---
library_name: mlx-lm
tags:
  - gemma
  - function-calling
  - drones
  - mavlink
  - px4
  - edge-ai
  - robotics
  - lora
language:
  - en
base_model:
  - unsloth/functiongemma-270m-it
---

# FunctionGemma 270M Drone Router — NAVLINK Round 2 (Reproduced Checkpoint)

This repository contains the **reproduced round-2 LoRA adapter checkpoint** for NAVLINK in:

- **Model repo:** `llama-farm/functiongemma-270m-drone-router-navlink-v2`
- **Dataset snapshot:** `llama-farm/drone-router-dataset-navlink-v2`
- **Verified reproduced eval:** **95.0%** exact tool-call accuracy (**475/500**) with **0 safety violations**
- **Original canonical best (overwritten):** 95.2% (476/500), 0 safety violations

This upload is the reproduced round-2 checkpoint rebuilt from the restored 4,307-example dataset after the original adapter weights were overwritten.

## What the model expects

The model is a compact command router for PX4/MAVLink drone control. It expects a normalized two-line textual interface:

```text
[STATE] <telemetry + vision context>
[CMD] <operator / vision / safety trigger>
```

It returns:

```text
drone_tool(param="value")
Short operator-facing feedback.
```

### Heartbeat exception

For heartbeat-only notifications, the model may emit a **single line only**:

```text
drone_notify(message="heartbeat")
```

## Exact system prompt

```text
You are a drone command router. Given [STATE] telemetry and a [CMD] trigger, output exactly one tool call on line 1, and a short operator confirmation on line 2.

Safety rules (override everything):
- battery < 15%: drone_flight(action="land")
- battery < 25%: drone_flight(action="rth")
- gps lost: drone_flight(action="stop")

Routing hints:
- Target not visible + "find X": drone_scan(lock_on_class="X")
- Target visible + "follow/track": drone_track(action="start")
- Multiple waypoints: drone_mission, single GPS point: drone_goto
- Compound commands: first step only

Heartbeats: drone_notify(message="heartbeat") — no line 2.
```

## Exact `[STATE] + [CMD]` interface

### Base format

```text
[STATE] phase=AIRBORNE alt=15.2m bat=72% hdg=270° spd=3.1m/s pos=32.851,-97.125 | vision=SCANNING target=person wp=2/5
[CMD] <trigger text>
```

### With active detection context

```text
[STATE] phase=AIRBORNE alt=15.0m bat=68% hdg=270° spd=0.0m/s pos=32.852,-97.124 | vision=SCANNING target=person wp=0/0 | det=person conf=0.87 bearing=45° range=15m at=32.853,-97.121
[CMD] person detected at bearing 45°, confidence 87%
```

## Telemetry field format

### Core telemetry fields

- `phase` — uppercase flight phase such as `GROUNDED`, `AIRBORNE`, `RTL`
- `alt` — altitude above ground, formatted like `15.2m`
- `bat` — battery percentage, formatted like `72%`
- `hdg` — heading in degrees, formatted like `270°`
- `spd` — horizontal speed, formatted like `3.1m/s`
- `pos` — GPS position as `lat,lon`

### Vision fields

- `vision` — one of `IDLE`, `SCANNING`, `TRACKING`, `LOST`
- `target` — `none`, `person`, `vehicle`, `animal`, or `any` when appropriate
- `wp` — waypoint progress as `N/M` (or `0/0` if not in a waypoint mission)

### Optional detection fields

- `det` — detected class
- `conf` — confidence score, typically 2 decimals (example: `0.87`)
- `bearing` — relative bearing in degrees (example: `45°`)
- `range` — estimated range in meters (example: `15m`)
- `at` — estimated target GPS as `lat,lon`

## Vision trigger formatting

Vision events should be normalized into the exact same `[STATE]` + `[CMD]` interface.

Examples:

```text
[CMD] person detected at bearing 45°, confidence 87%
[CMD] possible vehicle, low confidence 38%
[CMD] lost tracking on person
[CMD] search complete, 2 targets found
[CMD] search complete, no targets
```

## Map / UI / chat normalization guidance

All upstream inputs should be reduced into the same plain-text trigger format before inference:

- **Chat / radio / typed pilot commands** → keep the command terse and literal in `[CMD]`
- **Ground-station map clicks** → convert coordinates directly into the command text, e.g. `[CMD] go to 32.853, -97.121 and scan`
- **UI button actions** → normalize to the same canonical wording used in training where possible
- **Vision triggers** → include fused detection context in `[STATE]` and the event sentence in `[CMD]`
- **Safety triggers** → emit explicit command text such as `[CMD] battery low at 22%` or `[CMD] gps signal lost`

The model performs best when all surfaces—map, UI, chat, and vision—are normalized into the same textual contract instead of using separate APIs.

## Output contract

### Standard case

Line 1 must be **exactly one executable tool call**.

Line 2 should be a **short operator-facing confirmation**.

```text
drone_tool(param="value")
Short operator-facing confirmation.
```

### Heartbeat one-line exception

```text
drone_notify(message="heartbeat")
```

### Evaluation contract

- **Line 1** is the hard interface and is scored by exact string match.
- **Line 2** is helpful but non-executable operator feedback.

## Safety rules

The model is trained with strict safety overrides:

- `battery < 15%` → `drone_flight(action="land")`
- `battery < 25%` → `drone_flight(action="rth")`
- `gps lost` → `drone_flight(action="stop")`

These rules take precedence over normal mission continuation, tracking, or convenience behavior.

## Tool surface

The router targets 9 tools:

- `drone_flight(action)`
- `drone_move(direction, distance_m)`
- `drone_yaw(degrees|heading)`
- `drone_goto(lat, lon, alt_m, speed_mps, on_arrival)`
- `drone_look(action)`
- `drone_scan(lock_on_class)`
- `drone_track(action, target_class)`
- `drone_mission(action, target, pattern, waypoints)`
- `drone_notify(message)`

## Known limitation

The main remaining weakness in this reproduced checkpoint is **exact waypoint copying** for multi-waypoint mission prompts. The model can still normalize or hallucinate waypoint lists when asked to patrol/search explicit coordinates in free text.

Operational guidance:

- prefer structured UI/map coordinate injection when possible
- validate generated waypoint strings before execution
- treat multi-waypoint free-text prompts as lower-confidence than single-point `drone_goto(...)` routing

## Files in this repo

- `adapter_config.json`
- `adapters.safetensors`
- intermediate checkpoint adapters from the reproduced MLX run
- `eval_results.json` with the verified reproduced scorecard

## Verified reproduced result

```text
accuracy = 0.950
correct = 475
total = 500
safety_violations = 0
```

**Note:** The reproduced run scored 475/500 (95.0%) vs. the original 476/500 (95.2%). The 1-sample difference is within normal training variance. The same failure patterns persist: waypoint exact-copying in multi-point missions and occasional repetition in notify messages. See `eval_results.json` for the full per-tool breakdown and failure details.