# Arriella Scout

Fast 0.5B edge tier · Heretic + QLoRA — **currently underperforming**

| Field | Value |
|-------|-------|
| **Fleet ID** | `arriella-scout` |
| **Ollama** | `ollama run arriella-scout` |
| **Parameters** | ~494M (Ollama) |
| **Foundation (clay only)** | `Qwen/Qwen2.5-0.5B-Instruct` |
| **Merged weights** | `fleet/scout-qwen05/model` |
| **Demo priority** | **Low** — do not lead investor / product demos |
| **Business role** | Edge / low-VRAM routing (when recovered) |

## Honest status (Jul 2026)

Scout still **loads** in Ollama and MIP and remains part of the **four-core text fleet**, but live quality is weak relative to Growth/Flagship. Treat as an edge experiment until a focused recover + gate pass. Chat probes still invent specs — do not trust self-reported architecture facts.

## Description

Custom-trained (not stock Qwen). Path: Heretic abliteration → distillation → merge → eat/grow. Role intent: lowest VRAM / highest throughput routing tier.

## Features (design)

- Smallest VRAM footprint in the core four
- Same thinking / vision-routing plumbing as siblings
- Useful as a **MIP contrast** (tiny param cloud) even when answers lag

## Out of scope

- Leading demos as “the Arriella model”
- Claiming capability-gate PASS without a fresh green report

## Benchmarks

See [docs/benchmarks/README.md](../../docs/benchmarks/README.md).

## Hub card stub

[`docs/papers/hf-cards/scout.md`](../../docs/papers/hf-cards/scout.md)

## Recover path

```powershell
.\.venv\Scripts\python.exe scripts\fleet_eat.py --plan
.\.venv\Scripts\python.exe scripts\fleet_grow.py --help
.\.venv\Scripts\python.exe scripts\fleet_benchmark.py
```