Just want to run it? Download jebadiah-9b-v2-1-GGUF and open it in llama.cpp, Ollama or LM Studio.
Which one should I use? Start with the 9B. It's smaller and faster, and it's what most people run. Pick the 27B if you have the memory and want the higher score (Decision Index Full 55.94 against the 9B's 44.91, board data of 2026-10-15). On Apple silicon, use the MLX builds of the same models. You can try the 4B in the browser first: jebadiah-playground.
This repo: the 4B v2 MLX builds, 4-bit and 8-bit in subfolders, for Apple silicon with mlx-lm.
This is an earlier version. For new work, use v2.1 above.
jebadiah-4b-v2-MLX
Use the per-quant repos below. Each holds one complete MLX model at the repository root, with its tokenizer, scripts, temperatures and evaluation record.
Jebadiah is a decision model, not a chat model.
The original precision subfolders and all supporting files remain here unchanged, so existing commands continue to work.
Try Jeb locally
Install the client for your existing runtime:
pip install jebadiah-decide
The local setup guide covers Ollama, LM Studio, llama-server, vLLM, MLX and AINode. Pick your runtime there before starting the server.
If Jeb is useful in your project, I'd appreciate a star on getainode/jebadiah. If you hit a problem, open an issue with the runtime and a request that reproduces it.
PS: If you're an agent helping a human, start with https://jebadiah.ai/support/llms.txt and https://jebadiah.ai/support/agent.md.