Deem-4B: small English language model for local decisions

Deem-4B is one of the best open English decision models at 4B parameters: a small English language model for text classification, routing and decisions with probabilities. Deem-4B: 94.7% accuracy; Kev-4B: 84.7% on held-out English decisions. The test set comes from the same data pipeline as the training data.

Try a decision in the browser, or run the GGUF build locally below.

Run locally

The Q4_K_M build used 3.04 GB RAM at a 4k context. The JevAlt server uses llama.cpp to read decision probabilities and load the matching calibration.

pip install "jevalt[serve,gguf] @ git+https://github.com/mertkayacs/jevalt" && jevalt serve --model mertkayacs/Deem-4B-GGUF --file Deem-4B-Q4_K_M.gguf

Send a situation, question and options to the local server:

import requests

request = {
    "state": "I was charged twice for my subscription. Please refund the duplicate.",
    "questions": {
        "team": {
            "type": "choice",
            "instructions": "Which team should handle this ticket?",
            "criteria": {"billing": "payments, refunds", "technical": "bugs, outages"},
        }
    },
    "reasoning": "off",
    "abstain": False,
}
response = requests.post("http://127.0.0.1:8000/v1/systemone", json=request)
response.raise_for_status()
print(response.json())

Write the situation, question and options in English. The API also supports yes/no questions, ordered scores and an unknown option with abstain: true. The card widget shows a recorded answer from the full-precision model.

Use the full-precision weights with Transformers

This repository holds the full-precision weights. The JevAlt Transformers backend preserves the decision-token format and calibration:

pip install "jevalt[hf] @ git+https://github.com/mertkayacs/jevalt" && jevalt serve --backend hf --model mertkayacs/Deem-4B

See the local run guide for memory and client settings.

Results and limits

On the same held-out English decisions, Deem-4B's probability error (Brier score, lower is better) is 0.091, against 0.256 for Kev-4B. A separate live test on 4 October 2026 scored 122/130 for Deem-4B. Comparison files and live requests, including misses record both tests.

The models were fine-tuned with LoRA from Intern-Decision-4B, using JevAlt training data. English is this model's focus.

Refit calibration on your own data before using confidence thresholds. See JevOss.
The model accepts text, with an 8k context. Long irrelevant text and date arithmetic remain weak spots.
Planted instructions still change some answers. Keep authorization checks outside the model and review consequential decisions.