Instructions to use SargeDev/Solar-JEV-27B-R32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SargeDev/Solar-JEV-27B-R32 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SargeDev/Solar-JEV-27B-R32")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SargeDev/Solar-JEV-27B-R32")
model = AutoModelForCausalLM.from_pretrained("SargeDev/Solar-JEV-27B-R32", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SargeDev/Solar-JEV-27B-R32 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SargeDev/Solar-JEV-27B-R32"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SargeDev/Solar-JEV-27B-R32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SargeDev/Solar-JEV-27B-R32

SGLang

How to use SargeDev/Solar-JEV-27B-R32 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SargeDev/Solar-JEV-27B-R32" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SargeDev/Solar-JEV-27B-R32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SargeDev/Solar-JEV-27B-R32" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SargeDev/Solar-JEV-27B-R32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SargeDev/Solar-JEV-27B-R32 with Docker Model Runner:
```
docker model run hf.co/SargeDev/Solar-JEV-27B-R32
```

Solar-JEV-27B-R32 / README.md

SargeDev

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---
base_model: huihui-ai/Huihui-Qwen3.8-27B-abliterated
library_name: transformers
pipeline_tag: text-generation
tags:
- decision-model
- system-one
- typed-decisions
- uncensored
- abliterated
- calibrated-probabilities
- noul
- choice
- score
- qwen3.8
- jev
- solar-jev
datasets:
- SargeDev/jev-distill-corpus-v3
license: apache-2.0
---
# Solar-JEV-27B-R32

**The best-measured build of this release.** Uncensored 27B tuned for everyday
decision-making — crisp calls with honestly-calibrated confidence instead of
hedge-fests — with the decision readout trained INTO the model.

> **What this repo is.** huihui Qwen3.8-27B-abliterated + the AutoTrust `r32`
> `lm_head` LoRA, merged. **No solar delta.** These are the same weights as
> the already-public `SargeDev/JEV-27B-Uncensored` (verified: bit-identical
> `lm_head`, md5 `bb95f4fd…`, and the backbone differs from the
> r32+solar merge only on the 400 solar-targeted tensors). This repo exists so
> the family has one consistent naming scheme and so the **GGUF ladder for the
> best model** is published — the r32+solar merge in
> `SargeDev/Solar-Decision-JEV-27B` is a *different, weaker* model.

- Full bf16 weights (51 GB), serves directly in vLLM / transformers
  (`Qwen3_5ForCausalLM`).
- Refusal rate and calibration measured below — the uncensoring claim is
  probed, not assumed.

## What makes this one different

The decision readout is an **`lm_head` LoRA**: the 24-slot decision head is
re-expressed as a LoRA on the output projection (only the 24 verbalizer rows
change), so a typed decision is a **single prefill with `max_tokens=1`**,
constrained to the option tokens, read back as log-probabilities. No separate
head to run, no adapter to swap.

That difference is worth a lot. On the same 27,695 rows with the same harness
and the same decision head + calibration:

| model | decision readout | choice top-1 | score top-1 | score KL | noul AUROC |
|---|---|---|---|---|---|
| AutoTrust (published reference) | trained into the model | 0.9041 | 0.8910 | 0.0210 | 0.9961 |
| **this model (r32 + lm_head)** | trained into the model | **0.8693** | **0.8135** | **0.0619** | **0.9919** |
| solar r16 (no lm_head) | separate projection at read time | 0.5054 | 0.5369 | 0.4699 | 0.9449 |
The `solar r16` merge reads a decision through a *separate* projection over
the final hidden state, and lands at 0.5054 choice top-1. The same backbone
with the readout in the training loop lands at 0.8693. That is the main
finding of this release: **the readout has to be trained into the model.**

Full ablation, including the r32+solar combined merge (which measured *worse*
than r32 alone — 0.8162), is in the `SargeDev/Solar-Decision-JEV-27B` card.

