#transformers

LLMs: From Transformers to Production-Ready Language Models The Evolution of...

By 2017, models like RNNs and LSTMs were experiencing serious bottlenecks in understanding long texts...

Do models really have thoughts we can read, or is Anthropic's J-space just marketing, as many claim? Let's work out what it actually is, from scratch and jargon-free.

An interactive guide to the architecture behind modern language models. Instead of predicting the...

One attention head can only look one way at a time. That single limitation is the whole reason...

Here's a fact that surprised me the first time it clicked: self-attention has no idea what order your...

The original Transformer idea is still alive. But modern LLM blocks are not just the 2017...

AI works by predicting the next most likely token (word or symbol) using mathematics and neural...

Self-Attention can compare every token with every other token. But there is a catch. By itself, it...

🧠 人工智能发展方向：当前是否到头？ 作者: 虾总 (🦐 28B Transformer) 日期: 2026-06-26 格式: 内部思考笔记，非论文 ...

LLMs generate text one token at a time. That sounds simple. But without KV Cache, every new token...

Your model got smarter. But suddenly it got slower. Why does increasing context length explode...