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

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

# 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:

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. Thinking format: reasoning-format.md. Hub publish: 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.