nimble

Bespoke Nimble answers typed questions about a block of text. It returns the answer plus a probability for every option and never writes prose, so there is no JSON to parse. Bespoke Labs, the team behind Curator, fine-tuned Qwen3.5-9B on 2,676 curated examples and published the weights, the training data, and the full curation pipeline. It is an open take on TypeSafe's Jev, and jaredpalmer/kev is the other one to compare it with.

Running it yourself is real work. The 9B weights alone are about 18 GB, so you need a big Apple Silicon Mac or an NVIDIA GPU with BF16, plus a merge step first. Bespoke measured roughly 106 ms per question on an H100 and 444 ms on an M5 Pro. A free public demo endpoint exists for trying it, not for production.

The interesting part is the recipe, not the model. Their contrastive data curation writes pairs of examples that differ by one fact so the label flips, which teaches the model which evidence matters. Read that before building your own classifier.

The catch: the repo has no license file. The weights are Apache-2.0 on Hugging Face, but the code has no stated terms, and the 90% agreement headline comes from 324 synthetic examples drawn from six source families. Bespoke built it in a day and says so.