I’m very excited that we @baselabs are today announcing our research efforts toward a common safety standard for open models, and our aim to build this into the infrastructure that serves these models at @baseten Our research will concern model alignment (and the training methods to meaningfully achieve this), interpretability and of course investigations where open models fail. As with our original promise, Base Labs plans to publish all the methods, code and evaluations behind our stack, such that the community can collectively build the safety standard for open models. We are initially excited to partner with @huggingface and @GoodfireAI to kickstart this, and invite others to join our journey!

This is great news out of @huggingface. Similar to Thomas, I also doubt that alignment will easily be solved by the frontier labs. The more alignment research we can do and share in the open, the better open-source models will be for it. Naturally, it is one of our core agenda items at @baselabs.

Today we launch @baselabs. It is time to take a very very big swing. Over the past few years, I’ve been astonished that seemingly anything the AI community has pursued, in the big or small labs, has worked. I think all scientists are when their experiments actually work and their theories are validated. But like come on? Backprop through a trillion parameters (yes I know only the active ones), add some correction to a memory block, let a sigmoid function decide what to forget? How does this all actually work? And yet “we can only see a short distance ahead, but we can see plenty there that needs to be done” (Turing, 1950). Base Labs is built on the premise that we all deserve to have a say in advancing the science of intelligence. That yes, we have made many big strides, but that the fragments of our success in training and inference have been scattered and only pulled together through the sheer willpower of a few incredible researchers and engineers. And, at the same time there are many fragments we cannot see, and a science that is being built and organised away from our eyes. Today we announce our research mission to advance and democratize open-source intelligence. Base Labs is our bet that there is much work to be done to train and serve SOTA models, and that this work should be organised (like a science) and for everyone to access and contribute to. We will work across model specialization, learning, memory, reasoning, and serving, and in doing so publish all our results (positive or negative), experiments and data for everyone to learn from and work on. Join us!