WebDec 19, 2024 · GitHub - lucidrains/mixture-of-experts: A Pytorch implementation of Sparsely-Gated Mixture of Experts, for massively increasing the parameter count of language models lucidrains / mixture-of-experts Public master 1 branch 8 tags lucidrains 0.2.1 0b6bf96 on Dec 19, 2024 29 commits Failed to load latest commit information. … WebEquiformer - Pytorch (wip) Implementation of the Equiformer, SE3/E3 equivariant attention network that reaches new SOTA, and adopted for use by EquiFold (Prescient Design) for protein folding. The design of this seems to build off of SE3 Transformers, with the dot product attention replaced with MLP Attention and non-linear message passing from …
GitHub - lucidrains/lambda-networks: Implementation of …
WebGitHub - lucidrains/electra-pytorch: A simple and working implementation of Electra, the fastest way to pretrain language models from scratch, in Pytorch lucidrains / electra-pytorch Public Notifications Fork 38 Star 182 master 1 branch 4 tags Code 62 commits Failed to load latest commit information. .github/ workflows electra_pytorch WebJul 29, 2024 · GitHub - lucidrains/PaLM-pytorch: Implementation of the specific Transformer architecture from PaLM - Scaling Language Modeling with Pathways lucidrains / PaLM-pytorch Public main 2 branches 17 tags Code lucidrains 0.2.2 7164d13 on Jul 29, 2024 48 commits .github/ workflows scaffold last year examples/ … chiswick a4
怎么在pytorch中使用Google开源的优化器Lion? - 知乎
WebNov 18, 2024 · Implementation of LambdaNetworks, a new approach to image recognition that reaches SOTA with less compute - GitHub - lucidrains/lambda-networks: Implementation of LambdaNetworks, a new approach to image recognition that reaches SOTA with less compute WebDec 9, 2024 · GitHub - lucidrains/PaLM-rlhf-pytorch: Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM lucidrains / PaLM-rlhf-pytorch Public main 1 branch 69 tags Go to file Code lucidrains fix a bug with the final norm in palm, thanks to @conceptofmind and … Webimport torch from rotary_embedding_torch import RotaryEmbedding # instantiate the positional embedding in your transformer and pass to all your attention layers rotary_emb = RotaryEmbedding ( dim = 32, use_xpos = True # set this to True to make rotary embeddings extrapolate better to sequence lengths greater than the one used at training time) # mock … chiswick airbnb