Release Qev-4B v0.1.0: direct base initialization, MMLU-Pro 50.30%, JevBench 190/231

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,203 @@
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+      To apply the Apache License to your work, attach the following
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+   Copyright 2026 Qiqian Fu and Qev contributors
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NOTICE

@@ -0,0 +1,36 @@
+Qev
+Copyright 2026 Qiqian Fu and Qev contributors
+
+Qev code, documentation, original illustrations, examples, generated prediction
+results, and published adaptation weights are licensed under Apache-2.0.
+
+This project adapts conventions and implementation ideas from Kev:
+  https://github.com/jaredpalmer/kev
+  Copyright 2026 Jared Palmer
+  Apache License, Version 2.0
+
+Adaptations include Qwen delimiters, text rendering, LoRA target selection, and
+inference cache forking. Modified files carry Qev adaptation notices. The original
+Kev license and copyright notice are retained in licenses/Kev-Apache-2.0.txt.
+
+Qev uses Qwen3.5 models and tokenizers:
+  https://huggingface.co/Qwen/Qwen3.5-9B-Base
+  https://huggingface.co/Qwen/Qwen3.5-2B-Base
+  Copyright 2026 Alibaba Cloud
+  Apache License, Version 2.0
+
+The public weight package includes the Qwen tokenizer and Qev adaptation weights.
+Qwen base weights are downloaded separately. The original Qwen license is in
+licenses/Qwen3.5-Apache-2.0.txt.
+
+The optional JevBench dataset preparer downloads public evaluation tasks by
+Florian Standhartinger and contributors. It includes their MIT license,
+third-party notices, and per-question provenance with the prepared data.
+
+See THIRD_PARTY_NOTICES.md for licensing scope and external dependencies.
+Third-party datasets and services retain their own terms.
+
+Crafter visuals in the project gameplay recording:
+  https://github.com/danijar/crafter
+  Copyright (c) 2021 Danijar Hafner
+  MIT License; see licenses/Crafter-MIT.txt.

