KronQ: LLM Quantization via Kronecker-Factored Hessian
Paper • 2607.07964 • Published • 33
Paper: arXiv:2607.07964 · Code: GitHub
Llama-2-70b-hf quantized to 4-bit weights / 16-bit activations with KronQ (Kronecker-factored Hessian quantization). Weights are stored packed int4 (~33 GB); a fused dequant + bidirectional-incoherence (BiIP) CUDA kernel unpacks them on the fly.
Perplexity: 3.40
Zero-shot accuracy:
| PIQA | ARC-E | ARC-C | HellaSwag | WinoGrande | BoolQ | OBQA | Average |
|---|---|---|---|---|---|---|---|
| 82.32 | 80.26 | 57.85 | 83.81 | 79.87 | 85.38 | 49.20 | 74.10 |
(lm-evaluation-harness, 0-shot. acc_norm for PIQA/HellaSwag/ARC/OBQA, acc for WinoGrande/BoolQ.)
KronQ-packed checkpoint. Load with the KronQ runtime:
python eval_pretrained.py meta-llama/Llama-2-70b-hf donghyunli/Llama-2-70b-KronQ-W4A16 --ppl --zs
Per-channel asymmetric W4, weight-only (a_bits=16), --alpha 0.25, BiIP, act_order, raw H_G. 128 WikiText-2 calibration sequences.
Derivative of Llama-2-70b-hf — llama2 license.
Base model
meta-llama/Llama-2-70b-hf