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donghyunli/Llama-2-7b-KronQ-W2A16
Llama-2-7b-KronQ-W2A16 is a text generation model from donghyunli. Use it when you need the model to write or continue text. The card lists the license as llama2.
Downloads · 30 days
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Updated Jul 24, 2026
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1.9B
4.6 GB on disk
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.safetensors2.2 GB · 100%
How the weights are stored.
U81.6B · 86%
From the Hugging Face model README
Paper: arXiv:2607.07964 · Code: GitHub
Llama-2-7b quantized to 2-bit weights / 16-bit activations with KronQ (Kronecker-factored Hessian quantization). Weights are stored packed int2 (~2.3 GB vs 13 GB fp16); a fused dequant + bidirectional-incoherence (BiIP) CUDA kernel unpacks them on the fly at inference.
Perplexity: 8.19
Zero-shot accuracy:
| PIQA | ARC-E | ARC-C | HellaSwag | WinoGrande | BoolQ | OBQA | Average |
|---|---|---|---|---|---|---|---|
| 67.95 | 54.34 | 31.31 | 51.16 | 60.77 | 64.25 | 34.80 | 52.08 |
(lm-evaluation-harness, 0-shot. acc_norm for PIQA/HellaSwag/ARC/OBQA, acc for WinoGrande/BoolQ.)
KronQ-packed checkpoint (model.safetensors carries biip_w_codes/scale/zero + BiIP buffers, see kronq_packed_config.json). Load with the KronQ runtime:
# clone https://github.com/<...>/KronQ and build the CUDA kernels, then:
python eval_pretrained.py meta-llama/Llama-2-7b-hf donghyunli/Llama-2-7b-KronQ-W2A16 --ppl --zs
Per-channel asymmetric W2, weight-only (a_bits=16), --alpha 0.25, bidirectional incoherence processing (BiIP, Hadamard kernel), act_order. Calibrated on 128 WikiText-2 sequences with raw H_G. To reproduce instead of using these weights, see the KronQ repo's "Reproduce" path.
Derivative of Llama-2-7b — subject to the Llama 2 Community License.