README.md

@@ -0,0 +1,88 @@
+---
+license: apache-2.0
+base_model: Qwen/Qwen3.5-4B-Base
+base_model_relation: adapter
+language:
+  - en
+  - zh
+tags:
+  - qev
+  - lora
+  - decision-model
+  - knowledge-distillation
+---
+
+<p align="center"><img src="assets/banner.svg" alt="Qev — decisions, grounded in Qwen" width="100%"></p>
+
+# Qev-4B
+
+Qev-4B turns context, a question and candidate answers into a decision and a probability for every option. It supports **Choice**, **Noul** (yes/no) and **Score** (ordered ratings) through the same API as Qev-2B and Qev-9B.
+
+[Source and installation](https://github.com/QiqianFu/Qev#installation) · [Training method](https://github.com/QiqianFu/Qev/blob/main/docs/training-4b.md) · [中文训练说明](https://github.com/QiqianFu/Qev/blob/main/docs/training-4b.zh-CN.md)
+
+This release starts **directly from Qwen3.5-4B-Base**, with fresh LoRA and decision-head parameters. A single two-epoch stage learns a 9B teacher's option probabilities. It does not inherit an earlier distilled 4B checkpoint or include representation-response continuation.
+
+| Property | Qev-4B v0.1.0 |
+|---|---|
+| Base | Qwen3.5-4B-Base, revision `710fd005d44d55ee27b7ad5147e318e546efdbfe` |
+| Adaptation | LoRA rank 64, alpha 128 |
+| Decision head | 256 dimensions, four attention heads, two Transformer layers |
+| Candidate interaction | Full sibling interaction in the final full-attention layer |
+| Training | Teacher-probability cross entropy; 44,576 single-question inputs |
+| Schedule | Two epochs, 2,786 steps, four GPUs, global batch 32, seed 17 |
+| Temperatures | Teacher 1.563437713227029; student 1 |
+| Input limits | 4,096-token state and complete path; question 512, candidate 256 |
+| Computation | BF16 backbone, FP32 decision head |
+| Download | Approximately 524 MiB; the Qwen base downloads separately |
+
+## Use the model
+
+Install the [Qev source package](https://github.com/QiqianFu/Qev#installation) with Python 3.12 and a hardware-compatible PyTorch 2.8.0 build:
+
+```python
+from qev import Qev
+
+model = Qev.from_pretrained("AustinFu/Qev-4B", revision="v0.1.0", device="cuda")
+answers = model.predict({
+    "state": "I was charged twice for the same order.",
+    "questions": {
+        "department": {
+            "type": "choice",
+            "instructions": "Which team should handle this?",
+            "criteria": {"billing": "Charges and refunds", "shipping": "Delivery problems"},
+        }
+    },
+})
+print(answers["department"]["choice"])
+print(answers["department"]["probabilities"])
+```
+
+The loader restores the LoRA adapter, decision head, interaction gate and tokenizer, and downloads the pinned Qwen base. No teacher is needed at inference time. The package contains all trained adaptation tensors, training and fine-tuning configurations, evaluation results and license files; optimizer state and base weights are excluded.
+
+## Recorded evaluation
+
+| Benchmark | Correct / total | Accuracy (%) |
+|---|---:|---:|
+| Decision development · clean | 1099 / 1264 | 86.95 |
+| Transfer development · clean | 538 / 656 | 82.01 |
+| MMLU-Pro | 503 / 1000 | 50.30 |
+| SemIf · handwritten | 130 / 144 | 90.28 |
+| scienthoon | 670 / 873 | 76.75 |
+| WANLI | 188 / 256 | 73.44 |
+| JevBench public | 190 / 231 | 82.25 |
+
+Results use BF16 backbone computation, an FP32 head, temperature 1 and full causal reference execution. There were zero rejected questions in all seven original evaluations. The development and SemIf rows use the same subsets as the [Qev leaderboard](https://github.com/QiqianFu/Qev#evaluation); full-suite counts are included in `evaluation.json`.
+
+![Qev benchmark matrix](assets/evaluation-matrix.svg)
+
+These are measurements from one checkpoint and seed. Training data and objectives differ across the 2B, 4B and 9B releases; their scores do not isolate model-size effects. A native 4B language-model baseline was not run. The gameplay recordings in the source repository use 9B models.
+
+## Training and availability
+
+The input pool combines general decisions, science and reasoning, Principle judgments, controlled boundaries, web actions and additional rule/reasoning tasks. Each record contains one question with its hard label removed. Training uses a single pool without late-stage repetition or online option changes.
+
+The recorded teacher is a separate 9B research checkpoint trained with additional web and rule tasks, at step 2,658; it is different from the public Qev-9B v0.2.0. The teacher, full 44,576-input pool and cached teacher outputs are not distributed. [Qev-train](https://huggingface.co/datasets/AustinFu/Qev-train) remains a separate release of 2,442 synthetic examples with hard labels and synthesis documentation. The source repository provides the 4B method and a runnable example using a public teacher and user data.
+
+Use `configs/qev-4b-finetune.json` with `python -m qev.train --init-checkpoint AustinFu/Qev-4B@v0.1.0` for supervised adaptation. Validate performance and probabilities on your own task; calibration and production reliability have not been established.
+
+The Qev adaptation weights use Apache-2.0. The Qwen base, tokenizer and source datasets retain their respective terms. [License and attribution](https://github.com/QiqianFu/Qev/blob/main/THIRD_PARTY_NOTICES.md).

