My own implementation/experiments with a local softmax
Repositories
kyegomez repositories
Implementation of plug in and play Attention from "LongNet: Scaling Transformers to 1,000,000,000 Tokens"
A simplistic pytorch implementation of LongVit using my previous implementation of LongNet as a foundation.
Implementation of M2PT in PyTorch from the paper: "Multimodal Pathway: Improve Transformers with Irrelevant Data from Other Modalities"
Open source community's implementation of the model from "LANGUAGE MODEL BEATS DIFFUSION — TOKENIZER IS KEY TO VISUAL GENERATION"
Implementation of the model: "(MC-ViT)" from the paper: "Memory Consolidation Enables Long-Context Video Understanding"
An open source community implementation of the model MELLE from the paper: "Autoregressive Speech Synthesis without Vector Quantization"
The open source implementation of the multi grouped query attention by the paper "GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints"
Community Implementation of the paper: "Multi-Head Mixture-of-Experts" In PyTorch
"Multimodal Instruction Tuning", An SOTA video dataset
Simple Implementation of a Transformer in the new framework MLX by Apple
PyTorch Implementation of the paper "MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training"
The open source community's implementation of the all-new Multi-Modal Causal Attention from "DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention"
Experiments around using Multi-Modal Casual Attention with Multi-Grouped Query Attention
Implementation of "MORPHEUS-1" from Prophetic AI and "The world’s first multi-modal generative ultrasonic transformer designed to induce and stabilize lucid dreams. "
MOSS-RLHF
MPBS - Model PreTraining Benchmarking Suite to test various metrics for pytorch models before pretraining, training, finetuning, or inference.
Implementation of MambaByte in "MambaByte: Token-free Selective State Space Model" in Pytorch and Zeta
MambaDecoderBlock is a novel decoder architecture that replaces traditional self-attention mechanisms with Mamba state space models, augmented by Mixture of Experts (MoE) layers.
Implementation of MambaFormer in Pytorch ++ Zeta from the paper: "Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks"