Implementation of Universal Transformer in Pytorch
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Updated
Nov 19, 2018 - Python
Implementation of Universal Transformer in Pytorch
Looped transformers and recurrent depth: 333 papers, English reading guides, diagrams, and a searchable research library.
Awesome list of papers, code, models and blogs on Looped / recurrent-depth / weight-tied Transformers — depth as a third scaling axis
The implementation of Adaptive Computation function used in RNN "https://arxiv.org/pdf/1603.08983.pdf" and Universal Transformer Network "https://arxiv.org/pdf/1807.03819.pdf"
Universal Transformers implementation in PyTorch
Unofficial Implementation of Universal Transformer https://arxiv.org/abs/1807.03819
A Universal Transformer that solves 4x4 Sudoku by repeatedly applying one shared block and learning when to stop
Does the Universal/Looped-Transformer trick (reuse one block N times instead of stacking N) work on Mamba? Weight-shared depth in a state-space model, with honest per-seed results.
Local EDU AI LAB for looped (latent-recurrent) transformers - run, patch and measure loop counts on local GPU
Simple from-scratch implementations of transformer-based models that match the state of the art.
Multi strategy logit fusion between Ouro-1.4B (Universal Transformer) and HRM-Text-1B (prefix-LM) for improved text generation
Untying Prefill and Decode Loops in Looped Language Models: a loop adapter lets a looped (recurrent-depth) LM prefill with m loops and decode with n. Ouro, SenseNova-Loop, Lev; every number recomputable.
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