The DeepSeek v3 paper (and are out, after yesterday's mysterious launch of Plenty of interesting particulars in right here. Plenty of fascinating particulars in right here. While we've seen makes an attempt to introduce new architectures reminiscent of Mamba and more not too long ago xLSTM to only title a number of, it appears possible that the decoder-only transformer is right here to remain - at the very least for the most half. Dense transformers across the labs have in my opinion, converged to what I call the Noam Transformer (because of Noam Shazeer). The current "best" open-weights models are the Llama three series of models and Meta seems to have gone all-in to practice the absolute best vanilla Dense transformer. Meta is behind a popular open-source AI model called Llama. While much of the progress has happened behind closed doorways in frontier labs, now we have seen a variety of effort within the open to replicate these results. By far essentially the most interesting detail although is how a lot the coaching value. • We are going to constantly research and refine our mannequin architectures, aiming to further improve both the training and inference effectivity, striving to method efficient help for infinite context length. While RoPE has labored properly empirically and gave us a way to increase context windows, I believe one thing more architecturally coded feels better asthetically.
