How tbd-0 stays small.

Constant context size

Most language models treat a conversation as one growing block of text. On every turn, the whole history goes through the model again. Long conversations get slower and more expensive, and details in the middle get lost.

tbd-0 works with a context of fixed size. What the model needs right now stays in the context. Everything else moves into memory and comes back when it becomes relevant.

The result: turn 50 costs the same as turn 1, conversations have no length limit, and nothing gets lost in the middle.

Three more ideas

Expert models

A general-purpose model has to know a little about everything. An expert model only has to know one field well. That makes it smaller and cheaper to run, and on tasks in its field it outperforms larger general-purpose models.

Help us build one · See the models

Retrieval models

Facts don’t have to live in the model’s weights. Our retrieval models keep knowledge in a separate layer and look it up when needed. Knowledge can be updated without retraining, and the model itself stays small.

Distributed mixture of experts

A mixture-of-experts model splits its parameters into specialised parts and only activates the ones a request needs. We spread these experts across machines, so each request uses a fraction of the compute and the system grows by adding machines, not by building bigger ones.