Explore a detailed technical video analysis of Meta's groundbreaking Byte Latent Transformers (BLT) model, breaking down the revolutionary paper "Byte Latent Transformers - Patches scale better than Tokens." Learn fundamental concepts from transformer architectures and subword tokenizers to byte encodings and entropy models, with visual explanations and architectural insights. Dive deep into how dynamic compute allocation could revolutionize Large Language Models (LLMs), examining the BLT architecture's components including local encoders, latent transformers, and local decoders. Master complex technical concepts through clear visual explanations and practical examples across the 37-minute presentation, supported by comprehensive coverage of transformer technology, embedding systems, and the innovative use of patches in language modeling.
Byte Latent Transformers - Understanding Meta's BLT Model for Efficient Language Processing
Neural Breakdown with AVB via YouTube
Overview
Syllabus
- Intro
- Intro to Transformers
- Subword Tokenizers
- Embeddings
- How does vocab size impact Transformer FLOPs?
- Byte Encodings
- Pros and Cons of Byte Tokens
- Patches
- Entropy
- Entropy model
- Dynamically Allocate Compute
- Latent Space
- BLT Architecture
- Local Encoder
- Latent Transformer and Local Decoder in BLT
- Outro
Taught by
Neural Breakdown with AVB
Related Courses
-
Byte Latent Transformer: Token-Free Architecture Using Entropy-Based Byte Prediction
-
Byte Latent Transformer - Dynamic Patches vs Traditional Tokenization in Language Models
-
Mathematics Behind Large Language Models and Transformers
-
LLMs and Transformers Demystified: Introduction to AI Engineering - Lecture 1
-
Understanding Diffusion Transformers and the Technology Behind Sora
-
From Attention to Generative Language Models - Building Transformers from Scratch
