Coding LLaMA 2 from scratch in PyTorch - KV Cache, Grouped Query Attention, Rotary PE, RMSNorm

The KV Cache: Memory Usage in Transformers

LLaMA explained: KV-Cache, Rotary Positional Embedding, RMS Norm, Grouped Query Attention, SwiGLU

PyTorch 2.0 Q&A: Optimizing Transformers for Inference

Accelerate Big Model Inference: How Does it Work?

How a Transformer works at inference vs training time

Accelerating Generative AI - Christian Puhrsch & Horace He, Meta

Mistral Architecture Explained From Scratch with Sliding Window Attention, KV Caching Explanation

Coding a Multimodal (Vision) Language Model from scratch in PyTorch with full explanation

Fast LLM Serving with vLLM and PagedAttention

Efficient LLM Inference (vLLM KV Cache, Flash Decoding & Lookahead Decoding)

Implement Llama 3 From Scratch - PyTorch

The math behind Attention: Keys, Queries, and Values matrices

Attention mechanism: Overview

Efficient Inference of Vision Instruction-Following Models with Elastic Cache - ArXiv:24

Coding a Transformer from scratch on PyTorch, with full explanation, training and inference.

FlashAttention - Tri Dao | Stanford MLSys #67

Let's build GPT: from scratch, in code, spelled out.

【Transformerの基礎】Multi-Head Attentionの仕組み

GPT-Fast - blazingly fast inference with PyTorch (w/ Horace He)