StyleTune for your 12B QAT model — without training anything.

Gemma-4-12B-QAT-StyleTune-Voice

Google's QAT base + Gryphe's 12B StyleTune voice. Best of both. One 2.0 GB download. Zero extra models.

This voice is cast from Gryphe/Gemma-4-12B-StyleTune — 56% fewer clichés (1.050 → 0.463 per 100 words), 16.8% shared trigram vocabulary vs base instruct — and matched to Google's QAT checkpoint, google/gemma-4-12B-it-qat-q4_0-unquantized. If your GGUF is built from the QAT weights, this is the variant that fits it exactly.

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Why QAT + StyleTune is the good combo

Two independent optimizations, now combined:

Google's QAT trains the model to survive quantization. Per Google, QAT gives 54% less perplexity drop at Q4_0 versus standard post-training quantization (Google Developers Blog) — 5,000 steps of fake-quantized forward passes with KL divergence to the BF16 teacher. Your Q4_0 GGUF keeps noticeably more of the original model's fidelity — and at 12B, every bit of fidelity counts double.

StyleTune then changes how it writes: per Gryphe's benchmarks — 200 diverse roleplay prompts, greedy 0.0 — 56% fewer clichés with only 16.8% shared vocabulary vs the base instruct.

The old tradeoff was: QAT model or StyleTune model, pick one. Now you don't pick.

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Two steps

# 1. Get the voice tool (one-time): https://huggingface.co/Wiself/voice
python3 voice.py path

# 2. Cast onto your QAT-derived GGUF
voice cast ./gemma-4-12b-it-qat-q4_0-Q4_0.gguf voice.safetensors --out ./voiced/gemma-4-12b-qat-styletune.gguf

Run it:

llama serve -m ./voiced/gemma-4-12b-qat-styletune.gguf --jinja

No QAT finetune was needed. No extra model was downloaded.

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Why the QAT-matched variant exists

The QAT checkpoint's lm_head weights differ slightly from the standard instruct's (that's the point of QAT — the weights learned to live with quantization). This voice is a delta against the normal google/gemma-4-12B-it (voice − base where base is the standard instruct, not QAT). When you later do delta + QAT_head → Q8_0, you add StyleTune's style to the QAT head — correct math, matched foundation.

Your GGUF is built from	Use
google/gemma-4-12B-it (standard)	12B voice
google/gemma-4-12B-it-qat-q4_0-unquantized	this voice

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If it loops on abliterated targets

On some abliterated targets a direct cast can loop. The fix is the delta path (base is the normal instruct — correct math):

voice delta voice.safetensors --base google/gemma-4-12B-it
voice cast ./model.gguf delta-voice.safetensors --out ./voiced/model.gguf

This is the exact path that fixed looping on the 26B QAT abliterated model — same recipe, 12B scale. Full story in the Voice tool card.

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What's inside

• voice.safetensors — the style delta, F16, shape [262144, 3840], ~2.0 GB
• voice.json — metadata: source, dtype, shape, base (google/gemma-4-12B-it)

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Compatibility

Target	Works?
QAT-derived Gemma 4 12B GGUFs (any quant)	✅ primary target
Abliterated QAT 12B variants	✅ via the delta path above
Standard (non-QAT) 12B GGUFs	✅ works, but the 12B voice is the exact match
Gemma 4 other sizes / non-Gemma	❌ shape mismatch — use the matching voice

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Notes

• Casting quantizes only the head to Q8_0 (near-lossless); every other tensor is byte-copied from your model — your QAT weights stay QAT weights.
• Sampler tips from Gryphe: temp 1.0, MinP 0.10, DRY sampler on. Gemma 4's native chat template applies automatically.
• Verify: voice info voice.safetensors → delta marker, [262144, 3840] · F16.
• Family: 12B voice · 26B A4B V2 voice · 26B A4B QAT voice · 31B voice · 31B QAT voice · 12B QAT voice (this repo)

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References & Credits

• QAT: Google Developers Blog — Gemma 3 Quantization-Aware Training — 54% less perplexity drop at Q4_0; checkpoint google/gemma-4-12B-it-qat-q4_0-unquantized, Gemma terms.
• StyleTune: Gryphe/Gemma-4-12B-StyleTune — 56% cliché reduction, 16.8% shared trigram vocabulary; Anthracite, Latitude.
• Tool: Voice — lift a voice, cast it onto any compatible GGUF.