---
language:
- en
license: mit
library_name: little-gemma
tags:
- gemma4
- decision-head
- gguf
- voice-classroom
- turn-taking
---
# Gemma 4 classroom decision heads

Small CPU classifiers that map a Gemma 4 **final-prompt hidden vector** to
`speak`, `defer` or `wait`. These are auxiliary heads, not language models or
voice weights. They accompany the GGUF-based
[little-gemma v1.0 engine](https://github.com/cortexist/little-gemma/tree/v1.0)
and [little-gemma-tools v1.0 classroom](https://github.com/cortexist/little-gemma-tools/tree/v1.0).
The classifier training revision is **v2.1 (2026-10-05)**; it is independent of
the software's v1.0 version. This package replaces `gemma4-decision-head_v0.9`.

## Files

| File | Use |
| --- | --- |
| `gemma4-E4B-decision-head.gguf` | E4B native C runtime head |
| `gemma4-12B-decision-head.gguf` | 12B native C runtime head |
| `e4b/head.npz`, `e4b/contract.json` | E4B NumPy reference and feature contract |
| `12b/head.npz`, `12b/contract.json` | 12B NumPy reference and feature contract |
| `SHA256SUMS` | Checksums of release files |

The GGUFs are byte-identical to the validated v2.1 exports, with plain public
filenames. Their embedded contracts retain original training provenance.
In particular, `deployment_status` records export-time status, not current
runtime permission. E4B subsequently ran in the GGUF classroom; the supplied
12B revision is the latest offline-validated candidate, not a claim that every
live 12B deployment used it.

## Feature compatibility

| Head | Hidden width | Layer | Required backbone GGUF SHA-256 |
| --- | ---: | ---: | --- |
| E4B | 2560 | 42 | `df0fd4ee07072c607c29a0a1cb4f98918426cca12f45a2776bdd6ee6d09a4de3` |
| 12B | 3840 | 48 | `90fd44e29e0d7cffeb0fd00dc73cfdab9ed0b0e95306ecf7821ea634c940c370` |

Capture **after output normalization at the final prompt token, before any
generated-token forward**, without answer-channel seeding. The input contract
is `trio_sound_event_v2_staged_history` and task is `final_prompt-action`.
Extraction used little-gemma revision `c304f086a40c46ae4b67d5402738769dc12132a6`;
its serving support is included in v1.0. Matching dimensions alone is not enough:
check backbone hash, layer, normalization, prompt format, and capture boundary.
Other quantizations/backends, including EXL3, require separate validation or
refitting. Do not bypass feature-contract checks to force an incompatible model.

Each head is a three-seed ensemble of 64-unit tanh MLPs with standardized,
clipped inputs, mean logits and temperature softmax. Labels and numerical
parameters are stored in the package; use them rather than hardcoding order.
Training/dev/calibration support contains 5,751 authored examples, including
3,943 training rows across 37 training families. Probabilities are calibrated
only on authored calibration families, not on unconstrained live conversations.

## Use

```sh
hf download cortexist/gemma4-decision-head --local-dir decision-heads
cd decision-heads
sha256sum -c SHA256SUMS
```

Build the engine's `decision_head` shared library and `decision-head` CLI:

```sh
cmake -S little-gemma -B little-gemma/build -DCMAKE_BUILD_TYPE=Release
cmake --build little-gemma/build --target decision_head decision-head
little-gemma/build/decision-head inspect decision-heads/gemma4-E4B-decision-head.gguf
```

Configure the classroom's `turn_head.backend` as `c`, `library` as the absolute
shared-library path, and each `models` entry as a head `path` plus its SHA-256.
Use `mode: control`, `text_decision_tags: true` and `prompt_lane: v2_sound_event`
with the compatible `whisper_at_v2` acoustic adapter. The engine snapshots the
prompt feature, runs the CPU head, then applies the decision to the **same** turn.
The head itself does not synthesize audio, schedule speakers or detect overlap.
See [runtime configuration](https://github.com/cortexist/little-gemma-tools/blob/v1.0/script/deviceui/TURN_HEAD.md)
and [classroom setup](https://github.com/cortexist/little-gemma-tools/blob/v1.0/docs/trio-setup.md).

## Evaluation and limitations

| Authored panel | E4B | 12B |
| --- | ---: | ---: |
| Reserved invitation cases, three views | 360 / 360 | 360 / 360 |
| Previously exposed retention cases | 1,212 / 1,212 | 1,211 / 1,212 |
| Required-wait compact cases | 40 / 40 | 40 / 40 |
| Follow-up compact cases | 30 / 30 | 30 / 30 |

Views are correlated renderings, not independent real-world trials. The 360-row
panel contains 120 compact cases. The older retention set was exposed during
development and is not a fresh holdout. The 12B retention error is a teacher-role
case without sound metadata: a guest's own-name correction predicts **Speak**
instead of **Defer**. Guest-role retention views passed. A previously exposed
live E4B case was included in training and is not held-out evidence.

The original export check found identical C/NumPy choices on 6,111 vectors per
model. These results establish narrow authored classroom behavior and export
parity, not general conversational accuracy, interruption detection, live audio
robustness or end-to-end latency. Sound metadata in the authored evaluation is
synthetic; live ASR, identity, acoustics and prompt shifts can change outcomes.
No private hidden vectors, transcripts, voice recordings or raw training corpus
are distributed here.

## Voices and licenses

[Agent Voice](https://huggingface.co/cortexist/agent-voice) provides the public
guest voice separately. Users must choose **two additional voices** for teacher
and student, prepare phoneme timing and split `.enc.onnx`/`.dec.onnx` graphs,
and generate matching identity references. **Chey's voice is private and
closed-weight; it is not included or available for download.**

The decision-head package is MIT licensed. Gemma backbones, voice models and
external ASR/identity components retain their own terms; this license does not
relicense those artifacts.