Learn how attention mechanisms work in transformers by visualizing what LLMs see when processing text. Discover how attention connects semantically related tokens (like Paris → French), understand the Query-Key-Value framework, and explore how different attention heads specialize in syntax, semantics, and coreference.

Implement Activation Addition (ActAdd) and Contrastive Activation Addition (CAA) to steer language models at inference time without training. Learn how adding vectors to the residual stream changes behavior, with practical code implementations and analysis of both methods.

Learn how supervised fine-tuning (SFT) fits into the LLM training pipeline. This post explains the three-step process (pretraining → SFT → alignment), demonstrates SFT implementation with a practical example, and shows how fine-tuning transforms a base model into a task-specific assistant.

Word embeddings are one of the most transformative developments in Natural Language Processing (NLP). They solve a fundamental problem: how can we rep...

Exploring model architecture optimizations for Large Language Model (LLM) inference, focusing on Group Query Attention (GQA) and Mixture of Experts (MoE) techniques.

Scaling laws in AI offer a quantitative framework for understanding the relationship between model size, data, and compute resources. Learn about the Chinchilla scaling law, power laws, and the future of large models.

Exploration of AI accelerators and their impact on deploying Large Language Models (LLMs) at scale.

Overview of Large Language Model (LLM) inference, its importance, challenges, and key problem formulations.