2017 · computer-science
Attention Is All You Need
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.
Study Attention Is All You Need2024 · computer-science
DeepStudy R6 Many-Author Attention Study
This repository-owned acceptance paper studies grounded attention with reproducible evidence.
Acceptance run 73ee1b64-493b-44f3-87f1-13267e6acc3c
Study DeepStudy R6 Many-Author Attention Study2024 · computer-science
DeepStudy R6 Many-Author Attention Study
This repository-owned acceptance paper studies grounded attention with reproducible evidence.
Acceptance run 22a595b4-9d12-4ed7-9971-37e9a6cffbe5
Study DeepStudy R6 Many-Author Attention Study2024 · computer-science
DeepStudy R6 Many-Author Attention Study
This repository-owned acceptance paper studies grounded attention with reproducible evidence.
Acceptance run 452cd717-d1c8-4cce-be95-3b196e144069
Study DeepStudy R6 Many-Author Attention Study2025 · computer-science
Kimi Linear: An Expressive, Efficient Attention Architecture
We introduce Kimi Linear, a hybrid linear attention architecture that, for the first time, outperforms full attention under fair comparisons across various scenarios -- including short-context, long-context, and reinforcement learning (RL) scaling regimes. At its core lies Kimi Delta Attention (KDA), an expressive linear attention module that extends Gated DeltaNet with a finer-grained gating mechanism, enabling more effective use of limited finite-state RNN memory. Our bespoke chunkwise algorithm achieves high hardware efficiency through a specialized variant of the Diagonal-Plus-Low-Rank (DPLR) transition matrices, which substantially reduces computation compared to the general DPLR formulation while remaining more consistent with the classical delta rule. We pretrain a Kimi Linear model with 3B activated parameters and 48B total parameters, based on a layerwise hybrid of KDA and Multi-Head Latent Attention (MLA). Our experiments show that with an identical training recipe, Kimi Linear outperforms full MLA with a sizeable margin across all evaluated tasks, while reducing KV cache usage by up to 75% and achieving up to 6 times decoding throughput for a 1M context. These results demonstrate that Kimi Linear can be a drop-in replacement for full attention architectures with superior performance and efficiency, including tasks with longer input and output lengths. To support further research, we open-source the KDA kernel and vLLM implementations, and release the pre-trained and instruction-tuned model checkpoints.
Study Kimi Linear: An Expressive, Efficient Attention Architecture2020 · computer-science
Scaling Laws for Neural Language Models
We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the dependence of overfitting on model/dataset size and the dependence of training speed on model size. These relationships allow us to determine the optimal allocation of a fixed compute budget. Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence.
Study Scaling Laws for Neural Language Models