fkodom / soft-mixture-of-expertsLinks
PyTorch implementation of Soft MoE by Google Brain in "From Sparse to Soft Mixtures of Experts" (https://arxiv.org/pdf/2308.00951.pdf)
β78Updated 2 years ago
Alternatives and similar repositories for soft-mixture-of-experts
Users that are interested in soft-mixture-of-experts are comparing it to the libraries listed below
Sorting:
- Implementation of π» Mirasol, SOTA Multimodal Autoregressive model out of Google Deepmind, in Pytorchβ90Updated last year
- Some personal experiments around routing tokens to different autoregressive attention, akin to mixture-of-expertsβ120Updated last year
- Implementation of Infini-Transformer in Pytorchβ113Updated 11 months ago
- Yet another random morning idea to be quickly tried and architecture shared if it works; to allow the transformer to pause for any amountβ¦β53Updated 2 years ago
- PyTorch implementation of "From Sparse to Soft Mixtures of Experts"β66Updated 2 years ago
- Implementation of the general framework for AMIE, from the paper "Towards Conversational Diagnostic AI", out of Google Deepmindβ71Updated last year
- Implementation of Soft MoE, proposed by Brain's Vision team, in Pytorchβ337Updated 8 months ago
- Official repository for the paper "SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention"β102Updated last year
- Implementation of Zorro, Masked Multimodal Transformer, in Pytorchβ97Updated 2 years ago
- Explorations into the recently proposed Taylor Series Linear Attentionβ100Updated last year
- HGRN2: Gated Linear RNNs with State Expansionβ55Updated last year
- β186Updated last year
- Revisiting Efficient Training Algorithms For Transformer-based Language Models (NeurIPS 2023)β81Updated 2 years ago
- Pytorch implementation of the PEER block from the paper, Mixture of A Million Experts, by Xu Owen He at Deepmindβ131Updated last month
- β106Updated last year
- Code and benchmark for the paper: "A Practitioner's Guide to Continual Multimodal Pretraining" [NeurIPS'24]β60Updated 11 months ago
- One Initialization to Rule them All: Fine-tuning via Explained Variance Adaptationβ45Updated last month
- A repository for DenseSSMsβ89Updated last year
- Official code for "TOAST: Transfer Learning via Attention Steering"β188Updated 2 years ago
- Implementation of CALM from the paper "LLM Augmented LLMs: Expanding Capabilities through Composition", out of Google Deepmindβ178Updated last year
- Griffin MQA + Hawk Linear RNN Hybridβ89Updated last year
- Implementation of GateLoop Transformer in Pytorch and Jaxβ91Updated last year
- [ICLR 2023] "Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers" by Tianlong Chen*, Zhenyu Zhang*, Ajay Jaiswalβ¦β56Updated 2 years ago
- Implementation of Agent Attention in Pytorchβ92Updated last year
- β89Updated last year
- Implementation of Memformer, a Memory-augmented Transformer, in Pytorchβ125Updated 5 years ago
- Triton Implementation of HyperAttention Algorithmβ48Updated last year
- MambaFormer in-context learning experiments and implementation for https://arxiv.org/abs/2402.04248β56Updated last year
- Why Do We Need Weight Decay in Modern Deep Learning? [NeurIPS 2024]β69Updated last year
- Exploring an idea where one forgets about efficiency and carries out attention across each edge of the nodes (tokens)β55Updated 8 months ago