Mira Murati, former CTO of OpenAI, has introduced the first product from her startup, Thinking Machines Lab, with the launch of an API called Tinker. The platform is designed to simplify the fine-tuning of both large and small open-weight AI models, removing the complexity of distributed training while giving researchers full control over algorithms and data.
Murati announced the launch on X, saying, “Today we launched Tinker. Tinker brings frontier tools to researchers, offering clean abstractions for writing experiments and training pipelines while handling distributed training complexity. It enables novel research, custom models, and solid baselines. Excited to see what people build.”
Currently in private beta, Tinker allows developers to scale effortlessly from lightweight models to massive architectures such as Qwen-235B-A22B with just a single line of Python code. The service will initially be free, with usage-based pricing expected in the coming weeks.
Early adopters include Princeton’s Goedel Team, which is using Tinker for training mathematical theorem provers; Stanford’s Rotskoff Chemistry group for reasoning tasks; Berkeley’s SkyRL group for multi-agent reinforcement learning experiments; and Redwood Research for tackling complex AI control problems.
To further support researchers, Thinking Machines Lab has also introduced the Tinker Cookbook, an open-source library featuring modern post-training methods built on the Tinker API. This resource enables seamless experimentation and customization, making it easier for developers to push the boundaries of AI research.
The launch of Tinker positions it as a potential game-changer in the fine-tuning of open-weight AI models, providing a robust foundation for both academic and applied AI projects. By combining simplicity, scalability, and flexibility, Tinker aims to empower a new generation of AI researchers and innovators.
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