KronQ: LLM Quantization via Kronecker-Factored Hessian
Paper • 2607.07964 • Published • 33
Paper: arXiv:2607.07964 · Code: GitHub
Llama-2-7b quantized to 4-bit weights / 16-bit activations, group size 128, with KronQ (Kronecker-factored Hessian quantization). Packed int4 (~3.8 GB); a fused dequant + bidirectional-incoherence (BiIP) CUDA kernel with per-group scales unpacks on the fly.
Perplexity: 5.537
Zero-shot accuracy:
| PIQA | ARC-E | ARC-C | HellaSwag | WinoGrande | BoolQ | OBQA | Average |
|---|---|---|---|---|---|---|---|
| 77.97 | 73.06 | 44.62 | 75.38 | 68.90 | 77.89 | 43.60 | 65.92 |
(lm-evaluation-harness 0-shot; acc_norm for PIQA/HellaSwag/ARC, acc for WinoGrande/BoolQ.)
python eval_pretrained.py meta-llama/Llama-2-7b-hf donghyunli/Llama-2-7b-KronQ-W4A16-g128 --ppl --zs
Group-128 asymmetric W4, weight-only (a_bits=16), --alpha 0.25, --act_order, BiIP, raw H_G. 128 WikiText-2 calibration sequences.
Derivative of Llama-2-7b — llama2 license.
Base model
meta-llama/Llama-2-7b-hf