Recent Developments in LLM Architectures: KV Sharing, mHC, and Compressed…
From Gemma 4 to DeepSeek V4, How New Open-Weight LLMs Are Reducing Long-Context Costs
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magazine.sebastianraschka.com
23articoli totali nell'archivio
My Workflow for Understanding LLM Architectures
A learning-oriented workflow for understanding new open-weight model releases
Components of A Coding Agent
How coding agents use tools, memory, and repo context to make LLMs work better in practice
A Visual Guide to Attention Variants in Modern LLMs
From MHA and GQA to MLA, sparse attention, and hybrid architectures
A Dream of Spring for Open-Weight LLMs: 10 Architectures from Jan-Feb 2026
A Round Up And Comparison of 10 Open-Weight LLM Releases in Spring 2026
Categories of Inference-Time Scaling for Improved LLM Reasoning
And an Overview of Recent Inference-Scaling Papers
The State Of LLMs 2025: Progress, Progress, and Predictions
A 2025 review of large language models, from DeepSeek R1 and RLVR to inference-time scaling, benchmarks, architectures, and…
A Technical Tour of the DeepSeek Models from V3 to V3.2
Understanding How DeepSeek's Flagship Open-Weight Models Evolved
Beyond Standard LLMs
Linear Attention Hybrids, Text Diffusion, Code World Models, and Small Recursive Transformers
Understanding the 4 Main Approaches to LLM Evaluation (From Scratch)
Multiple-Choice Benchmarks, Verifiers, Leaderboards, and LLM Judges with Code Examples
Understanding and Implementing Qwen3 From Scratch
A Detailed Look at One of the Leading Open-Source LLMs
The Big LLM Architecture Comparison
From DeepSeek-V3 to Kimi K2: A Look At Modern LLM Architecture Design
Understanding and Coding the KV Cache in LLMs from Scratch
KV caches are one of the most critical techniques for efficient inference in LLMs in production.