisible precisely because the request looked deliberate.
Both adapters now share a named tuple of fields whose zero is meaningful, so
the next one is added in one place rather than as a second special case.
Deliberately left filtered: top_k, top_p, min_p and the penalties. Their zero
is not a value a caller means — sglang disables top_k with -1, not 0, so
forwarding 0 there would turn a default into an invalid argument.
Verified on the sglang backend (Qwen3.5-MoE, arm64): with the temperature fix
alone, two identical requests at temperature 0 are byte-identical, but pinning
seed 0 has no effect until this change.
Signed-off-by: pos-ei-don <1822533+pos-ei-don@users.noreply.github.com>
Add complete vLLM and SGLang entries with their exact tool parsers. Preserve an explicit zero temperature in both backend adapters.
Assisted-by: Codex:gpt-5
Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
Those backends only forwarded the flag when it was "true", so "false"
never reached apply_chat_template and Qwen3 kept thinking on.
Signed-off-by: lei_lei <96427312+leilei3167@users.noreply.github.com>
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 + l4t13 paths are 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; new pyproject.toml-driven
l4t13 install path so [tool.uv.sources] can pin torch/torchvision/
torchaudio/sglang to the jetson-ai-lab index without forcing every
transitive PyPI dep through the L4T mirror's flaky proxy (mirrors the
equivalent fix in backend/python/vllm/install.sh).
* backend/python/sglang/pyproject.toml (new): L4T project spec with
explicit-source jetson-ai-lab index. Replaces requirements-l4t13.txt
for the l4t13 BUILD_PROFILE; other profiles still go through the
requirements-*.txt pipeline via libbackend.sh's installRequirements.
* backend/python/sglang/requirements-l4t13.txt: removed; superseded
by pyproject.toml.
* 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>
pos-ei-don