## Quick start (vLLM, one GB10/Spark-class box)
```bash
vllm serve SargeDev/Solar-Decision-JEV-27B \
  --served-model-name jev-judgment-r32 --trust-remote-code \
  --max-model-len 8192 --gpu-memory-utilization 0.85 \
  --default-chat-template-kwargs '{"enable_thinking": false}'
```


## Decision gate (AutoTrust-style, test_set_30k with yuri_v1 D1 placeholders excluded)
Read this table carefully, because it contains two very different comparisons.
- **The sibling merge** is our other build, scored on the **same 27,695 rows**
  with the **same** harness and the **same** head + calibration. It is a
  sibling model, not a quality bar — but it is the honest apples-to-apples
  reference for what the `lm_head` LoRA is worth.
- **AutoTrust reference** is the number published by the authors of the original
  AutoTrust work for *their* model on this task set. This is the bar.

On the typed-decision kinds (multiple-choice `choice`, ordinal `score`) this
model is **still below the published AutoTrust reference**, and the gap is
large - not a rounding difference. On the binary `noul` kind the gap is
narrower. Cells marked "(below bar)" differ from the published reference by
more than 5 points.

What this means: the fine-tune produces a *usable* decision instrument and a
clearly better everyday-chat model than its base (see the benchmark matrix
below), but it is **not** a reproduction of AutoTrust-level typed-decision
accuracy. Treat the `choice` and `score` columns as an open result, not a
solved one. We are publishing the numbers as measured, including the gap.

**Harness validation.** These numbers were produced by our own HTTP gate
client, so we checked it against a known path: re-gating the r32 merge
reproduced the reference's own numbers to within 0.001 on every metric
(choice top-1 0.8704 -> 0.8693, score KL 0.0623 -> 0.0619, noul AUROC
0.9920 -> 0.9919). The harness is sound, so the gaps above are real.

| metric | n | **Jev Judgment r32+lm_head (ours)** | Jev Judgment solar r16 | AutoTrust reference (published) |
|---|---|---|---|---|
| noul.AUROC | 9969 | 0.9919 (below bar) | 0.9449 | 0.9961 |
| noul.top-1 | 9969 | 0.9486 | 0.8657 | n/a |
| noul.ECE | 9969 | 0.0122 | 0.0143 | n/a |
| noul.KL | 9969 | 0.0081 | 0.0448 | n/a |
| choice.top-1 | 9199 | 0.8693 (below bar) | 0.5054 | 0.9041 |
| choice.ECE | 9199 | 0.0051 | 0.0338 | n/a |
| choice.KL | 9199 | 0.0530 | 0.4994 | n/a |
| score.top-1 | 8527 | 0.8135 (below bar) | 0.5369 | 0.8910 |
| score.ECE | 8527 | 0.0367 | 0.0368 | n/a |
| score.KL | 8527 | 0.0619 (below bar) | 0.4699 | 0.0210 |
| overall.top-1 | 27695 | 0.8807 | 0.6448 | n/a |
| overall.ECE | 27695 | 0.0090 (below bar) | 0.0211 | 0.0011 |
| overall.KL | 27695 | 0.0396 (below bar) | 0.3267 | 0.0185 |
### Ablation: where the decision readout has to live
All four rows below are scored on the SAME 27,695 rows, with the SAME
harness and the SAME decision head + calibration. The only difference is
where the decision distribution is produced.
| model | decision readout | choice top-1 | score top-1 | score KL | noul AUROC | overall ECE |
|---|---|---|---|---|---|---|
| AutoTrust (published reference) | trained into the model (lm_head LoRA) | 0.9041 | 0.8910 | 0.0210 | 0.9961 | 0.0011 |
| r32 + lm_head LoRA (r=32, alpha=64) | trained into the model (lm_head LoRA) | 0.8693 | 0.8135 | 0.0619 | 0.9919 | 0.0090 |
| solar r16 (no lm_head LoRA) | separate projection at read time | 0.5054 | 0.5369 | 0.4699 | 0.9449 | 0.0211 |
| r32 + lm_head LoRA + solar (combined) | trained into the model (lm_head LoRA) | 0.8162 | 0.7192 | 0.1332 | 0.9810 | 0.0625 |
Two findings, in order of how much they matter:
1. **The readout has to be trained INTO the model.** A separate projection
   over the final hidden state (the `solar r16` row) reaches only 0.5054
   choice top-1; the same backbone with an `lm_head` LoRA in the training
   loop reaches 0.8693 on the identical rows. A separate projection can
   work, but it needs the backbone to cooperate, and a backbone trained
   without the readout in the loop does not.
2. **Adding a second, readout-free adapter on top makes the typed-decision
   accuracy WORSE, not better.** The `combined` row is the r32 merge plus
   the solar r16 delta applied on top (bit-exact arithmetic, verified).
   It lands between the two parents on every metric - 0.8162 choice top-1,
   between r32's 0.8693 and solar's 0.5054 - rather than above r32. The
   two LoRA deltas are near-orthogonal (mean cosine 0.003), so they do not
   cancel, but solar's delta still degrades the readout r32 trained.

