ALiBi: Attention with Linear Biases
Learn ALiBi, the position encoding method that adds linear biases to attention scores for exceptional length extrapolation in transformers.
19 min readConcept
Tagged with
transformers
Explore machine learning concepts related to transformers. Clear explanations and practical insights.
20
Concepts Found
Concepts Related to transformers
MHA vs GQA vs MQA: Choosing the Right Attention
Compare Multi-Head, Grouped-Query, and Multi-Query Attention mechanisms to understand their trade-offs and choose the optimal approach for your use case.
9 min readConcept
Attention Sinks: Stable Streaming LLMs
Learn about attention sinks, where LLMs concentrate attention on initial tokens, and how preserving them enables streaming inference.
18 min readConcept
Cross-Attention: Bridging Different Modalities
Understand cross-attention, the mechanism that enables transformers to align and fuse information from different sources, sequences, or modalities.
15 min readConcept
Grouped-Query Attention (GQA)
Learn how Grouped-Query Attention (GQA) balances Multi-Head quality with Multi-Query efficiency for faster LLM inference.
7 min readConcept
Linear Attention Approximations
Explore linear complexity attention mechanisms including Performer, Linformer, and other efficient transformers that scale to very long sequences.
6 min readConcept
Masked and Causal Attention
Learn how masked attention enables autoregressive generation and prevents information leakage in transformers and language models.
7 min readConcept
Multi-Query Attention (MQA)
Learn Multi-Query Attention (MQA), the optimization that shares keys and values across attention heads for massive memory savings.
7 min readConcept
Rotary Position Embeddings (RoPE)
Learn Rotary Position Embeddings (RoPE), the elegant position encoding using rotation matrices, powering LLaMA, Mistral, and modern LLMs.
8 min readConcept
Scaled Dot-Product Attention
Master scaled dot-product attention, the fundamental transformer building block. Learn why scaling is crucial for stable training.
6 min readConcept
Sliding Window Attention
Sliding Window Attention for long sequences: local context windows enable O(n) complexity, used in Mistral and Longformer models.
14 min readConcept
Sparse Attention Patterns
Explore sparse attention mechanisms that reduce quadratic complexity to linear or sub-quadratic, enabling efficient processing of long sequences.
7 min readConcept
Prompt Influence Flow Through Transformer Layers
Deep dive into how different prompt components influence model behavior across transformer layers, from surface patterns to abstract reasoning.
6 min readConcept
Cross-Encoder vs Bi-Encoder
Understand the fundamental differences between independent and joint encoding architectures for neural retrieval systems.
7 min readConcept
Context Windows: The Memory Limits of LLMs
Interactive visualization of LLM context windows - sliding windows, expanding contexts, and attention patterns that define model memory limits.
6 min readConcept
KV Cache: The Secret to Fast LLM Inference
Interactive KV cache visualization - how key-value caching in LLM transformers enables fast text generation without quadratic recomputation.
7 min readConcept
Tokenization: Converting Text to Numbers
Interactive exploration of tokenization methods in LLMs - BPE, SentencePiece, and WordPiece. Understand how text becomes tokens that models can process.
5 min readConcept
Modern Object Detection: DETR and Transformers
Understanding end-to-end object detection with transformers, from DETR's object queries to bipartite matching and attention-based localization
9 min readConcept
Layer Normalization for Transformers
Learn layer normalization for transformers and sequence models: how normalizing across features enables batch-independent training.
7 min readConcept
Mixture of Experts (MoE)
Understanding sparse mixture of experts models - architecture, routing mechanisms, load balancing, and efficient scaling strategies for large language models
6 min readConcept