# Fleet training, eat, and grow

## What the fleet is

**Four core text products** built from open instruct foundations, then trained and grown in-repo, plus a **multimodal extension**:

| ID | Dir | Path | Role |
|----|-----|------|------|
| `arriella-flagship` | `fleet/flagship-qwen15/` | Heretic → premium distill | General-purpose ops (primary demo) |
| `arriella-growth` | `fleet/growth-llama32/` | LLaMA-Factory on `arriella_qa` | Domain / instruction growth |
| `arriella-ascension` | `fleet/ascension-deepseek15/` | LLaMA-Factory + catch-up | Native DeepSeek-style reasoning |
| `arriella-scout` | `fleet/scout-qwen05/` | Heretic → QLoRA | Fast 0.5B (currently weak — see benchmarks) |
| `arriella-grapevine` *(extension)* | `fleet/inkling/` | Omni QLoRA + runtime LoRA | Vision / audio — [grapevine.md](grapevine.md) |

Manifest SoT: `fleet/fleet_manifest.yaml` (text tiers). Grapevine has a parallel toolchain under `scripts/*grapevine*` / `train_arriella_inkling_local.py`. Canonical identity sheet: `fleet/CORE_FLEET_SPEC.md`.

**Field / business routing:** pick the tier that matches the job (edge, domain QA, general ops, reasoning, multimodal). Stability comes from anchors + gates + optional enterprise base blend — not from pretending one model covers every workflow.

## Pipeline sketch

```
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
           ↓
    fleet/<tier>/model/   (standalone HF)
           ↓
    enterprise base blend (optional) + capability / fleet-spec anchors
           ↓
    optional eat (weight ingest / distill) + post-deploy grow
           ↓
    GGUF → ollama run arriella-*
```

## Common commands

```powershell
# Train / refresh from manifest (core text + stub tiers in YAML)
.\.venv\Scripts\python.exe scripts\train_fleet.py

# Post-deploy grow
.\.venv\Scripts\python.exe scripts\fleet_grow.py --help

# Eat plan + weight ingest (default eat set: Scout / Growth / Flagship)
.\.venv\Scripts\python.exe scripts\fleet_eat.py --plan
.\.venv\Scripts\python.exe scripts\fleet_weight_ingest.py --help

# Continuous eat loop (weight → distill → bakeoff)
.\.venv\Scripts\python.exe scripts\fleet_continuous_eat.py --max-cycles 1

# Enterprise base blend / retrain
.\.venv\Scripts\python.exe scripts\fleet_enterprise_align.py --blend-only

# Ascension catch-up toward Flagship
.\.venv\Scripts\python.exe scripts\fleet_ascension_catchup.py --help

# Export GGUF + refresh Ollama
.\.venv\Scripts\python.exe scripts\export_fleet_gguf.py
.\.venv\Scripts\python.exe scripts\refresh_fleet_ollama.py

# Size-tier bakeoff / capability suite
.\.venv\Scripts\python.exe scripts\fleet_bakeoff.py
.\.venv\Scripts\python.exe scripts\fleet_benchmark.py
```

Chat a deployed model:

```powershell
ollama list
ollama run arriella-flagship
ollama run arriella-growth
ollama run arriella-ascension
ollama run arriella-scout
ollama run arriella-grapevine
```

Details on honest “eat” language: [eat-system.md](eat-system.md). Thinking format: [reasoning-format.md](reasoning-format.md). Hub publish: [hugging-face-publish.md](hugging-face-publish.md).

## Model cards

Per-tier cards live next to weights:

- `fleet/scout-qwen05/MODEL_CARD.md`
- `fleet/growth-llama32/MODEL_CARD.md`
- `fleet/flagship-qwen15/MODEL_CARD.md`
- `fleet/ascension-deepseek15/MODEL_CARD.md`
- `fleet/inkling/MODEL_CARD.md` (Grapevine)

## Manifest stub tiers

`arriella-smol` and `arriella-qwen3b` appear in the manifest with LLaMA-Factory configs but are **not** currently in `ollama list` and have no merged weights. Treat as future capacity, not shipped products.