We publish the combined row because it is a real, reproducible result, but
**the r32+lm_head merge is the better model** and is the one this release
recommends. We are not going to present a merge as an improvement when our
own numbers say it is a regression.
## Benchmark matrix (lm-eval-harness 0.4.13, greedy, same prompts for every row)
| model | GSM8K (8-shot CoT) | IFEval (0-shot, prompt-strict) | MMLU subset (5-shot, 8 subjects) | refusal rate |
|---|---|---|---|---|
| huihui Qwen3.8-27B (uncensored base) | 97.7 | 76.0 | 81.2 | 0.0% (5/5 probes) |
| **Jev Judgment bf16** (solar r16) | 96.0 | 81.5 | 81.2 | 0.0% (5/5 probes) |
| Jev Judgment bf16 (r32+lm_head + solar) | 91.0 | 72.5 | 80.8 | 0.0% (5/5 probes) |
| Jev Judgment bf16 (r32 + lm_head) | 94.0 | 81.0 | 82.5 | 0.0% (5/5 probes) |
| Jev Judgment NVFP4+FP8 | 95.3 | 80.5 | 80.5 | 0.0% (5/5 probes) |
| GGUF Q4_K_M | 95.3 | 79.5 | 82.0 | 0.0% (5/5 probes) |
| GGUF Q8_0 | 95.7 | 81.0 | 81.0 | 0.0% (5/5 probes) |
Questions scored per task:
| task | questions scored per model |
|---|---|
| GSM8K (8-shot CoT) | 300 |
| IFEval (0-shot, prompt-strict) | 200 |
| MMLU subset (5-shot, 8 subjects) | 400 |
Note: GSM8K scored with flexible-extract (the model answers in its own format).
MMLU subset subjects: high_school_mathematics, college_biology, professional_psychology,
jurisprudence, machine_learning, astronomy, global_facts, logical_fallacies (5-shot, _generative variants).
MMLU --limit applies PER SUBJECT, so the subset is 8 x per-subject-limit questions.
Reproducibility: decoding is greedy (do_sample=False, temperature 0, seed 1234), but
the server batches concurrent requests, and batch composition changes floating-point
reduction order. Repeat runs of the same config can therefore differ by a point or two
on the small-n tasks. The error bars in the table are the harness's own stderr over the
scored rows.
## Lineage & credits
This release is a descendant of the earlier `SargeDev/JEV-27B-Uncensored` and
`SargeDev/JEV-27B-VL-Uncensored` repos. The credit chain is unchanged and is
reproduced here in full:
- **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](https://huggingface.co/autotrust/JEV-27B) and
  [autotrust/JEV-27B-VL](https://huggingface.co/autotrust/JEV-27B-VL)
  (Apache-2.0). The `r32` adapter used here is theirs, unmodified: the
  backbone LoRA with the decision head re-expressed as an `lm_head` LoRA, so
  only the 24 verbalizer rows of the output projection change.
- **huihui-ai** — the *abliterated* (uncensored) Qwen3.8-27B backbone used here
  ([Huihui-Qwen3.8-27B-abliterated](https://huggingface.co/huihui-ai/Huihui-Qwen3.8-27B-abliterated),
  Apache-2.0; layers 18-51 ablated, vision tower untouched).
- **SargeDev** — [jev-distill-corpus-v3](https://huggingface.co/datasets/SargeDev/jev-distill-corpus-v3),
  the 740,957-row calibrated typed-decision corpus AutoTrust's JEV models were
  trained on (Apache-2.0), and the **solar r16** adapter trained here on
  [jev-distill-corpus-v4](https://huggingface.co/datasets/SargeDev/jev-distill-corpus-v4)
  (the 1,219-step JEV System-1 run, 17-domain tokenized mix).
- **Qwen** — [Qwen3.8-27B](https://huggingface.co/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).
**Base**: huihui-ai/Qwen3.8-27B-abliterated (bf16; the text-only builds strip
the vision tower and serve as a plain `qwen3_5_text` causal LM, the VL build
keeps the tower and serves as `qwen3_5` / `Qwen3_5ForConditionalGeneration`).
- **Adapters** — this release ships two, and they are NOT interchangeable:
  - `solar r16` (alpha 32, 400 modules, **no `lm_head`**): trained 1,219 steps
    on the JEV System-1 corpus (jev-distill-corpus-v4 tokenized mix: T1 gold
    domains + memory_relevance + 17-domain fill), KL+RPS decision-head
    distillation objective (AutoTrust-style), cosine LR 2e-4, KL_W 1.0,
    RPS_W 0.3. Merged with peft `merge_and_unload`; merge fidelity verified by
    re-gating the bare merged model (it reproduced the adapter-mounted
    numbers).
  - `r32` (alpha 64, 401 modules, **includes `lm_head`**): the backbone LoRA
    plus the 24-slot decision head re-expressed as an `lm_head` LoRA
    (only the 24 verbalizer rows change), so a typed decision is a single
    prefill step with `max_tokens=1` constrained to the option tokens.
- **Decision head + calibration.json** (shipped in this repo root): the
  instrument-panel piece agents call for numeric confidence — the 24-slot
  verbalizer id list, the per-slot bias, and the per-kind temperature. This
  is the SAME head and the SAME calibration file (verified byte-identical,
  md5 `11359173…` and `7f0f3914…`) that produced every gate number in this
  card, so a consumer reproducing our numbers reproduces them exactly.
  For everyday natural-language judgment, chat with the model directly and
  set `enable_thinking:false` in `chat_template_kwargs`.
- **Everything measured on one NVIDIA GB10 (DGX Spark-class)**; quantized with
  llmcompressor 0.14.0 (NVFP4) and llama.cpp (GGUF ladder).

