Meta-Llama-3-8B quantized to 2-bit weights / 16-bit activations with KronQ (Kronecker-factored Hessian quantization). Weights are stored packed int2 (~4.6 GB; the 128k-token embedding + lm_head stay fp16); a fused dequant + bidirectional-incoherence (BiIP) CUDA kernel unpacks them on the fly.

Results (WikiText-2, seqlen 2048)

Perplexity: 11.92

Zero-shot accuracy:

PIQA	ARC-E	ARC-C	HellaSwag	WinoGrande	BoolQ	OBQA	Average
67.03	50.67	30.63	49.00	61.09	66.12	33.00	51.08

(lm-evaluation-harness, 0-shot. acc_norm for PIQA/HellaSwag/ARC/OBQA, acc for WinoGrande/BoolQ.)

Usage

KronQ-packed checkpoint (model.safetensors carries biip_w_codes/scale/zero + BiIP buffers, see kronq_packed_config.json). Load with the KronQ runtime:

python eval_pretrained.py meta-llama/Meta-Llama-3-8B donghyunli/Meta-Llama-3-8B-KronQ-W2A16 --ppl --zs

Recipe

Per-channel asymmetric W2, weight-only (a_bits=16), --alpha 0.25, bidirectional incoherence processing (BiIP), act_order, raw H_G. Calibrated on 128 WikiText-2 sequences.

License

Derivative of Meta-Llama-3-8B — subject to the Llama 3 Community License.