Arriella Fleet Whitepaper

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

Version 4.1 · July 2026

arXiv manuscripts (publication SoT): ../arxiv/ · submit via ../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

Useful custom models on a consumer GPU (8 GB Blackwell, Windows)
Role-scoped tiers for field and business use — not one generic chatbot
Continuous improvement via eat (weight ingest / distill) and grow (post-deploy LoRA)
Stability knobs: enterprise base blend, capability/math/thinking anchors, gates before Ollama
A product nobody else ships: live interior visualization of decode
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. Benchmarks: ../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. HF draft: 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.

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. 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, ../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/
Workflow: ../arxiv/SUBMISSION.md
Hub guide: ../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