Transformers
English
arriella
infinidev
documentation
technical-report
model-card
local-llm
not-for-inference
Instructions to use UnaverageTech411/arriella-docs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UnaverageTech411/arriella-docs with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UnaverageTech411/arriella-docs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,465 Bytes
61cf0ce | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | # Multimodal architecture & growth
**Version 1.2 · July 2026**
## Summary
Arriella supports multimodal work in **two different ways**:
1. **Routed vision (core text tiers)** — image paths → Gemma3 description → Scout / Growth / Flagship / Ascension
2. **Native Omni (Grapevine extension)** — Qwen2.5-Omni-3B student with vision/audio (and pre-export video) — [grapevine.md](../guides/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`](../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](../guides/grapevine.md), root [`grapevine.md`](../../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):
```powershell
.\.venv\Scripts\python.exe scripts\fleet_organic.py --id arriella-growth --multimodal
```
## Formats
See [../guides/reasoning-format.md](../guides/reasoning-format.md).
|