And How They Stack Up Against Qwen3| magazine.sebastianraschka.com
From DeepSeek-V3 to Kimi K2: A Look At Modern LLM Architecture Design| Ahead of AI
A topic-organized collection of 200+ LLM research papers from 2025| Ahead of AI
KV caches are one of the most critical techniques for efficient inference in LLMs in production.| Ahead of AI
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 of fun doing it.| Ahead of AI
Understanding GRPO and New Insights from Reasoning Model Papers| Ahead of AI
Six influential AI papers from July to December| Ahead of AI
Six influential AI papers from January to June| Ahead of AI
A curated list of interesting LLM-related research papers from 2024, shared for those looking for something to read over the holidays.| Ahead of AI
Finetuning a GPT Model for Spam Classification| Ahead of AI
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 development cycle of LLMs, covering everything from architectural implementation to the finetuning stages.| Ahead of AI
A Deep Dive into the Lifecycle of LLM Development| Ahead of AI
Discussing the Latest Model Releases and AI Research in May 2024| Ahead of AI
Discussing the Latest Model Releases and AI Research in April 2024| Ahead of AI
What are the different ways to use and finetune pretrained large language models (LLMs)? The most common ways to use and finetune pretrained LLMs include a feature-based approach, in-context prompting, and updating a subset of the model parameters.| magazine.sebastianraschka.com
Inference-Time Compute Scaling Methods to Improve Reasoning Models| magazine.sebastianraschka.com
Welcome to the next stage of large language models (LLMs): reasoning. LLMs have transformed how we process and generate text, but their success has been largely driven by statistical pattern recognition. However, new advances in reasoning methodologies now enable LLMs to tackle more complex tasks, such as solving logical puzzles or multi-step arithmetic. Understanding these methodologies is the central focus of this book.| magazine.sebastianraschka.com
Methods and Strategies for Building and Refining Reasoning Models| magazine.sebastianraschka.com
A Look at How Moderns LLMs Are Trained| magazine.sebastianraschka.com
The Latest Research in Instruction Finetuning| magazine.sebastianraschka.com
Model Merging, Mixtures of Experts, and Towards Smaller LLMs| magazine.sebastianraschka.com
Things I Learned From Hundreds of Experiments| magazine.sebastianraschka.com
Using Mixed-Precision and Fully Sharded Data Parallelism| magazine.sebastianraschka.com
I frequently reference a process called Reinforcement Learning with Human Feedback (RLHF) when discussing LLMs, whether in the research news or tutorials.| magazine.sebastianraschka.com
Several people asked me to dive a bit deeper into large language model (LLM) jargon and explain some of the more technical terms we nowadays take for granted. This includes references to "encoder-style" and "decoder-style" LLMs. What do these terms mean?| magazine.sebastianraschka.com
In the last couple of months, we have seen a lot of people and companies sharing and open-sourcing various kinds of LLMs and datasets, which is awesome.| magazine.sebastianraschka.com
An introduction to the core ideas and approaches| magazine.sebastianraschka.com
