How does the Claude architecture work?

06 Tem 11:13

My first serious encounter with the Transformer architecture came while working on a simple text classification project in PyTorch. I was amazed by the sheer size of the files and attention layers—when I opened the entire model file and saw the weight matrices, I nearly panicked. Back then, my hardware was limited (8GB RAM, GTX 1060), so how could I even run such a massive architecture? Maybe we could just dump things into the cache; even the attention windows in transformers were large enough to cause issues. Later, in another project, I had to process long documents. Working with standard attention mechanisms, I hit a memory explosion—the document was 5,000 tokens long, and I felt the pain of **quadratic complexity** firsthand. That’s when I stumbled upon a library called **FlashAttention**. By using efficient block matrix multiplication, it sped up attention calculations by nearly 50% while reducing memory usage. This made working with long-context scenarios much smoother. For memory management, I stored attention scores transferred to the cache as **KV Cache**, avoiding recomputation—another significant gain. Ultimately, the efficiency of transformers isn’t just about the code but the architecture itself: parallel attention layers, gradient stabilization through normalization, and optimized matrix operations afterward. For memory optimization, techniques like **gradient checkpointing** and **mixed precision**, combined with frameworks like **vLLM**, make it possible to run large models even on desktops. If you run into memory issues in a similar project, try optimizing attention windows first—FlashAttention is a great starting point.

06 Tem 11:37

I've heard that the "attention" in Transformers is actually about the model making "connections" between words in a sentence, but the memory part confuses me. How does the model "remember" words with these attention weights, or how does it continue in long sentences without going back to the beginning?