--- title: Solar-JEV-27B canonical_url: "https://www.modelscope.cn/models/SargeDev/Solar-JEV-27B" md_url: "https://www.modelscope.cn/models/SargeDev/Solar-JEV-27B.md" repository: SargeDev/Solar-JEV-27B last_updated: 2026-10-27 license: apache-2.0 pipeline_tag: text-generation tasks: - text-generation model_type: - qwen3_5_text architectures: - Qwen3_5ForCausalLM base_model: - huihui-ai/Huihui-Qwen3.8-27B-abliterated base_model_relation: finetune parameters: 27.3B tensor_type: - BF16 library_name: - safetensors - pytorch frameworks: - pytorch downloads: 25 stars: 0 tags: - decision-model - system-one - typed-decisions - uncensored - abliterated - calibrated-probabilities - noul - choice - score - qwen3.8 - jev - solar-jev --- # Solar-JEV-27B > Solar-JEV-27B - SargeDev 在 ModelScope 开源的模型。Uncensored 27B tuned for everyday decision-making: crisp calls with honestly-calibrated confidence instead of hedge-fests. Full bf16 weights (51 GB). SargeDev/Solar-JEV-27B 是 ModelScope 魔搭社区上的 27.3B 参数text-generation模型，采用 apache-2.0 许可，基于 huihui-ai/Huihui-Qwen3.8-27B-abliterated 构建。 - **Repository**: SargeDev/Solar-JEV-27B - **License**: apache-2.0 - **Tasks**: text-generation - **Parameters**: 27.3B - **Base model**: huihui-ai/Huihui-Qwen3.8-27B-abliterated - **Tags**: decision-model, system-one, typed-decisions, uncensored, abliterated, calibrated-probabilities, noul, choice, score, qwen3.8, jev, solar-jev - **Downloads**: 25 - **Stars**: 0 - **Last updated**: 2026-10-27 Source: https://www.modelscope.cn/models/SargeDev/Solar-JEV-27B --- # Solar-JEV-27B Uncensored 27B tuned for everyday decision-making: crisp calls with honestly-calibrated confidence instead of hedge-fests. 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. - The decision readout is trained INTO the model (`lm_head` LoRA), so a typed decision is one forward pass with `max_tokens=1` — no extra head to run. **Which model is this?** The `solar r16` merge: huihui Qwen3.8-27B-abliterated + a 16-rank LoRA trained 1,219 steps on the JEV System-1 corpus. The `SargeDev/Solar-JEV-27B-R32` repo carries the `r32` merge and **scores much better on the decision gate** (choice top-1 0.8693 vs 0.5054). The ablation table below shows both and the numbers that decided it. ## Quick start (vLLM, one GB10/Spark-class box) ```bash vllm serve SargeDev/Solar-JEV-27B \ --served-model-name jev-judgment --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 solar r16 (ours)** | Jev Judgment r32+lm_head | AutoTrust reference (published) | |---|---|---|---|---| | noul.AUROC | 9969 | 0.9449 (below bar) | 0.9919 | 0.9961 | | noul.top-1 | 9969 | 0.8657 | 0.9486 | n/a | | noul.ECE | 9969 | 0.0143 | 0.0122 | n/a | | noul.KL | 9969 | 0.0448 | 0.0081 | n/a | | choice.top-1 | 9199 | 0.5054 (below bar) | 0.8693 | 0.9041 | | choice.ECE | 9199 | 0.0338 | 0.0051 | n/a | | choice.KL | 9199 | 0.4994 | 0.0530 | n/a | | score.top-1 | 8527 | 0.5369 (below bar) | 0.8135 | 0.8910 | | score.ECE | 8527 | 0.0368 | 0.0367 | n/a | | score.KL | 8527 | 0.4699 (below bar) | 0.0619 | 0.0210 | | overall.top-1 | 27695 | 0.6448 | 0.8807 | n/a | | overall.ECE | 27695 | 0.0211 (below bar) | 0.0090 | 0.0011 | | overall.KL | 27695 | 0.3267 (below bar) | 0.0396 | 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 [solar-decisions-corpus-v4](https://huggingface.co/datasets/SargeDev/solar-decisions-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 (solar-decisions-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).