gqa

Train Scene Graph Generation for Visual Genome and GQA in PyTorch >= 1.2 with improved zero and few-shot generalization [BMVC 2020, ICCV 2021]

Decoding Attention is specially optimized for MHA, MQA, GQA and MLA using CUDA core for the decoding stage of LLM inference.

249M-param MoE transformer built from scratch in PyTorch. GQA, RoPE, SwiGLU, sparse MoE with 3 aux losses, AMP training loop no Trainer abstractions. Architecture mirrors LLaMA/Mistral/Mixtral decisions, fully inspectable.

PyTorch DataLoader for many VQA datasets

A PyTorch implementation of the Hyena-Y model, a convolution-based multi-hybrid architecture optimized for edge devices.

Predicting a subgraph alongside the answer in a graph based VQA model

MHA, MQA, GQA, MLA 相关原理及简要实现

Vision-Language, Solve GQA(Visual Reasoning in the Real World) dataset.

This is a multimodal model design for the Vision Question Answering (VQA) task. It integrates the Llama2 13B, OWL-ViT, and YOLOv8 models.

Build an LLM inference engine from scratch in pure C

A RAG-based question-answering system that processes user queries using local documents. It extracts relevant information to answer questions, falling back to a large language model when local sources are insufficient, ensuring accurate and contextual responses.

[GCPR 2023] Zero-shot Translation of Attention Patterns in VQA Models to Natural Language

CPU-first long-context KV memory optimization with deterministic page regeneration and exact folded attention.

A character-level GPT model built from scratch in PyTorch, exclusively trained to generate text in the style of Shakespeare. Features a modular RAG pipeline.

This repository contains an implementation of Group Query Attention (GQA), an efficient variant of multi-head attention used in modern transformer models like LLaMA.

A code deep-dive on one of the key innovations from Deepseek - Multihead Latent Attention (MLA)

A 197M-param decoder-only LM trained from scratch on FineWeb-Edu — PyTorch with RoPE, GQA, SwiGLU, 32k BPE, built for 6GB VRAM

LaTeX files for my honours thesis: "Graph Attention Networks for Compositional Visual Question Answering"

Modular, step-by-step implementation of GPT & Modern LLMs from scratch in PyTorch. Featuring RoPE, RMSNorm, SwiGLU, GQA, KV-Cache, Unit Tests, and an Interactive Web Playground.