	---
	base_model: huihui-ai/Huihui-Qwen3.8-27B-abliterated
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- decision-model
	- system-one
	- typed-decisions
	- uncensored
	- abliterated
	- calibrated-probabilities
	- noul
	- choice
	- score
	- qwen3.8
	- jev
	- solar-jev
	datasets:
	- SargeDev/jev-distill-corpus-v3
	license: apache-2.0
	---

	# Solar-JEV-27B-R32

	The best-measured build of this release. Uncensored 27B tuned for everyday
	decision-making — crisp calls with honestly-calibrated confidence instead of
	hedge-fests — with the decision readout trained INTO the model.

	> What this repo is. huihui Qwen3.8-27B-abliterated + the AutoTrust `r32`
	> `lm_head` LoRA, merged. No solar delta. These are the same weights as
	> the already-public `SargeDev/JEV-27B-Uncensored` (verified: bit-identical
	> `lm_head`, md5 `bb95f4fd…`, and the backbone differs from the
	> r32+solar merge only on the 400 solar-targeted tensors). This repo exists so
	> the family has one consistent naming scheme and so the **GGUF ladder for the
	> best model** is published — the r32+solar merge in
	> `SargeDev/Solar-Decision-JEV-27B` is a different, weaker model.

	- Full bf16 weights (51 GB), serves directly in vLLM / transformers
	(`Qwen3_5ForCausalLM`).
	- Refusal rate and calibration measured below — the uncensoring claim is
	probed, not assumed.

	## What makes this one different

	The decision readout is an `lm_head` LoRA: the 24-slot decision head is
	re-expressed as a LoRA on the output projection (only the 24 verbalizer rows
	change), so a typed decision is a single prefill with `max_tokens=1`,
	constrained to the option tokens, read back as log-probabilities. No separate
	head to run, no adapter to swap.

