How Hugging Face's Transformers Added UltraBERT with Layer-Specific Rotary Embeddings
Why UltraBERT Integration Matters The Hugging Face Transformers library is a cornerstone for NLP research and applications, providing a unified inte…
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They Put 7 Attention Mechanisms on a Latin Square. Then Removed Them One by One.
Since GPT, nearly every Transformer repeats the same attention mechanism at every layer. Forty-eight identical blocks, differing only in learned wei…
Черный лебедь в мире LLM или когда убьют трансформер?
И снова возвращаюсь к теме больших языковых моделей. В своей достаточно старой статье я писал, что текущая архитектура БЯМ(LLM) — это гонка в тупик.…
How a Baseten Engineer Traced 7 Years of Attention Mechanism Evolution -- From GPT-2 to Kimi K3, in Runable PyTorch
Last week, a Baseten inference engineer who goes by @waterloo_intern published a technical blog post titled "22,580: From GPT-2 to Kimi K3, Explaine…
How a Transformer Plays Tic-Tac-Toe
An interactive guide to the architecture behind modern language models. Instead of predicting the next word, this Transformer predicts the next move…
How Modern Transformer Blocks Work — From RMSNorm to MoE
The original Transformer idea is still alive. But modern LLM blocks are not just the 2017 Transformer copied and scaled. They are engineered for dee…
Why KV Cache Matters — How MQA, GQA, and MLA Make LLM Inference Faster
LLMs generate text one token at a time. That sounds simple. But without KV Cache, every new token would repeat a lot of old work. That is why infere…
[Day 7] Does Giving an AI More 'Thinking Time' Really Make It Smarter? Training an OpenMythos-Style Mini Model on DGX
[Day 7] Does Giving an AI More "Thinking Time" Really Make It Smarter? Training an OpenMythos-Style Mini Model on DGX Intro Day 7! Reddit kept surfa…