THIRD_PARTY_NOTICES.md

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+# Licensing and attribution
+
+## Qev materials
+
+Copyright 2026 Qiqian Fu and Qev contributors.
+
+Qev's code, documentation, original SVG illustrations, example requests, and generated prediction results are provided under the [Apache License 2.0](LICENSE). The Qev adaptation weights published at [AustinFu/Qev-2B](https://huggingface.co/AustinFu/Qev-2B), [AustinFu/Qev-4B](https://huggingface.co/AustinFu/Qev-4B) and [AustinFu/Qev-9B](https://huggingface.co/AustinFu/Qev-9B), including their LoRA adapters, decision heads and interaction gates, use the same license. Contributions are accepted under these terms unless explicitly stated otherwise.
+
+Third-party materials retain the terms listed below. The Qev license does not relicense training datasets, external services, or third-party recordings.
+
+## Included and adapted materials
+
+| Material | Source and copyright | License and scope |
+|---|---|---|
+| Kev conventions and adapted implementation | [Jared Palmer's Kev](https://github.com/jaredpalmer/kev), Copyright 2026 Jared Palmer | [Apache-2.0, original notice retained](licenses/Kev-Apache-2.0.txt). Qev adapts delimiter and rendering conventions, LoRA targets, and cache forking. Modified source files identify the Qev adaptation. |
+| Qwen models and tokenizers | [Qwen3.5-9B-Base](https://huggingface.co/Qwen/Qwen3.5-9B-Base), [Qwen3.5-4B-Base](https://huggingface.co/Qwen/Qwen3.5-4B-Base) and [Qwen3.5-2B-Base](https://huggingface.co/Qwen/Qwen3.5-2B-Base), Copyright 2026 Alibaba Cloud | [Apache-2.0](licenses/Qwen3.5-Apache-2.0.txt). Tokenizers accompany the corresponding Qev checkpoints; base weights are downloaded separately. |
+| JevBench public evaluation tasks | [JevBench](https://github.com/fstandhartinger/jevbench), Copyright 2026 Florian Standhartinger and contributors | [MIT](licenses/JevBench-MIT.txt), with [upstream third-party notices](licenses/JevBench-THIRD-PARTY.md). Tasks are downloaded by the optional preparation script. The original license, third-party notice, and per-question provenance accompany the prepared dataset. |
+| Crafter visuals in the gameplay recording | [Crafter](https://github.com/danijar/crafter), Copyright 2021 Danijar Hafner | [MIT](licenses/Crafter-MIT.txt). The gameplay recording contains the environment's visual assets; the recording and Qev decision overlay were produced for this project. |
+
+The reviewed Kev version is [557598f](https://github.com/jaredpalmer/kev/tree/557598fced1dada75dfbf36ed144dce309ac6ceb); the evaluation data use [JevBench v1.4.2](https://github.com/fstandhartinger/jevbench/tree/1df665e3956d7aab7fa0208ff6c4f2d8557f9f90). The 231 downloaded public tasks carry MIT in their individual provenance records; upstream notices describe the scope of other JevBench materials and evaluated services.
+
+The README layout takes inspiration from [JevAny](https://github.com/weitianxin/JevAny). Qev's SVG illustrations and chart code are original. The GIF and MP4 demos are this project's recordings of 9B research models. The Crafter research integration used JevAny's environment adapters; this source package does not vendor those adapters or JevAny's bundled recordings.
+
+## Dependencies installed separately
+
+Dependencies are installed from their own distributions and retain their licenses:
+
+- [PyTorch](https://github.com/pytorch/pytorch/blob/main/LICENSE): BSD-3-Clause, plus its bundled third-party notices.
+- [Transformers](https://github.com/huggingface/transformers/blob/main/LICENSE), [PEFT](https://github.com/huggingface/peft/blob/main/LICENSE), [Accelerate](https://github.com/huggingface/accelerate/blob/main/LICENSE), [Safetensors](https://github.com/huggingface/safetensors/blob/main/LICENSE), [Hugging Face Hub](https://github.com/huggingface/huggingface_hub/blob/main/LICENSE), and optional [Apache Arrow](https://github.com/apache/arrow/blob/main/LICENSE.txt): Apache-2.0.
+- Development tools [pytest](https://github.com/pytest-dev/pytest/blob/main/LICENSE) and [Matplotlib](https://matplotlib.org/stable/project/license.html) retain their MIT and Matplotlib/PSF-based license terms, respectively.
+
+Their source code and dependency distributions are not vendored in Qev's Python package. This list identifies direct dependencies; their own packages include notices for transitive components.
+
+## Training data
+
+The [Qev-train dataset](https://huggingface.co/datasets/AustinFu/Qev-train) publishes 2,442 synthetic training examples separately from this code repository. Its 1,534 original alignment and rule-compliance examples use Apache-2.0 where copyright applies. Its 308 Wikipedia-grounded knowledge examples retain CC BY-SA 4.0, with article versions, contributor links and changes recorded in `ATTRIBUTION.jsonl`. Its 600 boundary examples adapt NVIDIA HelpSteer3 Preference contexts and retain [CC BY 4.0](licenses/HelpSteer3-CC-BY-4.0.txt), with source lineage and changes in `HELPSTEER3_ATTRIBUTION.jsonl`. The [dataset license](https://huggingface.co/datasets/AustinFu/Qev-train/blob/main/LICENSE.md) identifies the scope of each component; combining them does not relicense the derived components under Apache-2.0.
+
+Qev-9B v0.2.0 also uses 4,459 Principle judgments from [NVIDIA HelpSteer3](https://huggingface.co/datasets/nvidia/HelpSteer3), by Zhilin Wang and collaborators, under CC BY 4.0. The source revision is `f6d145777bcbde96137596340fab89793acd1031`; those original judgments are not part of the synthetic Qev-train release. No endorsement by NVIDIA or the source authors is implied.
+
+The complete research training mixture and distillation data are not distributed. Other source datasets retain their own terms.
+
+The small JSONL requests in `examples/` were written for Qev and are covered by its Apache-2.0 license. The benchmark result tables and prediction files contain Qev's recorded outputs, not the original benchmark question text.