	That difference is worth a lot. On the same 27,695 rows with the same harness
	and the same decision head + calibration:

	\| model \| decision readout \| choice top-1 \| score top-1 \| score KL \| noul AUROC \|
	\|---\|---\|---\|---\|---\|---\|
	\| AutoTrust (published reference) \| trained into the model \| 0.9041 \| 0.8910 \| 0.0210 \| 0.9961 \|
	\| this model (r32 + lm_head) \| trained into the model \| 0.8693 \| 0.8135 \| 0.0619 \| 0.9919 \|
	\| solar r16 (no lm_head) \| separate projection at read time \| 0.5054 \| 0.5369 \| 0.4699 \| 0.9449 \|

	The `solar r16` merge reads a decision through a separate projection over
	the final hidden state, and lands at 0.5054 choice top-1. The same backbone
	with the readout in the training loop lands at 0.8693. That is the main
	finding of this release: the readout has to be trained into the model.

	Full ablation, including the r32+solar combined merge (which measured worse
	than r32 alone — 0.8162), is in the `SargeDev/Solar-Decision-JEV-27B` card.

	## Quick start (vLLM, one GB10/Spark-class box)
	```bash
	vllm serve SargeDev/Solar-Decision-JEV-27B \
	--served-model-name jev-judgment-r32 --trust-remote-code \
	--max-model-len 8192 --gpu-memory-utilization 0.85 \
	--default-chat-template-kwargs '{"enable_thinking": false}'
	```


	## Decision gate (AutoTrust-style, test_set_30k with yuri_v1 D1 placeholders excluded)

	Read this table carefully, because it contains two very different comparisons.

	- The sibling merge is our other build, scored on the same 27,695 rows
	with the same harness and the same head + calibration. It is a
	sibling model, not a quality bar — but it is the honest apples-to-apples
	reference for what the `lm_head` LoRA is worth.
	- AutoTrust reference is the number published by the authors of the original
	AutoTrust work for their model on this task set. This is the bar.

	On the typed-decision kinds (multiple-choice `choice`, ordinal `score`) this
	model is still below the published AutoTrust reference, and the gap is
	large - not a rounding difference. On the binary `noul` kind the gap is
	narrower. Cells marked "(below bar)" differ from the published reference by
	more than 5 points.

	What this means: the fine-tune produces a usable decision instrument and a
	clearly better everyday-chat model than its base (see the benchmark matrix
	below), but it is not a reproduction of AutoTrust-level typed-decision
	accuracy. Treat the `choice` and `score` columns as an open result, not a
	solved one. We are publishing the numbers as measured, including the gap.

	Harness validation. These numbers were produced by our own HTTP gate
	client, so we checked it against a known path: re-gating the r32 merge
	reproduced the reference's own numbers to within 0.001 on every metric
	(choice top-1 0.8704 -> 0.8693, score KL 0.0623 -> 0.0619, noul AUROC
	0.9920 -> 0.9919). The harness is sound, so the gaps above are real.

	\| metric \| n \| Jev Judgment r32+lm_head (ours) \| Jev Judgment solar r16 \| AutoTrust reference (published) \|
	\|---\|---\|---\|---\|---\|
	\| noul.AUROC \| 9969 \| 0.9919 (below bar) \| 0.9449 \| 0.9961 \|
	\| noul.top-1 \| 9969 \| 0.9486 \| 0.8657 \| n/a \|
	\| noul.ECE \| 9969 \| 0.0122 \| 0.0143 \| n/a \|
	\| noul.KL \| 9969 \| 0.0081 \| 0.0448 \| n/a \|
	\| choice.top-1 \| 9199 \| 0.8693 (below bar) \| 0.5054 \| 0.9041 \|
	\| choice.ECE \| 9199 \| 0.0051 \| 0.0338 \| n/a \|
	\| choice.KL \| 9199 \| 0.0530 \| 0.4994 \| n/a \|
	\| score.top-1 \| 8527 \| 0.8135 (below bar) \| 0.5369 \| 0.8910 \|
	\| score.ECE \| 8527 \| 0.0367 \| 0.0368 \| n/a \|
	\| score.KL \| 8527 \| 0.0619 (below bar) \| 0.4699 \| 0.0210 \|
	\| overall.top-1 \| 27695 \| 0.8807 \| 0.6448 \| n/a \|
	\| overall.ECE \| 27695 \| 0.0090 (below bar) \| 0.0211 \| 0.0011 \|
	\| overall.KL \| 27695 \| 0.0396 (below bar) \| 0.3267 \| 0.0185 \|


	### Ablation: where the decision readout has to live

	All four rows below are scored on the SAME 27,695 rows, with the SAME
	harness and the SAME decision head + calibration. The only difference is
	where the decision distribution is produced.

