KronQ: LLM Quantization via Kronecker-Factored Hessian
Paper • 2607.07964 • Published • 33
Paper: arXiv:2607.07964 · Code: GitHub
Llama-2-13b quantized to 2-bit weights / 16-bit activations, group size 128, with KronQ (Kronecker-factored Hessian quantization). Packed int2 (~4.0 GB); a fused dequant + bidirectional-incoherence (BiIP) CUDA kernel with per-group scales unpacks on the fly.
Perplexity: 6.508
Zero-shot accuracy:
| PIQA | ARC-E | ARC-C | HellaSwag | WinoGrande | BoolQ | OBQA | Average |
|---|---|---|---|---|---|---|---|
| 72.58 | 68.86 | 38.14 | 63.88 | 65.98 | 66.02 | 36.80 | 58.89 |
(lm-evaluation-harness 0-shot; acc_norm for PIQA/HellaSwag/ARC, acc for WinoGrande/BoolQ.)
python eval_pretrained.py meta-llama/Llama-2-13b-hf donghyunli/Llama-2-13b-KronQ-W2A16-g128 --ppl --zs
Group-128 asymmetric W2, weight-only (a_bits=16), --alpha 0.25, --act_order, BiIP, raw H_G. 128 WikiText-2 calibration sequences.
Derivative of Llama-2-13b — llama2 license.
Base model
meta-llama/Llama-2-13b-hf