# Arriella Fleet Whitepaper

**Scout · Growth · Flagship · Ascension — plus Grapevine multimodal & Model Interior Projection**

*Version 4.1 · July 2026*

**arXiv manuscripts (publication SoT):** [`../arxiv/`](../arxiv/) · submit via [`../arxiv/SUBMISSION.md`](../arxiv/SUBMISSION.md) → HF Papers after arXiv ID

---

## Read this first

Arriella is a **local training factory** that turns open instruct foundations into named small LMs you own on disk, plus a **Model Interior Projection (MIP)** viewer that makes inference inspectable in 3D — attention trails on a real parameter cloud, not a black-box chat box.

Organization: **Infinidev Corp** (Infinidev). Lead developers: **Beelzebub4888** and **Tcoder**.

### What problem we solve

1. Useful custom models on a consumer GPU (8 GB Blackwell, Windows)
2. **Role-scoped tiers** for field and business use — not one generic chatbot
3. Continuous improvement via **eat** (weight ingest / distill) and **grow** (post-deploy LoRA)
4. Stability knobs: enterprise base blend, capability/math/thinking anchors, gates before Ollama
5. A product nobody else ships: **live interior visualization of decode**
6. A separate **native multimodal** extension when text routing is not enough

### What “Arriella” means

| Sense | Meaning |
|-------|---------|
| Project | This repo — scripts, configs, `fleet/`, docs, MIP |
| Persona | System identity in datasets + chat |
| Dataset | `arriella_qa` (~90k Pile-derived Alpaca records) |
| Core products | `arriella-scout`, `arriella-growth`, `arriella-flagship`, `arriella-ascension` |
| Extension | `arriella-grapevine` (Omni multimodal — not a fifth core text model) |

Arriella is **not** a Meta/Alibaba/DeepSeek foundation model. Foundations are clay; products are what comes out of the factory.

---

## Product thesis — stable models for fields & business

Enterprise and field deployments fail when a single mid-size chat model is asked to be edge-fast, domain-deep, reason-heavy, and multimodal at once. Arriella ships **specialists with shared factory discipline**:

| Need | Route to | Why |
|------|----------|-----|
| Low VRAM / high throughput | Scout | Smallest core text tier |
| Domain QA / continuation | Growth | Trained on full Arriella QA pile |
| General instruction ops | Flagship | Heretic + premium teacher distill |
| Explicit step-by-step reasoning | Ascension | DeepSeek-R1 distill native think |
| Image / audio in the loop | Grapevine | Qwen2.5-Omni-3B student |

Reliability comes from **gates and anchors**, not slogans: capability / math / thinking / identity / fleet-spec datasets, optional enterprise base blend (~8–15% foundation retention), bakeoffs vs same-size stock instruct, and honest modality claims.

---

## Fleet tiers (Jul 2026 stance)

### Core text fleet (exactly four)

| Tier | Size (Ollama) | Foundation | Training path | Product stance |
|------|---------------|------------|---------------|----------------|
| **Flagship** | ~1.5B | Qwen2.5-1.5B-Instruct | Heretic → premium QLoRA distill | Primary quality / general ops |
| **Growth** | ~1.2B | Llama-3.2-1B-Instruct | LLaMA-Factory on `arriella_qa` | Primary mid / domain tier |
| **Ascension** | ~1.8B | DeepSeek-R1-Distill-Qwen-1.5B | LLaMA-Factory + Flagship catch-up | Reasoning / native think — **not** automatically smarter than Flagship |
| **Scout** | ~494M | Qwen2.5-0.5B-Instruct | Heretic → QLoRA | Present but **currently underpowered** — edge experiments, not demos |

Canonical numbers and truth rules: [`fleet/CORE_FLEET_SPEC.md`](../../fleet/CORE_FLEET_SPEC.md). Benchmarks: [../benchmarks/README.md](../benchmarks/README.md).

