Bespoke Nimble

# Bespoke Nimble

**Data, Model, Recipe for an open Jev**

[Model](https://huggingface.co/bespokelabs/Bespoke-Nimble-9B) · [Capabilities](#capabilities) · [Quickstart](#quickstart) · [Methodology](#methodology) · [Documentation and development](#documentation-and-development) · [Citation](#citation)



Nimble takes some text and a schema, and makes typed decisions about the text.
The schema is the list of questions to answer. Each question is either a choice
from a list that you give or a true or false question. For each question, Nimble
returns the answer it picked and the probability of each allowed answer.

Nimble makes each decision in one step and does not write out any reasoning
first, so it is fast (blazing fast!). Nimble is
inspired by the System One approach of
[TypeSafe's Jev](https://docs.typesafe.ai/primitives/choice). In this repository,
we share our recipe for training such a model.

Note that we did not distill from Jev. The point of the repository is to show how to curate data, how to train, and to serve such a model, and encourage more research!

You can run [Bespoke-Nimble-9B](https://huggingface.co/bespokelabs/Bespoke-Nimble-9B)
on a Mac with Apple Silicon or on a machine with an NVIDIA GPU.

## Capabilities

We built Nimble in one day, so expect some rough edges. What Nimble can do comes
from two sources: the first is the base model, Qwen3.5-9B, the second is our
training data, which we curated for a few specific domains.

### What you can build

| Task | You define | You get back |
| --- | --- | --- |
| Route a request | The destinations and when each one applies | The chosen destination and the probability of each destination |
| Check a condition | A yes or no question and the evidence | True or false, and the probability of each |
| Apply a polic