Multimodal architecture & growth
Version 1.2 · July 2026
Summary
Arriella supports multimodal work in two different ways:
- Routed vision (core text tiers) — image paths → Gemma3 description → Scout / Growth / Flagship / Ascension
- Native Omni (Grapevine extension) — Qwen2.5-Omni-3B student with vision/audio (and pre-export video) — grapevine.md
Core text tiers also support:
- Thinking — Ollama
--think↔ Arriella/think…/answer(or<scratchpad>/<answer>); Ascension uses DeepSeek-native think - Post-deploy growth — artifact ingest + organic LoRA grow
Certification notes and older wording live in ../archive/2026-07/FLEET_MULTIMODAL_PAPER.md. This page is the maintained version.
Architecture A — routed (core text)
User image ──► Gemma3 (Ollama) ──► categorized description
│
User prompt ──► Arriella text tier
think=true → scratchpad / thinking channel
answer body → user
Text tiers never see raw pixels on this workstation’s VRAM budget. Vision is behavior absorbed via vision_teacher_anchor distill grows — not by stuffing a VLM into 0.5–1.8B weights.
Thinking is absorbed similarly from family-matched teachers (qwen3:0.6b, lfm2.5-thinking:1.2b, deepseek-r1:1.5b, etc.).
Architecture B — native Omni (Grapevine)
User text / image / audio ──► Qwen2.5-Omni-3B (+ Arriella LoRA + mmproj)
↓
Ollama arriella-grapevine
Grapevine is a fleet extension, not a fifth core text model. Inkling is a capability target only. Details and smoke vs Flagship: grapevine.md, root grapevine.md.
Growth paths
| Path | Script / entry | Use |
|---|---|---|
| Artifact ingest | fleet_artifact_ingest.py |
ollama: / lora: / hf: teachers |
| Organic grow | fleet_organic.py, fleet_grow.py |
Datasets / multimodal anchors |
| Grapevine repair | prepare_grapevine_*, train_arriella_inkling_local.py |
Omni identity / deployment repair |
Example (text tier):
.\.venv\Scripts\python.exe scripts\fleet_organic.py --id arriella-growth --multimodal