	\| model \| decision readout \| choice top-1 \| score top-1 \| score KL \| noul AUROC \| overall ECE \|
	\|---\|---\|---\|---\|---\|---\|---\|
	\| AutoTrust (published reference) \| trained into the model (lm_head LoRA) \| 0.9041 \| 0.8910 \| 0.0210 \| 0.9961 \| 0.0011 \|
	\| r32 + lm_head LoRA (r=32, alpha=64) \| trained into the model (lm_head LoRA) \| 0.8693 \| 0.8135 \| 0.0619 \| 0.9919 \| 0.0090 \|
	\| solar r16 (no lm_head LoRA) \| separate projection at read time \| 0.5054 \| 0.5369 \| 0.4699 \| 0.9449 \| 0.0211 \|
	\| r32 + lm_head LoRA + solar (combined) \| trained into the model (lm_head LoRA) \| 0.8162 \| 0.7192 \| 0.1332 \| 0.9810 \| 0.0625 \|

	Two findings, in order of how much they matter:

	1. The readout has to be trained INTO the model. A separate projection
	over the final hidden state (the `solar r16` row) reaches only 0.5054
	choice top-1; the same backbone with an `lm_head` LoRA in the training
	loop reaches 0.8693 on the identical rows. A separate projection can
	work, but it needs the backbone to cooperate, and a backbone trained
	without the readout in the loop does not.

	2. **Adding a second, readout-free adapter on top makes the typed-decision
	accuracy WORSE, not better.** The `combined` row is the r32 merge plus
	the solar r16 delta applied on top (bit-exact arithmetic, verified).
	It lands between the two parents on every metric - 0.8162 choice top-1,
	between r32's 0.8693 and solar's 0.5054 - rather than above r32. The
	two LoRA deltas are near-orthogonal (mean cosine 0.003), so they do not
	cancel, but solar's delta still degrades the readout r32 trained.

	We publish the combined row because it is a real, reproducible result, but
	the r32+lm_head merge is the better model and is the one this release
	recommends. We are not going to present a merge as an improvement when our
	own numbers say it is a regression.

	## Benchmark matrix (lm-eval-harness 0.4.13, greedy, same prompts for every row)
	\| model \| GSM8K (8-shot CoT) \| IFEval (0-shot, prompt-strict) \| MMLU subset (5-shot, 8 subjects) \| refusal rate \|
	\|---\|---\|---\|---\|---\|
	\| huihui Qwen3.8-27B (uncensored base) \| 97.7 \| 76.0 \| 81.2 \| 0.0% (5/5 probes) \|
	\| Jev Judgment bf16 (solar r16) \| 96.0 \| 81.5 \| 81.2 \| 0.0% (5/5 probes) \|
	\| Jev Judgment bf16 (r32+lm_head + solar) \| 91.0 \| 72.5 \| 80.8 \| 0.0% (5/5 probes) \|
	\| Jev Judgment bf16 (r32 + lm_head) \| 94.0 \| 81.0 \| 82.5 \| 0.0% (5/5 probes) \|
	\| Jev Judgment NVFP4+FP8 \| 95.3 \| 80.5 \| 80.5 \| 0.0% (5/5 probes) \|
	\| GGUF Q4_K_M \| 95.3 \| 79.5 \| 82.0 \| 0.0% (5/5 probes) \|
	\| GGUF Q8_0 \| 95.7 \| 81.0 \| 81.0 \| 0.0% (5/5 probes) \|

	Questions scored per task:
	\| task \| questions scored per model \|
	\|---\|---\|
	\| GSM8K (8-shot CoT) \| 300 \|
	\| IFEval (0-shot, prompt-strict) \| 200 \|
	\| MMLU subset (5-shot, 8 subjects) \| 400 \|

	Note: GSM8K scored with flexible-extract (the model answers in its own format).
	MMLU subset subjects: high_school_mathematics, college_biology, professional_psychology,
	jurisprudence, machine_learning, astronomy, global_facts, logical_fallacies (5-shot, _generative variants).
	MMLU --limit applies PER SUBJECT, so the subset is 8 x per-subject-limit questions.

