apiVersion: lumbridge/v1
kind: Scene
metadata:
  name: qwen38-dspark
  version: 1
  description: "qwen38-mia tuning with the DSpark speculative stack (code/tool-call optimised)."
  author: karti
  tags: [brain, vision, voice, sglang, dspark, benchmark]

# Same model set and same MiaAI-Lab tuning as `qwen38-mia`; only the
# speculative stack differs (EAGLE/MTP -> DSpark) plus torch.compile and
# continuous-decode-steps 2.
#
# Benchmark with MiaAI-Lab's own net-decode harness, NOT the prose `performance`
# suite: python3 /tmp/ndec_ours.py
#   qwen38-mia (EAGLE) baseline: code 31.60 / prose 23.97 tok/s
#   MiaAI-Lab reports for DSpark: code 51.5 / prose 18.3
#
# DSpark is faster on code/math/tool-calls and SLOWER on free-form prose, so
# check both probes before adopting — the squawk/news path is prose.
#
# Same swap discipline as every other scene: kill stale `scene activate`,
# deactivate, wait for free -g >= 110 GB, activate, then activate again to
# persist desired.
models:
  - brain-qwen38-dspark
  - ears
  - voice-vox
  - voice

budget_gb: 100
activation:
  order: listed
  wait_healthy: true