adapter/adapter_config.json

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+  "alpha_pattern": {},
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+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen3.5-4B-Base",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "kasa_config": null,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
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+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
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+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
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+  "peft_type": "LORA",
+  "peft_version": "0.21.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": "710fd005d44d55ee27b7ad5147e318e546efdbfe",
+  "target_modules": [
+    "up_proj",
+    "in_proj_qkv",
+    "q_proj",
+    "out_proj",
+    "gate_proj",
+    "o_proj",
+    "in_proj_z",
+    "k_proj",
+    "in_proj_a",
+    "in_proj_b",
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+  ],
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+  "task_type": "FEATURE_EXTRACTION",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
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benchmarks.json

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+{
+  "date": "2026-10-02",
+  "metric": "argmax_accuracy",
+  "models": {
+    "jev": {
+      "description": "Hosted service; JevBench tested locally at Jev 1.13.0, other suites from Kev authors"
+    },
+    "kev_9b": {
+      "revision": "2629c06a",
+      "code_revision": "557598fced1dada75dfbf36ed144dce309ac6ceb",
+      "dtype": "fp32",
+      "temperature": 1.0,
+      "source": "JevBench locally rerun; other suites from author reports"
+    },
+    "qev_9b": {
+      "repo_id": "AustinFu/Qev-9B",
+      "revision": "v0.2.0",
+      "base": "Qwen/Qwen3.5-9B-Base",
+      "base_revision": "68c46c4b3498877f3ef123c856ecfde50c39f404",
+      "step": 2643,
+      "dtype": "bf16",
+      "execution": "reference",
+      "seed": 17,
+      "temperature": 1.0
+    },
+    "qwen35_9b_base": {
+      "description": "Frozen native LM head; zero-shot candidate code probabilities",
+      "dtype": "bf16"
+    },
+    "qev_2b": {
+      "base": "Qwen/Qwen3.5-2B-Base",
+      "base_revision": "b1485b2fa6dfa1287294f269f5fb618e03d52d7c",
+      "dtype": "bf16",
+      "description": "Probability and representation-response distillation from Qev-9B v0.1.0",
+      "seed": 17,
+      "continuation_steps": 800,
+      "teacher_revision": "v0.1.0"
+    },
+    "qwen35_2b_base": {
+      "description": "Frozen native LM head; zero-shot candidate code probabilities",
+      "dtype": "bf16",
+      "base": "Qwen/Qwen3.5-2B-Base",
+      "base_revision": "b1485b2fa6dfa1287294f269f5fb618e03d52d7c"
+    },
+    "qev_4b": {
+      "repo_id": "AustinFu/Qev-4B",
+      "revision": "v0.1.0",
+      "base": "Qwen/Qwen3.5-4B-Base",
+      "base_revision": "710fd005d44d55ee27b7ad5147e318e546efdbfe",
+      "step": 2786,
+      "dtype": "bf16",
+      "execution": "reference",
+      "seed": 17,
+      "temperature": 1.0,
+      "description": "Initialized directly from the Qwen base; teacher-probability cross entropy"
+    }
+  },
+  "results": {
+    "decision_dev_all": {
+      "total": 1468,
+      "models": {
+        "jev": {
+          "correct": 1221,
