Open source as it stands is insufficient to capture all parts of the pipeline. With code, you just compile and run it. Models require carefully curated trainin

Open source as it stands is insufficient to capture all parts of the pipeline. With code, you just compile and run it. Models require carefully curated training data (lots of it), training code, final weights, production inference code, etc. The danger is the world gets drunk on Meta's models, but then Meta pulls the plug. Meta is doing the world a lot of good with Llama, but we'll be left in a precarious position should they stop being so generous. The next generation of LLMs might make Llama completely pointless. Meta thinks a great deal about the strategy in this space. They license other weights and models as CC-BY-NC and other non-free licenses because they know that they have the SOTA models across several categories and that there's no other competition in the marketplace. They want to retain their advantage when it benefits them. So what happens when the LLM space shifts and Meta no longer needs to appear to be open source? What if the value switches over to diffusion models and away from LLMs, which is an area where Meta shines? Llama is a gift we can't replicate or properly repair and extend. And we need to be careful.