Odette's voice, matched to your QAT model — without training anything.

Odette-26B-A4B-QAT-Voice

Odette's chorus, fitted to QAT-derived GGUFs. Same three voices, correct math for QAT heads.

This carries the Odette chorus — Dark Scarlett pulse, MeroMero support at 0.8, Pantheon Reasoning lead at 0.9 — as a delta against the standard instruct, matched to Google's QAT checkpoint (google/gemma-4-26B-A4B-it-qat-q4_0-unquantized). If your GGUF is built from QAT weights, use this one; otherwise the standard voice is the exact fit.

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QAT + Odette

Two separate things, stacked:

Google's QAT trains the model to survive quantization — 54% less perplexity drop at Q4_0 versus standard post-training quantization (Google Developers Blog).

Odette is three grafted voices thinking, swooning, and desiring through one head. If you already run a QAT GGUF, this variant keeps the QAT foundation intact instead of overwriting it with standard-instruct weights.

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Two steps — any QAT-derived Gemma 4 26B A4B GGUF you already have

# 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-4-26b-a4b-it-qat-q4_0-Q4_0.gguf voice.safetensors --out ./voiced/gemma-4-26b-a4b-qat-odette.gguf

Append --speak to force the voice through output.weight (invented if the target lacks it and geometry survives; GGUF only).

Run it:

llama serve -m ./voiced/gemma-4-26b-a4b-qat-odette.gguf --jinja

No QAT finetune was needed. No extra model was downloaded. Your model, three voices, less quantization loss.

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 cast as a delta against the normal google/gemma-4-26B-A4B-it (voice − base where base is the standard instruct, not QAT). When you later do delta + QAT_head → Q8_0, you add Odette's chorus to the QAT head — correct math, matched foundation.

Your GGUF is built from	Use
google/gemma-4-26B-A4B-it (standard)	Odette voice — direct cast, one step
google/gemma-4-26B-A4B-it-qat-q4_0-unquantized	this voice — direct cast onto your QAT GGUF
Abliterated/looping targets (either base)	Delta path — see standard card, same voice delta recipe works here

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What's inside

• voice.safetensors — the voice delta (voice − base), source dtype preserved, arch-derived shape [262144, 2816], ~1.5 GB
• voice.json — metadata: source, dtype, shape, base

Confirm after download: voice info voice.safetensors → [262144, 2816] (delta marker present).

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Compatibility

Target	Works?
QAT-derived Gemma 4 26B A4B GGUFs (any quant)	✅ primary target
Abliterated/looping targets	Delta path — same voice delta recipe as the standard card
Standard (non-QAT) 26B A4B GGUFs	✅ works, but the Odette voice is the exact match
Gemma 4 other sizes / non-Gemma	❌ shape mismatch

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Notes

• Sampler tips from the source cards: temp 1.0 across all three; Pantheon adds MinP 0.05 and no repetition penalty (it corrupts thinking traces); Scarlett suggests Top-P 0.92; thinking ON at medium effort.
• Usage terms carry over: Scarlett's donors are 18+, personal use only, you accept full responsibility — see the source card before sharing voiced models. All three sources are Apache-2.0 under Gemma terms.
• Casting quantizes only the head to Q8_0 (near-lossless); every other tensor is byte-copied — your QAT weights stay QAT weights.

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References & Credits

• Chorus: Odette-26B-A4B-Voice — Scarlett pulse, MeroMero support at 0.8, Pantheon lead at 0.9 (DELLA, seed 42).
• QAT: Google Developers Blog — checkpoint google/gemma-4-26B-A4B-it-qat-q4_0-unquantized, Gemma terms.
• Tool: Voice — lift a voice, cast it onto any compatible GGUF.