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| Quant | Size | Notes |
|---|---|---|
Q4_0 | 6.5 GB | Recommended. QAT-matched: the base was quantization-aware-trained for the Q4_0 grid, so this 4-bit quant best preserves the original quality |
Q4_K_M | 6.9 GB | Standard K-quant 4-bit alternative |
Q8_0 | 11.8 GB | Near-lossless |
[!NOTE] This model derives from Google's QAT-Q4_0 checkpoint. Quantization-aware training was calibrated specifically for theQ4_0grid, soQ4_0is the quant that realizes the QAT advantage (≈bf16 quality at 4-bit).Q4_K_Mis a normal K-quant of the same weights and does not specifically leverage QAT.
| Metric | This model (v1.1) | Original |
|---|---|---|
| KL divergence | 0.32 | 0 (by definition) |
| Refusals, thinking on (adversarial harmful set) | ~22% | ~99% |
llama-cli -hf igorls/gemma-4-12B-it-qat-q4_0-unquantized-heretic-GGUF:Q4_0ollama run hf.co/igorls/gemma-4-12B-it-qat-q4_0-unquantized-heretic-GGUF:Q4_0gemma4) support.