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The Big LLM Architecture Comparison
From DeepSeek-V3 to Kimi K2: A Look At Modern LLM Architecture Design
 • Sebastian Raschka, PhD
Understanding Reasoning LLMs
Methods and Strategies for Building and Refining Reasoning Models
 • Sebastian Raschka, PhD
Understanding and Coding Self-Attention, Multi-Head Attention, Causal-Attention, and Cross-Attention in LLMs
This article will teach you about self-attention mechanisms used in transformer architectures and large language models (LLMs) such as GPT-4 and Llama.
Components of A Coding Agent
How coding agents use tools, memory, and repo context to make LLMs work better in practice
 • Sebastian Raschka, PhD
Understanding Large Language Models
A Cross-Section of the Most Relevant Literature To Get Up to Speed
 • Sebastian Raschka, PhD
From GPT-2 to gpt-oss: Analyzing the Architectural Advances
And How They Stack Up Against Qwen3
 • Sebastian Raschka, PhD
Understanding Multimodal LLMs
An introduction to the main techniques and latest models
 • Sebastian Raschka, PhD
The State Of LLMs 2025: Progress, Problems, and Predictions
A 2025 review of large language models, from DeepSeek R1 and RLVR to inference-time scaling, benchmarks, architectures, and predictions for 2026.
 • Sebastian Raschka, PhD
The State of Reinforcement Learning for LLM Reasoning
Understanding GRPO and New Insights from Reasoning Model Papers
 • Sebastian Raschka, PhD
Coding LLMs from the Ground Up: A Complete Course
Why build LLMs from scratch? It's probably the best and most efficient way to learn how LLMs really work. Plus, many readers have told me they had a lot…
 • Sebastian Raschka, PhD
Practical Tips for Finetuning LLMs Using LoRA (Low-Rank Adaptation)
Things I Learned From Hundreds of Experiments
 • Sebastian Raschka, PhD
Building LLMs from the Ground Up: A 3-hour Coding Workshop
If your weekend plans include catching up on AI developments and understanding Large Language Models (LLMs), I've prepared a 1-hour presentation on the…
 • Sebastian Raschka, PhD
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