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
Tagged with
transformers
Explore machine learning concepts related to transformers. Clear explanations and practical insights.
20
Concepts Found
Concepts Related to transformers
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
Attention Sinks: Stable Streaming LLMs
Learn about attention sinks, where LLMs concentrate attention on initial tokens, and how preserving them enables streaming inference.
17 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: How Instructions Propagate Through Model 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 Transformer-Based Approaches
Understanding end-to-end object detection with transformers, from DETR's object queries to bipartite matching and attention-based localization
8 min readConcept
Layer Normalization
Learn layer normalization for transformers and sequence models: how normalizing across features enables batch-independent training with interactive visualizations.
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