Thinking Machines Lab, a heavily funded startup cofounded by prominent researchers from OpenAI, has revealed its first product—a tool called Tinker that automates the creation of custom frontier AI models.
“We believe [Tinker] will help empower researchers and developers to experiment with models and will make frontier capabilities much more accessible to all people,” said Mira Murati, cofounder and CEO of Thinking Machines, in an interview with WIRED ahead of the announcement.
Big companies and academic labs already fine-tune open source AI models to create new variants that are optimized for specific tasks, like solving math problems, drafting legal agreements, or answering medical questions.
Typically, this work involves acquiring and managing clusters of GPUs and using various software tools to ensure that large-scale training runs are stable and efficient. Tinker promises to allow more businesses, researchers, and even hobbyists to fine-tune their own AI models by automating much of this work.
Essentially, the team is betting that helping people fine-tune frontier models will be the next big thing in AI. And there’s reason to believe they might be right. Thinking Machines Lab is helmed by researchers who played a core role in the creation of ChatGPT. And, compared to similar tools on the market, Tinker is more powerful and user friendly, according to beta testers I spoke with.
Murati says that Thinking Machines Lab hopes to demystify the work involved in tuning the world’s most powerful AI models and make it possible for more people to explore the outer limits of AI. “We’re making what is otherwise a frontier capability accessible to all, and that is completely game-changing,” she says. “There are a ton of smart people out there, and we need as many smart people as possible to do frontier AI research.”
Tinker currently allows users to fine-tune two open source models: Meta’s Llama and Alibaba’s Qwen. Users can write a few lines of code to tap into the Tinker API and start fine-tuning through supervised learning, which means adjusting the model with labeled data or through reinforcement learning, an increasingly popular method for tuning models by giving them positive or negative feedback based on their outputs. Users can then download their fine-tuned model and run it wherever they want.
The AI industry is watching the launch closely—in part due to the caliber of the team behind it.
Read the full article here
