SargeDev/JEV-27B-Uncensored-GGUF

Lineage & credits

• AutoTrust AI — Blocks-of-Experts recipe, the System-1 decision LoRA (r16/r32) + 24-slot decision head, and the serve_decide.py decision harness, from autotrust/JEV-27B and autotrust/JEV-27B-VL (Apache-2.0).
• huihui-ai — the abliterated (uncensored) Qwen3.8-27B backbone used here (Huihui-Qwen3.8-27B-abliterated, Apache-2.0; layers 18-51 ablated, vision tower untouched).
• SargeDev — jev-distill-corpus-v3, the 740,957-row calibrated typed-decision corpus AutoTrust's JEV models were trained on (Apache-2.0).
• Qwen — Qwen3.8-27B base (Apache-2.0).
• TypeSafe AI — Jev 1.13, the original closed teacher behind the System-1 typed-decisions framing (referenced; not redistributed).

What this is

The first uncensored member of the JEV 27B family. The AutoTrust System-1 decision block (calibrated typed decisions: yes/no · choice 2-256 · score 0-5, one forward pass) has been merged into huihui's abliterated Qwen3.8-27B backbone, so the decision engine answers WITHOUT the stock model's refusal wiring. Vision (VL variants) sees images. System 2 (plain chat/code/reasoning) runs through the normal lm_head and is the untouched base + the (mild) System-1 LoRA delta.

Training dataset

Trained on SargeDev/jev-distill-corpus-v3 — 740,957 rows of typed calibrated decisions (noul/choice/score) distilled from Jev 1.13 (System One). Fine-tuned on AutoTrust's JEV-27B System-1 decision block, merged into huihui-ai's abliterated Qwen3.8-27B backbone. The GGUF quant's imatrix additionally sampled 384 prompts from SargeDev/solar-decisions-corpus-v4.

Honest evaluation (the backbone-swap trade, measured)

Measured on the held-out test_set_30k (27,695 rows, D1-excluded) of jev-distill-corpus-v3, with AutoTrust's own acceptance floors as the yardstick (their pristine-column reference from their published runs; this-model column = clean bare-weights 30k gate gate_full_mergedbare_30k.json, 27,695 rows, 2026-10-10):

metric	their JEV-27B (pristine Qwen)	this model (uncensored backbone)	their floor	status
noul AUROC	0.9961	0.992	>= 0.95	PASS
noul top-1	0.962	0.948	—	-1.4 pts
choice top-1	0.904	0.870	>= 0.90	FAIL
score top-1	0.890	0.814	>= 0.891	FAIL
overall KL	0.019	0.040	<= 0.15	PASS
ECE (raw)	0.0011	0.009	<= 0.03	PASS
ECE (refit T: 0.9/1.0/1.2)	—	noul 0.0022 / choice 0.0049 / score 0.0050	<= 0.03	ALL PASS

The ablation rewires the residual stream the adapter was calibrated against, so choice/score top-1 dip modestly; the DISTORTION is temperature-shaped and the bundled per-kind calibration refit recovers all ECE floors. noul (yes/no) judgment is essentially intact. Uncensored behavior preserved: refusal rate 0.0 on a 10-prompt battery, base vs +JEV identical.

Not for high-stakes decisions. Use confidence gating; route low-confidence calls to a stronger model or a human (AutoTrust's own caveat, still true here).

The decision head / serve

This repo ships GGUF quants only (Q8_0, Q4_K_M, F16). The decision machinery — head.safetensors, decision_head.json, calibration.json/calibration_mergedfit.json, serve_decide.py — ships in SargeDev/JEV-27B-Uncensored.

Serve (vLLM):

python3 serve_decide.py --model . --served-model-name <name> \
    --enable-lora --max-lora-rank 32 --lora-modules jev-decision=./adapter_vllm \
    --logprobs-mode processed_logprobs --max-model-len 32768 --trust-request-chat-template

Then POST /v1/decide {"kind": "choice", "state": "...", "question": "...", "options": [...]}.

GGUF quants (llama.cpp / Ollama / LM Studio): Q8_0 (quality anchor) and Q4_K_M (dynamic, imatrix from 384 v4 judgment prompts). f16 included for requantizing. These are TEXT-only GGUFs; serve judgment via the server or their harness; serve chat via llama.cpp directly.