### Multimodal extension (not core text)

| Product | Foundation | Stance |
|---------|------------|--------|
| **Grapevine** | Qwen2.5-Omni-3B | Technical preview for vision/audio; modular runtime LoRA + mmproj. Inkling is a capability target only — no Inkling weights. |

Guide: [../guides/grapevine.md](../guides/grapevine.md). HF draft: [grapevine-hf.md](grapevine-hf.md).

---

## Training framework

```
foundation HF weights
    ├─ Path A (Scout / Flagship): Heretic abliteration → QLoRA distill → merge
    ├─ Path B (Growth / Ascension): LLaMA-Factory LoRA → merge
    └─ Path C (Grapevine): Omni Thinker QLoRA → merge / runtime adapter → GGUF + mmproj
           ↓
    fleet/<tier>/model/   (standalone HF)  |  fleet/inkling/ (Grapevine)
           ↓
    enterprise base blend (optional) + capability / fleet-spec anchors
           ↓
    optional eat (weight ingest / distill) + post-deploy grow
           ↓
    GGUF → ollama run arriella-*
```

**Heretic** abliterates refusal directions (not fine-tuning). Distillation then teaches what to say under QLoRA.

**Pufferfish** in this repo is the **long-run policy / WebUI preset layer** (`fleet_pufferfish.py`, `arriella_integration/`), not the upstream LightLM trainer as the primary engine.

Orchestrator: `scripts/train_fleet.py` + `fleet/fleet_manifest.yaml`.

---

## Eat & grow

After merge, capabilities improve by:

- **Weight ingest** — same-arch tensor merge (linear / TIES / DARE-TIES / …)
- **Distill grow** — cross-architecture teacher imitation (prefer larger teachers)
- **Organic grow** — post-deploy LoRA on anchors / datasets
- **Enterprise align** — base-preservation blend + capability re-eval (`fleet_enterprise_align.py`)

Honest language SoT: [../guides/eat-system.md](../guides/eat-system.md).

---

## MIP — Model Interior Projection

MIP loads a HF/Ollama-backed checkpoint, samples parameter space into ≤120k GPU display points, and during decode:

- Paints prompt-specific attention / residual activity on the cloud
- Traces a gold polyline (one node per generated token)
- Forges a deterministic projected **tesseract** from that trail
- Can **repeat** a prompt N times and **compare** solids (seed, tokens, geometry)

Guide: [../guides/mip-viewer.md](../guides/mip-viewer.md). Entry: `scripts/fleet_mip_web.py`.

This is the differentiator. Chat UIs show tokens. MIP shows **where** attention landed in the model’s own geometry.

---

## Multimodal (honest)

**Core text tiers** do not ingest raw pixels on 8 GB. Images go Gemma3 → categorized text → fleet model. Thinking/vision behaviors are **ingested** via teacher traces.

**Grapevine** is the native Omni path (text + image + audio in Ollama; video validated pre-export). Details: [multimodal.md](multimodal.md), [../guides/grapevine.md](../guides/grapevine.md).

---

## Competitive posture

- Compete with **same-size open instruct** checkpoints via bakeoffs.
- Do not claim frontier closed-model parity.
- Prefer measured tables in `docs/benchmarks/` over slogans.
- Prefer **role fit** (field/business job) over “one model to rule them all.”

---

## Publication (arXiv → Hugging Face Papers)

- Manuscript set: [`../arxiv/`](../arxiv/)
- Workflow: [`../arxiv/SUBMISSION.md`](../arxiv/SUBMISSION.md)
- Hub guide: [`../guides/hugging-face-publish.md`](../guides/hugging-face-publish.md)

Do **not** host papers as Spaces or model-only-markdown repos. Spaces are for runnable demos; Paper Pages require arXiv IDs.

---

## Document history

- v2.5 — fleet narrative without MIP as product centerpiece (`docs/archive/2026-07/WHITEPAPER.md`)
- v3.0 — reorganized docs; MIP as unique product; honest Scout status; three-tier only
- v4.0 — four-core text fleet + Grapevine extension; field/business thesis; HF paper prep