Sabri Eyuboglu

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updated a model about 2 months ago

sabrieyuboglu/haystacks-a7x4ogap

Updated Oct 31 • 6

updated a dataset 2 months ago

sabrieyuboglu/math-test-llama3.2-1b-n128-l512

Viewer • Updated Oct 11 • 128 • 34

liked a dataset 3 months ago

ScalingIntelligence/monkey_business

Viewer • Updated Sep 2 • 2.88k • 466 • 10

upvoted a paper 4 months ago

Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Paper • 2407.21787 • Published Jul 31 • 11

upvoted a collection 9 months ago

based

Collection

These language model checkpoints are trained at the 360M and 1.3Bn parameter scales for up to 50Bn tokens on the Pile corpus, for research purposes. • 15 items • Updated Oct 18 • 9

authored a paper 9 months ago

Zoology: Measuring and Improving Recall in Efficient Language Models

Paper • 2312.04927 • Published Dec 8, 2023 • 2

updated a collection 10 months ago

based

Collection

These language model checkpoints are trained at the 360M and 1.3Bn parameter scales for up to 50Bn tokens on the Pile corpus, for research purposes. • 15 items • Updated Oct 18 • 9

updated a dataset 10 months ago

hazyresearch/based-swde-deprecated

Viewer • Updated Mar 12 • 12.4k • 36

updated a collection 10 months ago

based

Collection

These language model checkpoints are trained at the 360M and 1.3Bn parameter scales for up to 50Bn tokens on the Pile corpus, for research purposes. • 15 items • Updated Oct 18 • 9

updated a dataset 10 months ago

hazyresearch/fda

Viewer • Updated Mar 12 • 1.1k • 36 • 1

commented a paper 10 months ago

Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28 • 18 •

12

upvoted a paper 10 months ago

Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28 • 18

updated a collection 10 months ago

based

Collection

These language model checkpoints are trained at the 360M and 1.3Bn parameter scales for up to 50Bn tokens on the Pile corpus, for research purposes. • 15 items • Updated Oct 18 • 9

authored a paper 10 months ago

Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28 • 18

updated a collection 10 months ago

based

Collection

These language model checkpoints are trained at the 360M and 1.3Bn parameter scales for up to 50Bn tokens on the Pile corpus, for research purposes. • 15 items • Updated Oct 18 • 9