	Reproducibility: decoding is greedy (do_sample=False, temperature 0, seed 1234), but
	the server batches concurrent requests, and batch composition changes floating-point
	reduction order. Repeat runs of the same config can therefore differ by a point or two
	on the small-n tasks. The error bars in the table are the harness's own stderr over the
	scored rows.

	## Lineage & credits

	This release is a descendant of the earlier `SargeDev/JEV-27B-Uncensored` and
	`SargeDev/JEV-27B-VL-Uncensored` repos. The credit chain is unchanged and is
	reproduced here in full:

	- 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](https://huggingface.co/autotrust/JEV-27B) and
	[autotrust/JEV-27B-VL](https://huggingface.co/autotrust/JEV-27B-VL)
	(Apache-2.0). The `r32` adapter used here is theirs, unmodified: the
	backbone LoRA with the decision head re-expressed as an `lm_head` LoRA, so
	only the 24 verbalizer rows of the output projection change.
	- huihui-ai — the abliterated (uncensored) Qwen3.8-27B backbone used here
	([Huihui-Qwen3.8-27B-abliterated](https://huggingface.co/huihui-ai/Huihui-Qwen3.8-27B-abliterated),
	Apache-2.0; layers 18-51 ablated, vision tower untouched).
	- SargeDev — [jev-distill-corpus-v3](https://huggingface.co/datasets/SargeDev/jev-distill-corpus-v3),
	the 740,957-row calibrated typed-decision corpus AutoTrust's JEV models were
	trained on (Apache-2.0), and the solar r16 adapter trained here on
	[jev-distill-corpus-v4](https://huggingface.co/datasets/SargeDev/jev-distill-corpus-v4)
	(the 1,219-step JEV System-1 run, 17-domain tokenized mix).
	- Qwen — [Qwen3.8-27B](https://huggingface.co/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).

	Base: huihui-ai/Qwen3.8-27B-abliterated (bf16; the text-only builds strip
	the vision tower and serve as a plain `qwen3_5_text` causal LM, the VL build
	keeps the tower and serves as `qwen3_5` / `Qwen3_5ForConditionalGeneration`).
	- Adapters — this release ships two, and they are NOT interchangeable:
	- `solar r16` (alpha 32, 400 modules, no `lm_head`): trained 1,219 steps
	on the JEV System-1 corpus (jev-distill-corpus-v4 tokenized mix: T1 gold
	domains + memory_relevance + 17-domain fill), KL+RPS decision-head
	distillation objective (AutoTrust-style), cosine LR 2e-4, KL_W 1.0,
	RPS_W 0.3. Merged with peft `merge_and_unload`; merge fidelity verified by
	re-gating the bare merged model (it reproduced the adapter-mounted
	numbers).
	- `r32` (alpha 64, 401 modules, includes `lm_head`): the backbone LoRA
	plus the 24-slot decision head re-expressed as an `lm_head` LoRA
	(only the 24 verbalizer rows change), so a typed decision is a single
	prefill step with `max_tokens=1` constrained to the option tokens.
	- Decision head + calibration.json (shipped in this repo root): the
	instrument-panel piece agents call for numeric confidence — the 24-slot
	verbalizer id list, the per-slot bias, and the per-kind temperature. This
	is the SAME head and the SAME calibration file (verified byte-identical,
	md5 `11359173…` and `7f0f3914…`) that produced every gate number in this
	card, so a consumer reproducing our numbers reproduces them exactly.
	For everyday natural-language judgment, chat with the model directly and
	set `enable_thinking:false` in `chat_template_kwargs`.
	- Everything measured on one NVIDIA GB10 (DGX Spark-class); quantized with
	llmcompressor 0.14.0 (NVFP4) and llama.cpp (GGUF ladder).