Instructions to use frontier-infra/jebadiah-27b-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use frontier-infra/jebadiah-27b-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir jebadiah-27b-MLX frontier-infra/jebadiah-27b-MLX
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Jebadiah 27B MLX
MLX builds of Jebadiah 27B for Apple silicon, one folder per precision (8bit/, 4bit/, group size 64).
Jebadiah answers a typed question (choice, noul or score) with a probability for every option, read from one
forward pass. Nothing is generated. Code, trainer and evals: getainode/jebadiah.
Files
Every file was checked on the 260 held-out questions the merged weights were checked on, and compared with the merged bf16 weights and with the training run's own eval records.
| File | Size | Same answer as bf16 | Same as the run | choice + noul | score | Prob. diff median / max |
|---|---|---|---|---|---|---|
8bit/ |
28.6 GB | 259 / 260 | 258 / 260 | 173 / 173 | 85 / 87 | 0.003 / 0.050 |
4bit/ |
15.1 GB | 248 / 260 | 247 / 260 | 169 / 173 | 78 / 87 | 0.023 / 0.177 |
| bf16 weights | 55.6 GB | 259 / 260 | 173 / 173 | 86 / 87 | 0.002 / 0.019 |
Which one: 8bit if your Mac has the memory; 4bit when it does not. A folder needs about its own
size in unified memory, plus about 1 GB for a 2k-token prompt.
4bit changes 12 of 260 answers against bf16 (8 on score questions) and moves probabilities more (median 0.023, max 0.18). Use it only when a larger build does not fit.
Run it
The script renders the prompt exactly as AINode does, runs one forward pass with mlx-lm, multiplies the
last hidden state by the option labels' output-head rows in fp32 and applies temperatures.json
(choice 1.2321, noul 1.297, score 0.7558). Needs mlx-lm 0.31 or newer (qwen3_5 support).
pip install "mlx-lm>=0.31"
hf download frontier-infra/jebadiah-27b-MLX --include "8bit/*" "scripts/*" temperatures.json --local-dir jebadiah-27b-MLX
cd jebadiah-27b-MLX
python scripts/decide_mlx.py --model 8bit --request scripts/example-request.json
--no-temperatures returns the raw probabilities.
On example-request.json (8bit/):
{
"route": {"type": "choice", "choice": "billing", "confidence": 0.646831, "probabilities": {"billing": 0.764554, "support": 0.03266, "sales": 0.202786}},
"urgent": {"type": "noul", "noul": 0.200315}
}
How it was measured
Jevals PubMedQA, Banking77 (77 options) and HelpSteer2, plus Nimble: the merge check's fixed sample (seed
20260925), the run's option order and the run's temperatures, so the probability differences compare like with like (the shipped temperatures.json since 1c0d794f uses 0.7558 for score; top picks do not depend on it). "Same answer" is the top option; "prob. diff" is the
largest change on any option against the run's CUDA record. Records: eval/agreement-*.json.
License
Apache-2.0, as the base model. Made in Texas.
Quantized