Lucidrains github.
Implementation of Axial attention - attending to multi-dimensional data efficiently - lucidrains/axial-attention
Implementation of CALM from the paper "LLM Augmented LLMs: Expanding Capabilities through Composition", out of Google Deepmind - lucidrains/CALM-pytorchfor awarding me the Imminent Grant to advance the state of open sourced text-to-speech solutions. This project was started and will be completed under this grant. StabilityAI for the generous sponsorship, as well as my other sponsors, for affording me the independence to open source artificial intelligence.. Bryan Chiang for the …GitHub Projects is a powerful project management tool that can greatly enhance team collaboration and productivity. Whether you are working on a small startup project or managing a...In today’s digital age, it is essential for professionals to showcase their skills and expertise in order to stand out from the competition. One effective way to do this is by crea... You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window.
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 GATv2.It also does a depthwise …
@inproceedings {qtransformer, title = {Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions}, authors = {Yevgen Chebotar and Quan Vuong and Alex Irpan and Karol Hausman and Fei Xia and Yao Lu and Aviral Kumar and Tianhe Yu and Alexander Herzog and Karl Pertsch and …
Implementation of NÜWA, state of the art attention network for text to video synthesis, in Pytorch - lucidrains/nuwa-pytorchImplementation of ETSformer, state of the art time-series Transformer, in Pytorch - lucidrains/ETSformer-pytorchAn implementation of Transformer with Expire-Span, a circuit for learning which memories to retain - lucidrains/learning-to-expire-pytorch. Implementation of Make-A-Video, new SOTA text to video generator from Meta AI, in Pytorch.They combine pseudo-3d convolutions (axial convolutions) and temporal attention and show much better temporal fusion.
Implementation of Geometric Vector Perceptron, a simple circuit for 3d rotation equivariance for learning over large biomolecules, in Pytorch. Idea proposed and accepted at ICLR 2021 - lucidrains/geometric-vector-perceptron
i would like to work on this but not sure how to set it up. #12 opened on Nov 8, 2023 by vivasvan1. Inference for TTS. #10 opened on Oct 25, 2023 by Wizard-The-Grey. 1. GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects.
When it comes to code hosting platforms, SourceForge and GitHub are two popular choices among developers. Both platforms offer a range of features and tools to help developers coll...Just some miscellaneous utility functions / decorators / modules related to Pytorch and Accelerate to help speed up implementation of new AI research - lucidrains/pytorch-custom-utilsHenryLhc 7 hours ago. I used the codes in the jupyter notebook provided by @MarcusLoppe in the discussion section, and have successfully succeeded trained the …Implementation of π-GAN, for 3d-aware image synthesis, in Pytorch - lucidrains/pi-GAN-pytorchImplementation of a U-net complete with efficient attention as well as the latest research findings - x-unet/setup.py at main · lucidrains/x-unet.A combination of Transformer-XL with ideas from Memory Transformers. While in Transformer-XL the memory is just a FIFO queue, this repository will attempt to update the memory (queries) against the incoming hidden states (keys / values) with a memory attention network.Ponder(ing) Transformer. Implementation of a Transformer that learns to adapt the number of computational steps it takes depending on the difficulty of the input sequence, using the scheme from the PonderNet paper. Will also try to abstract out a pondering module that can be used with any block that returns an output with the halting probability.
Explorations into Ring Attention, from Liu et al. at Berkeley AI - lucidrains/ring-attention-pytorchit turns out cuda kernel version works, but naive flash attention bac… Force push. lucidrainsforce pushed to main • 045d61c…df48d4d •. 5 days ago ...In today’s digital age, it is essential for professionals to showcase their skills and expertise in order to stand out from the competition. One effective way to do this is by crea...Implementation of Feedback Transformer in Pytorch. Contribute to lucidrains/feedback-transformer-pytorch development by creating an account on GitHub.@inproceedings {Ainslie2023CoLT5FL, title = {CoLT5: Faster Long-Range Transformers with Conditional Computation}, author = {Joshua Ainslie and Tao Lei and Michiel de Jong and Santiago Ontan'on and Siddhartha Brahma and Yury Zemlyanskiy and David Uthus and Mandy Guo and James Lee-Thorp and Yi Tay and Yun-Hsuan Sung and Sumit …Implementation of a holodeck, written in Pytorch. Contribute to lucidrains/holodeck-pytorch development by creating an account on GitHub.
