We need a different way to interact with powerful models.
Instead of the chat API, you submit a workflow. The model executes it and returns the results.
The "support" of intelligence is controlled without compromising prediction accuracy.
This is the vision behind TypeLLM.
Closed frontier LLMs are dangerous
In most post-training reasoning efforts, a pre-trained base model trains on its own chains of thought (CoTs) when a final-answer verifier says the answer is correct. This is the basic principle of RL methods based on outcome rewards, used for
In China, one surprisingly important AI task is counting barbecue skewers. At many Chinese barbecue restaurants, staff count the used skewers to calculate the bill. So you need a model that can accurately count them, which might explain why Chinese AI models tend to be particularly good at this task.
New in the TypeLLM API today: Array generation is now more complete and faster — with up to 34% higher recall and 27% lower latency.
Try it for object detection or document parsing:
typellm.ai/cookbook/dense…
Jev 1.14 from @typesafeai can't do this.
TypeLLM can detect every vehicle in an image and return an array of bounding boxes — including @Tesla's new flying Roadster.
Try it now: typellm.ai/cookbook/locat…
Jev 1.14 is a big improvement, but it still has a limit of 255 classes.
What if you need to classify across more than 255 classes, such as time zones?
TypeLLM supports up to 512 classes natively.
Try it now: typellm.ai/cookbook/class…
@Ranicket@typesafeai@Tesla No, it can't; it only supports one box per question docs.typesafe.ai/primitives/box. There is no way it can count and generate all the boxes of the same category (e.g. cars). You can try yourself by uploading an image with two or more cats and let it generate all the boxes.
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