SetDefaults injected the llama.cpp server options cache_reuse
(ApplyServingDefaults) and parallel (ApplyHardwareDefaults, re-applied
per selected node by the distributed router) onto every model config
regardless of backend. Every other backend ignores options it does not
understand, so this was harmless until longcat-video, which strictly
validates its options and fails LoadModel with
"unknown model option(s): cache_reuse, parallel".
Gate both injections behind a new UsesLlamaCppServingOptions allow-list
(llama-cpp plus the empty/auto-detect case that resolves to llama.cpp
from a GGUF file, mirroring how llamaCppDefaults is registered). This
follows the existing UsesLlamaSamplerDefaults precedent for llama-only
defaults. The typed NBatch field is deliberately left alone: it is a
proto field every backend simply ignores, which is why batch never
triggered the error.
Also harden the longcat-video backend to warn-and-ignore unknown model
options and request params through a testable select_known_options
helper, matching the other LocalAI Python backends, so a future
server-injected option cannot break loading again.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* feat(config): enable cross-request prefix caching for serving (Phase 2)
The llama.cpp backend ships n_cache_reuse=0 (cross-request KV prefix reuse via
shifting disabled). Enable it by default (256) so repeated prefixes - system
prompts, RAG context, agent scaffolds, multi-turn chat - aren't recomputed. This
is the universally-useful part of 'paged attention' (shared-prefix reuse, which
the upstream maintainers themselves identify as where paged attn actually helps)
and needs none of the block-KV machinery.
Lives in a serving_defaults.go sibling to hardware_defaults.go (device-driven vs
serving-policy defaults); both run from SetDefaults and only fill unset values.
Explicit cache_reuse/n_cache_reuse always wins. Device-independent, so it
propagates to distributed nodes via the model options with no router change.
Shares the backendOptionSet helper with the Phase-1 parallel default.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactor(config): extract generic fallback defaults into ApplyGenericDefaults
Behavior-preserving: move the inline sampling-param + runtime-flag fallbacks out
of SetDefaults into ApplyGenericDefaults, completing the domain-grouped tiers
(ApplyInferenceDefaults=family, ApplyHardwareDefaults=device, ApplyServingDefaults
=serving, ApplyGenericDefaults=generic fallbacks). SetDefaults is now a clean
orchestrator. Same order (runs after the family/hardware/serving tiers so those
win) and same conditions (TopK gated on UsesLlamaSamplerDefaults, MMap on XPU).
No behavior change; full config suite green. (NGPULayers stays in the GGUF-read
path for now - it's device-driven but coupled to model-size detection; a separate
follow-up.)
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
