autotrust/JEV-Gemma4-26B-A4B

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autotrust/JEV-Gemma4-26B-A4B

An integrated System 1 + System 2 open model on Gemma-4-26B-A4B-it — Decision Index 0.2.1: 58.05

This bundle serves two systems from one set of base weights. System 2 is the unmodified google/gemma-4-26B-A4B-it (a 26 B-parameter mixture-of-experts model with ≈ 4 B active parameters per token, text and image input): generation and reasoning, bit-identical to the base model. System 1 adds a LoRA adapter and a 24-slot decision head and returns calibrated probabilities for three typed primitives in one prefill pass:

noul: true / false
choice: 2–16 options (wider questions are read in groups of ≤ 16 plus a final)
score: 0–5

Two models, two organisations. TypeSafe Jev 1.13 is the hosted, closed model made by TypeSafe AI. autotrust/JEV-Gemma4-26B-A4B is an independent open-weights model built by AutoTrust AI; it is not affiliated with, endorsed by, or a product of TypeSafe AI.

Decision Index 0.2.1

Complete run of all 150,759 requests of suite 0.2 (150,317 scored; 0 errors, 0 unsupported), scored with the kit's score --edition 0.2.1. Results: autotrust/jev-decision-index-results (runs/jev-gemma4-26b-a4b).

	Decision Index (balanced skill)	balanced raw	breadth skill
autotrust/JEV-Gemma4-26B-A4B	58.05	67.35	56.98
TypeSafe Jev 1.13 (board)	57.91	—	—
autotrust/JEV-27B	53.30	64.32	52.21

area (skill)	Knowledge & Reasoning	Language	Retrieval & Classification	Tools & Automation	Arts & Taste
JEV-Gemma4-26B-A4B	0.430	0.636	0.679	0.697	0.415

Engine and read-out

Template bare-v1, prefixed with <bos>: [kind] … [state] … [question] … [options] A) … [decision]:
Read-out: the final-norm hidden state of the last token goes through a linear fp32 head (hidden 2,816 → 24 slots), soft-capped at 30 like Gemma's own logits; inactive slots are masked, the logits are divided by the per-kind temperature and softmaxed. Nothing is generated.
Adapter: adapter/ is merged into the bf16 backbone in memory at load (as in the Decision Index run).
More than 16 options: ⌈n/16⌉ contiguous groups read whole, then a final of 16; every option is read, none pruned.

The reference engine for the Decision Index is submissions/jev/jev_engine.py in the Decision Index kit.

Temperatures

table	noul	choice	score
`calibration.json` (default; used in the Decision Index run)	1.003	1.017	0.999
`calibration_gold.json` (calibrated against ground-truth answers)	1.214	1.098	1.000

Use calibration_gold.json when you gate automatic actions on confidence.

Usage (transformers + peft)

python

import json, torchfrom huggingface_hub import snapshot_downloadfrom peft import PeftModelfrom safetensors.torch import load_filefrom transformers import AutoTokenizer, Gemma4ForConditionalGeneration
d = snapshot_download("autotrust/JEV-Gemma4-26B-A4B")tok = AutoTokenizer.from_pretrained(d)base = Gemma4ForConditionalGeneration.from_pretrained(d, dtype=torch.bfloat16, device_map="cuda")# System 2: `base` is gemma-4-26B-A4B-it unchanged — use base.generate(...) (text or images).
# System 1: adapter merged in memory + headm = PeftModel.from_pretrained(base, f"{d}/adapter").merge_and_unload().eval()backbone = m.modeljc, T = json.load(open(f"{d}/judge_config.json")), json.load(open(f"{d}/calibration.json"))["per_kind"]head = load_file(f"{d}/head.safetensors"); W, b = head["proj.weight"].cuda(), head["proj.bias"].cuda()
@torch.no_grad()def decide(kind, state, question, options):    lines = options if kind != "choice" else [f"{'ABCDEFGHIJKLMNOP'[i]}) {o}" for i, o in enumerate(options)]    text = f"[kind] {kind}\n[state] {state}\n[question] {question}\n[options]\n" + "\n".join(lines) + "\n[decision]:"    ids = torch.tensor([[tok.bos_token_id] + tok.encode(text, add_special_tokens=False)], device="cuda")    h = backbone(input_ids=ids, use_cache=False).last_hidden_state[0, -1].float()    z = 30.0 * torch.tanh((W @ h + b) / 30.0)    s, _ = jc["slots"]["ranges"][kind]    return dict(zip(options, torch.softmax(z[s:s + len(options)] / T[kind], 0).tolist()))
print(decide("noul", "Customer says the parcel arrived damaged and wants their money back.",             "Is the customer asking for a refund?", ["false", "true"]))

Training data (disclosure)

System 1 was trained on teacher distributions and ground-truth decision data. The ground-truth data includes the public training splits of some datasets whose test splits the Decision Index uses; no test split of any benchmark was used, and suite items were excluded before training. The list has been provided to the Decision Index maintainers with the submission. MMMU / MMMU-Pro are not valid evaluations for this model.

Limitations

Where the teacher is wrong, System 1 often is too.
Weaker than JEV-27B on long structured inputs (e.g. the Decision Index's Home appliance simulator and POP909).
HLE: below chance, like every open entry on the board; do not use System 1 for expert-level questions.
noul and score accept only their canonical options.
English-centric; not for high-stakes decisions without confidence gating.

Files

model-*.safetensors · config.json · processor_config.json · tokenizer* · chat_template.jinja                          google/gemma-4-26B-A4B-it, unchanged (System 2; text + image input)adapter/                  System 1 LoRA (peft)head.safetensors          24-slot decision head (fp32): proj.weight [24, 2816], proj.bias [24]judge_config.json         slot layout, verbalizer ids, softcap, read-out, provenancecalibration.json          per-kind temperatures (default)calibration_gold.json     per-kind temperatures calibrated against ground-truth answersreports/                  Decision Index scores

License

Apache-2.0 for the adapter, head and calibration files; base model under the Gemma 4 terms (https://ai.google.dev/gemma/docs/gemma_4_license). Not affiliated with TypeSafe AI.