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Roadmap update: Tinker launched into private beta a month ago, and we've seen hundreds of builders and researchers train and experiment with models on our platform. This month we've added new models, expanded the cookbook, and improved overall capacity and performance.

As part of our commitment to open and collaborative science, we’re expanding free access to Tinker for researchers and instructors with our grants program.

Combining the benefits of RL and SFT with on-policy distillation, a promising approach for training small models for domain performance and continual learning.

We're happy to support the Human Centered LLMs course, on topics close to our hearts. We'd like to support more classes with free credits for students to use on assignments and projects. If you're an instructor interested in using Tinker in your course, please reach out to

Tinker is cool. If you're a researcher/developer, tinker dramatically simplifies LLM post-training. You retain 90% of algorithmic creative control (usually related to data, loss function, the algorithm) while tinker handles the hard parts that you usually want to touch much less

Very excited to see the Tinker release! @pcmoritz and I had a chance to experiment with the API. It does a nice job of providing flexibility while abstracting away GPU handling. Here's a simple example showing how to generate synthetic data and fine tune a text to SQL model.

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

Today on Connectionism: establishing the conditions under which LoRA matches full fine-tuning performance, with new experimental results and a grounding in information theory

Sharing our second Connectionism research post on Modular Manifolds, a mathematical approach to refining training at each layer of the neural network

At Thinking Machines, our work includes collaborating with the broader research community. Today we are excited to share that we are building a vLLM team at @thinkymachines to advance open-source vLLM and serve frontier models. If you are interested, please DM me or @barret_zoph!

A big part of our mission at Thinking Machines is to improve people’s scientific understanding of AI and work with the broader research community. Introducing Connectionism today to share some of our scientific insights.

Thinking Machines Lab exists to empower humanity through advancing collaborative general intelligence. We're building multimodal AI that works with how you naturally interact with the world - through conversation, through sight, through the messy way we collaborate. We're

If you’d like to be part of a team making huge ambitious bets on multimodality among other things & work with Rowan, we’re hiring!

Follow us @thinkymachines for more updates over the coming weeks

If you’re interested in joining our team, consider applying here

I started Thinking Machines Lab alongside a remarkable team of scientists, engineers, and builders. We're building three things: - Helping people adapt AI systems to work for their specific needs - Developing strong foundations to build more capable AI systems - Fostering a

I shared the following note with the OpenAI team today.

All Plus and Team users in ChatGPT

The Safety and Security Committee—a committee established to review critical safety and security issues—has made recommendations across five key areas, which we are adopting.