TurboAttention: Efficient Attention Approximation For High Throughputs LLMs

Crafting papers on machine learning

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Language models are few-shot learners, 2020

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Svd-softmax: Fast softmax approximation on large vocabulary neural networks

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Seq2sql: Generating structured queries from natural language using reinforcement learning

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Zhong, V., Xiong, C., and Socher, R · 2017

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Online normalizer calculation for softmax

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Pytorch: An imperative style, high-performance deep learning library

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Triton: an intermediate language and compiler for tiled neural network computations

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Huggingface’s transformers: State-of-the-art natural language processing

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Training verifiers to solve math word problems, 2021

Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C., and Schulman, J · 2021

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Documenting large webtext corpora: A case study on the colossal clean crawled corpus, 2021

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Measuring mathematical problem solving with the math dataset, 2021

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Efficient softmax approximation for deep neural networks with attention mechanism, 2021

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Vasyltsov, I. and Chang, W · 2021

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Flashattention: Fast and memory-efficient exact attention with io-awareness, 2022

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Dao, T., Fu, D. Y., Ermon, S., Rudra, A., and Ré, C · 2022

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Llm.int8(): 8-bit matrix multiplication for transformers at scale, 2022

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Dettmers, T., Lewis, M., Belkada, Y., and Zettlemoyer, L · 2022

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Challenging big-bench tasks and whether chain-of-thought can solve them, 2022

Suzgun, M., Scales, N., Schärli, N., Gehrmann, S., Tay, Y., Chung, H. W., Chowdhery, A., Le, Q. V., Chi, E. H., Zhou, D., and Wei, J · 2022

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Striped attention: Faster ring attention for causal transformers

Flashdecoding: Stanford CRFM — crfm.stanford.edu

Dao, T., Haziza, D., Massa, F., and Sizov, G · 2024

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The llama 3 herd of models, 2024

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