---
license: apache-2.0
base_model: unsloth/gemma-4-E2B-it
language:
- en
tags:
- gemma-4
- on-device
- multimodal
- scam-detection
- lora
- cactus
- int8
---

# Keelhaul — Cactus INT8 Adapter Weights

Five fine-tuned multimodal LoRA adapters for **on-device scam detection** on Gemma 4 E2B, packaged for the [Cactus](https://github.com/cactus-compute/cactus) on-device inference runtime.

This is the model-asset companion to the Keelhaul Android app —
submission for the **Gemma 4 Good Hackathon** (Safety & Trust + Unsloth +
LiteRT + Cactus tracks, deadline 2026-05-18).

Code: https://github.com/BrianIsaac/keelhaul

## What is shipped

| Tarball | Modality | Role | Size INT8 |
|---|---|---|---:|
| `keelhaul_text_triage_v2.tar.gz` | text | Single-message scam / benign classification with phase + technique tagging | ~4.7 GB compressed (5.6 GB extracted) |
| `keelhaul_text_reasoner_v4.tar.gz` | text | Trajectory-window reasoning: tier (safe / watching / concerned / alert) + phase + technique stack + grounded `evidence: [{turn, role}]` + 2-4-sentence reasoning | ~4.7 GB compressed |
| `keelhaul_vision_v2.tar.gz` | vision | Scam-imagery classification: label + `intent_class` (e.g. `fake_document`, `stolen_persona`, `crypto_scam_visual`) + synthesis cues + region hint | ~4.7 GB compressed |
| `keelhaul_audio_v2.tar.gz` | audio | Voice-scam classification: label + `intent_class` (e.g. `authority_impersonation`, `account_takeover_demand`) + vocal cues + weakest segment | ~4.7 GB compressed |
| `keelhaul_video_v6.tar.gz` | video | Deepfake / synthetic-video detection on 4-keyframe + face-crop input; feeds into a runtime video composer alongside vision + audio | ~4.7 GB compressed |

Each tarball expands to a Cactus model directory: 1959 `.weights` files
+ `config.txt` + `tokenizer.json` + `tokenizer_config.json`.

## Quantisation

INT8 via Cactus's CLI converter. Per-group symmetric quantisation, group
size 32, FP16 scales. The multimodal projection layers and the synthetic
post-projection norm are kept at FP16 by design (1674 FP16 tensors,
278 INT8-quantised tensors per model).

**Quantisation quality** (averaged across all 5 conversions):

| Metric | Value |
|---|---:|
| MSE (mean) | 3.04 × 10⁻⁶ |
| SNR (mean) | 44.7 dB |
| Cosine similarity (mean) | 0.999936 |

Why INT8 not INT4: on-device validation revealed the vision_v2 and
video_v6 multimodal projection layers degraded reproducibly under INT4
weight quantisation (truncated JSON output, label-vs-intent_class
inconsistency on held-out scam imagery). INT8 uses the same SMMLA
hardware kernel path on Snapdragon 8 Gen 3 — same decode speed as INT4
— and restores near-lossless accuracy at ~2× the on-disk size. The full
migration narrative is in
[`docs/submission/cactus_models_manifest.md`](https://github.com/BrianIsaac/keelhaul/blob/main/docs/submission/cactus_models_manifest.md)
in the code repo.

## On-device performance (Honor Magic V3, Snapdragon 8 Gen 3)

- ~70 prefill tokens/sec
- ~5 decode tokens/sec (multimodal inputs are heavier than pure text)
- ~2 s TTFT
- ~3 GB resident per loaded model (28 GB on-disk for all 5; one model
  mmap'd at a time)

## Training

All five adapters fine-tuned via Unsloth FastModel on Kaggle T4 × 2
from `unsloth/gemma-4-E2B-it`. Held-out evaluation deltas (zero-shot
base → fine-tuned, 95 % bootstrap CI):

| Modality | n_eval | Label / tier ZS → FT | Δ |
|---|---|---|---:|
| text_triage_v2 | 563 | F1 0.520 → 0.757 | **+0.237** ★ |
| vision_v2 | 270 | F1 0.442 → 0.976 | **+0.535** ★ |
| audio_v2 | 140 | F1 0.896 → 0.951 | **+0.055** ★ |
| reasoner_v4 | 186 | tier acc 0.581 → 0.930 | **+0.349** ★ |
| video_v6 | 61 | F1 0.000 → 0.81 | **+0.81** |

13/13 metrics statistically significant at 95 % bootstrap CI. Full
scorecards + per-class breakdowns under
[`training/reports/v2/`](https://github.com/BrianIsaac/keelhaul/tree/main/training/reports/v2)
in the code repo.

## Usage on-device

The Keelhaul Android app downloads tarballs on first use of each
modality and caches them at
`/data/data/com.keelhaul.detector/files/cactus_models/<id>/`. From there
`cactus_init(dir.path, null, false)` loads them into the Cactus runtime;
the app's `CactusPipeline.completeRaw` hot-swaps modalities on demand.

## Limitations

- audio_v2 produced one observed false positive on a held-out benign
  LibriSpeech clip during on-device validation.
- video_v6 exhibited a pair inversion on the specific real-vs-fake
  samples used during demo capture; the binary deepfake signal is
  correct in aggregate (held-out F1 0.81) but is the noisiest of the
  five adapters.

## Licence

Apache-2.0. Base model
[`unsloth/gemma-4-E2B-it`](https://huggingface.co/unsloth/gemma-4-E2B-it)
is subject to Google's Gemma terms — please review before redistribution.

## Citation

```
Keelhaul: on-device multimodal scam detection on Gemma 4 E2B
Brian Isaac, 2026
Gemma 4 Good Hackathon submission
```