Instructions to use frontier-infra/jebadiah-9b-v2-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use frontier-infra/jebadiah-9b-v2-MLX with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("frontier-infra/jebadiah-9b-v2-MLX") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use frontier-infra/jebadiah-9b-v2-MLX with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "frontier-infra/jebadiah-9b-v2-MLX" --prompt "Once upon a time"
- Atomic Chat
jebadiah-9b-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.
Quantized