jeeves

What it is

Jeeves – Reasoning improves Jev-like decision models

What the README says it does

• A 9B Jev-like model (Qwen3.5-9B, LoRA, pointer head) that reasons before it decides when it is unsure, with a block-4 diffusion drafter and the full training code and train/dev/test data.
• Reasoning only when unsure, it beats Kev-9B, Jev and JevK5 on test data it was never trained on (0.905 vs 0.822, 0.857 and 0.802) and on JevBench's public tiers (0.896 vs 0.866 for Jev and 0.861 for JevK5).
• Supports yes/no (noul), multiple-choice (choice), and rating (score) questions in the same request, through a Jev-compatible API.
• About 20 ms per request without thinking and a 1.2 s median with it on one H100 with --precision fp8. Can be sped up by truncating chain length.
• Runs on CUDA in bf16, or with FP8 linear layers (--precision fp8) on GPUs with compute capability 8.9 or higher. Inference also runs on Apple Silicon (MPS) in bf16 or FP8.

From the project’s README.

Getting started

From the README:

pip install -r requirements.txt

Where the decision sits

jeeves calls the hosted decision API rather than running its own weights. That keeps the project small and means its behaviour moves whenever the vendor ships a new model version. It also means an API key and a network round trip sit in the path of every decision.

Models it references: Jev.

How active it is

Created 2026-09-29, last pushed 2026-10-16, 432 stars and 21 forks when we read it on 2026-11-02. Everything in this category is weeks old at most, so a stale push date means something different here than it would on a mature project. Check the repository before you depend on it.