⚠️ Fake-quant fp16 checkpoint. The 3-bit weights are stored in fp16 (KronQ does not pack int3), so this repo is the same size as bf16 — no compression or speedup, for PPL / accuracy reproduction only. For deployable low-bit, see the W4A16 (packed int4) repo.

Meta-Llama-3-8B quantized to 3-bit weights with KronQ (Kronecker-factored Hessian quantization), exported as a standard fp16 model.

Results (WikiText-2, seqlen 2048)

Perplexity: 7.09

Zero-shot accuracy:

PIQA	ARC-E	ARC-C	HellaSwag	WinoGrande	BoolQ	OBQA	Average
77.53	74.54	50.17	74.92	71.74	81.13	41.20	67.32

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

Usage

Loads as a standard fp16 model — no KronQ code needed:

python

from transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained("donghyunli/Meta-Llama-3-8B-KronQ-W3A16-fake", torch_dtype="float16").cuda()
tok = AutoTokenizer.from_pretrained("donghyunli/Meta-Llama-3-8B-KronQ-W3A16-fake")

Recipe

Per-channel asymmetric W3, 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.