KronQ: LLM Quantization via Kronecker-Factored Hessian
Paper • 2607.07964 • Published • 33
Paper: arXiv:2607.07964 · Code: GitHub
Llama-2-70b-hf quantized to 2-bit weights / 16-bit activations with KronQ (Kronecker-factored Hessian quantization). Weights are stored packed int2 (~17 GB); a fused dequant + bidirectional-incoherence (BiIP) CUDA kernel unpacks them on the fly.
Perplexity: 5.14
Zero-shot accuracy:
| PIQA | ARC-E | ARC-C | HellaSwag | WinoGrande | BoolQ | OBQA | Average |
|---|---|---|---|---|---|---|---|
| 77.37 | 75.93 | 46.93 | 71.98 | 74.98 | 79.69 | 43.20 | 67.15 |
(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-W2A16 --ppl --zs
Per-channel asymmetric W2, 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