SeerAttention-Llama-3.1-8B-AttnGates

This repo only contains the AttnGates' weights for Llama-3.1-8B-Instruct Model.

SeerAttention introduces learnable AttnGate modules to accelerate the computationally intensive prefill stage of long-context large language models (LLMs) via dynamic block-level sparsity. The AttnGates are trained in a parameter-efficient self-distillation framework, where they learn to mimic the 2D max-pooled attention patterns of the original frozen model, preserving its integrity while avoiding costly retraining. During inference, these gates generate block-sparse binary masks by applying threshold/TopK to their learned soft scores, enabling efficient computation through a custom block-sparse FlashAttention kernel.

Original Github Repo

https://github.com/microsoft/SeerAttention.

Evaluation Results

Perplexity on PG19

Density	8192	16384	32768	65536	131072
1.00	10.03	9.88	9.92	9.97	10.03
0.50	10.04	9.89	9.92	9.99	10.05
0.40	10.06	9.89	9.93	9.99	10.07
0.30	10.09	9.91	9.95	10.01	10.15
0.20	10.19	9.94	9.97	10.04	10.37
0.10	10.61	10.08	10.04	10.09	10.88

LongBench

With threshold set to 2e-3.

Task	0-4k	4-8k	8k+
2wikimqa	51.1	47.85	33.36
gov_report	35.03	35.05	34.57
hotpotqa	63.97	60.0	56.7
lcc	67.98	73.18	65.28
multi_news	28.1	25.78	24.25
multifieldqa_en	58.63	51.45	51.87
passage_count	18.0	10.15	11.88
passage_retrieval_en	100.0	99.0	98.0
qasper	47.77	44.04	39.63
repobench-p	51.78	56.24	56.75
samsum	43.28	41.19	45.29
trec	64.0	76.0	75.0
triviaqa	90.91	88.45	92.43
averaged	55.43	54.49	52.69

RULER

	Dense Baseline	SeerAttn	Avg density
4k	95.53	95.53	0.87
8k	92.27	92.71	0.72
16k	92.01	92.02	0.56
32k	87.63	88.49	0.46
64k	84.39	83.48	0.32
128k	76.26	73.37	0.17