An implementation of masked language modeling for Pytorch, made as concise and simple as possible - lucidrains/mlm-pytorchThis guy (Phil Wang, https://github.com/lucidrains) seems to have the hobby to just implement all models and papers he finds interesting. See his GitHub page. See his …
Implementation of NÜWA, state of the art attention network for text to video synthesis, in Pytorch - lucidrains/nuwa-pytorchout = attn ( x, mask = mask ) assert out. shape == x. shape. For a full fledged linear transformer based on agent tokens, just import AgentTransformer. import torch from agent_attention_pytorch import AgentTransformer transformer = AgentTransformer (. dim = 512 , depth = 6 , num_agent_tokens = 128 ,import torch from ema_pytorch import EMA # your neural network as a pytorch module net = torch. nn. Linear (512, 512) # wrap your neural network, specify the decay (beta) ema = EMA ( net, beta = 0.9999, # exponential moving average factor update_after_step = 100, # only after this number of .update() calls will it start …Implementation of Recurrent Memory Transformer, Neurips 2022 paper, in Pytorch - lucidrains/recurrent-memory-transformer-pytorch.import torch from performer_pytorch import PerformerLM model = PerformerLM ( num_tokens = 20000, max_seq_len = 2048, # max sequence length dim = 512, # dimension depth = 12, # layers heads = 8, # heads causal = False, # auto-regressive or not nb_features = 256, # number of random features, if not set, will default to (d …Phil Wang lucidrains · All gists 27 · Starred 7. Sort: Recently ...Implementation of GateLoop Transformer in Pytorch and Jax - lucidrains/gateloop-transformer.Implementation of COCO-LM, Correcting and Contrasting Text Sequences for Language Model Pretraining, in Pytorch - GitHub - lucidrains/coco-lm-pytorch: Implementation of COCO-LM, Correcting and Contrasting Text Sequences for Language Model Pretraining, in PytorchImplementation of Denoising Diffusion for protein design, but using the new Equiformer (successor to SE3 Transformers) with some additional improvements - lucidrains/equiformer-diffusion
Implementation of the GBST block from the Charformer paper, in Pytorch - lucidrains/charformer-pytorch
The RETRODataset class accepts paths to a number of memmapped numpy arrays containing the chunks, the index of the first chunk in the sequence to be trained on (in RETRO decoder), and the pre-calculated indices of the k-nearest neighbors per chunk.. You can use this to easily assemble the data for RETRO training, if you …
Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch - lucidrains/enformer-pytorchThey're uploading personal narratives and news reports about the outbreak to the site, amid fears that content critical of the Chinese government will be scrubbed. Facing the risk ...Todo · allow for local attention to be automatically included, either for grouped attention, or use LocalMHA from local-attention repository in parallel, ...Ponder(ing) Transformer. Implementation of a Transformer that learns to adapt the number of computational steps it takes depending on the difficulty of the input sequence, using the scheme from the PonderNet paper. Will also try to abstract out a pondering module that can be used with any block that returns an output with the halting probability.Implementation of Segformer, Attention + MLP neural network for segmentation, in Pytorch - lucidrains/segformer-pytorchCausal linear attention benchmark. #64. Closed. caffeinetoomuch opened this issue on Apr 12, 2021 · 13 comments. A paper by Jinbo Xu suggests that one doesn't need to bin the distances, and can instead predict the mean and standard deviation directly. You can use this by turning on one flag predict_real_value_distances, in which case, the distance prediction returned will have a dimension of 2 for the mean and standard deviation respectively. Implementation of GateLoop Transformer in Pytorch and Jax - lucidrains/gateloop-transformer.Sinkhorn Transformer - Practical implementation of Sparse Sinkhorn Attention - lucidrains/sinkhorn-transformerIn today’s digital landscape, efficient project management and collaboration are crucial for the success of any organization. When it comes to user interface and navigation, both G... Update: seems to work for my local enwik8 autoregressive language modeling. Update 2: experiments, seems much worse than Adam if learning rate held constant. Update 3: Dividing the learning rate by 3, seeing better early results than Adam.
Implementation of Nyström Self-attention, from the paper Nyströmformer - lucidrains/nystrom-attentionImplementation of a U-net complete with efficient attention as well as the latest research findings - x-unet/setup.py at main · lucidrains/x-unet.Implementation of Perceiver AR, Deepmind's new long-context attention network based on Perceiver architecture, in Pytorch.. Generated piano samples. I am building this out of popular demand, not because I believe in the architecture. As someone else puts it succinctly, this is equivalent to an encoder / decoder transformer architecture where the …Fabian's recent paper suggests iteratively feeding the coordinates back into SE3 Transformer, weight shared, may work. I have decided to execute based on this idea, even though it is still up in the air how it actually works. You can also use E(n)-Transformer or EGNN for structural refinement.. Update: Baker's lab have shown …Instagram:https://instagram. sierraskyeprivate nudesunsolved mysteries update gifanother word for in placestate of missouri employee salaries blue book Implementation of 🌻 Mirasol, SOTA Multimodal Autoregressive model out of Google Deepmind, in Pytorch - lucidrains/mirasol-pytorch sky zone trampoline park bowie reviewsvip nails palm city fl A practical implementation of GradNorm, Gradient Normalization for Adaptive Loss Balancing, in Pytorch - lucidrains/gradnorm-pytorch naruto x sasuke ao3 Implementation of AudioLM, a SOTA Language Modeling Approach to Audio Generation out of Google Research, in Pytorch - Releases · lucidrains/audiolm-pytorch Implementation of Gated State Spaces, from the paper Long Range Language Modeling via Gated State Spaces, in Pytorch.In particular, it will contain the hybrid version containing local self attention with the long-range GSS.