What Actually Changed Under the Hood
While everyone's raving about Llama 3, let's examine the specs. Yes, we get a larger context window and grouped-query attention (GQA), but fundamentally this remains transformer architecture. The real upgrade? Training data quality. Here's the repo for hands-on exploration. Don't expect leaps in reasoning capabilities—it's still an autoregressive model with familiar limitations. Meta continues its 'open-but-not-really' dance. The license is more permissive but still blocks training competitor models. Startups get breathing room, academia can work with it, but calling this 'open' feels disingenuous. "Open-weight ≠ open-source"—my new mantra when reading headlines about 'openness revolutions'. The new checkpoint works well for: But don't expect miracles from basic LoRA adapters—data quality remains king.License: Progress or Marketing?
Where Fine-Tuning Makes Sense Today