Thinking Machines Lab Drops Its First Model
Inkling, a 975-billion-parameter open source model, was trained to understand video and audio. It could help Thinking Machines establish itself among competitors like Anthropic and OpenAI.
Thinking Machines Lab, an artificial intelligence company started by exiles from OpenAI , has released its first model, called Inkling. The startup’s new model is open-weight, which means that researchers and startups will be able to download and modify it.
In a a blog post , the company says Inkling was trained from scratch to make sense of audio and video input as well as text. It says that while Inkling isn’t the best model on popular benchmarks, it performs well at many tasks, and is capable of advanced reasoning and coding. Like many open-weight models, Inkling is relatively large—975 billion parameters—and needs to run on a cluster of specialized chips.
In a sign of how AI models are increasingly being used to build AI , the lab also used Inkling to fine-tune and improve itself.
The release could help Thinking Machines establish itself as a legitimate player in the frenetic and big-spending AI race. Open-source models have proven popular because they’re cheaper to run than closed models, which can typically only be accessed for a fee. Open-source models can also be more easily modified for different tasks. The best open-weight models currently come from China, but Thinking Machines says Inkling offers a level of performance similar to those models.
The release of an open-weight model fits with a vision for AI that Thinking Machines laid out in a recent blog post . The company said the technology shouldn’t be controlled by just a few companies and should be decentralized so that more people can build their own models with their own data.
According to a company source who requested anonymity to discuss the development process, researchers discovered a strange phenomenon while training Inkling. Like other models, it usually provides a natural language explanation for its complex reasoning. Inkling decided to do away with this in the name of efficiency. “It determined that the grammar was overhead, which is interesting,” the source says. The company reinstated natural language reasoning to make the models' decisions more explainable, the person says.