+          "accuracy": 0.8317438692098093
+        },
+        "kev_9b": {
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+          "accuracy": 0.8780653950953679
+        },
+        "qev_9b": {
+          "correct": 1295,
+          "accuracy": 0.8821525885558583
+        },
+        "qwen35_9b_base": {
+          "correct": 1112,
+          "accuracy": 0.7574931880108992
+        },
+        "qev_2b": {
+          "correct": 1266,
+          "accuracy": 0.8623978201634878
+        },
+        "qwen35_2b_base": {
+          "correct": 927,
+          "accuracy": 0.6314713896457765
+        },
+        "qev_4b": {
+          "correct": 1286,
+          "accuracy": 0.8760217983651226
+        }
+      }
+    },
+    "decision_dev_clean": {
+      "total": 1264,
+      "models": {
+        "jev": {
+          "correct": 1068,
+          "accuracy": 0.8449367088607594
+        },
+        "kev_9b": {
+          "correct": 1102,
+          "accuracy": 0.8718354430379747
+        },
+        "qev_9b": {
+          "correct": 1105,
+          "accuracy": 0.8742088607594937
+        },
+        "qwen35_9b_base": {
+          "correct": 982,
+          "accuracy": 0.7768987341772152
+        },
+        "qev_2b": {
+          "correct": 1079,
+          "accuracy": 0.8536392405063291
+        },
+        "qwen35_2b_base": {
+          "correct": 827,
+          "accuracy": 0.6542721518987342
+        },
+        "qev_4b": {
+          "correct": 1099,
+          "accuracy": 0.8694620253164557
+        }
+      }
+    },
+    "transfer_dev_all": {
+      "total": 764,
+      "models": {
+        "jev": {
+          "correct": 647,
+          "accuracy": 0.8468586387434555
+        },
+        "kev_9b": {
+          "correct": 620,
+          "accuracy": 0.8115183246073299
+        },
+        "qev_9b": {
+          "correct": 630,
+          "accuracy": 0.824607329842932
+        },
+        "qwen35_9b_base": {
+          "correct": 561,
+          "accuracy": 0.7342931937172775
+        },
+        "qev_2b": {
+          "correct": 584,
+          "accuracy": 0.7643979057591623
+        },
+        "qwen35_2b_base": {
+          "correct": 493,
+          "accuracy": 0.6452879581151832
+        },
+        "qev_4b": {
+          "correct": 618,
+          "accuracy": 0.8089005235602095
+        }
+      }
+    },
+    "transfer_dev_clean": {
+      "total": 656,
+      "models": {
+        "jev": {
+          "correct": 562,
+          "accuracy": 0.8567073170731707
+        },
+        "kev_9b": {
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+          "accuracy": 0.8216463414634146
+        },
+        "qev_9b": {
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+          "accuracy": 0.8399390243902439
+        },
+        "qwen35_9b_base": {
+          "correct": 488,
+          "accuracy": 0.7439024390243902
+        },
+        "qev_2b": {
+          "correct": 507,
+          "accuracy": 0.7728658536585366
+        },
+        "qwen35_2b_base": {
+          "correct": 427,
+          "accuracy": 0.6509146341463414
+        },
+        "qev_4b": {
+          "correct": 538,
+          "accuracy": 0.8201219512195121
+        }
+      }
+    },
+    "mmlupro": {
+      "total": 1000,
+      "models": {
+        "jev": {
+          "correct": 835,
+          "accuracy": 0.835
+        },
+        "kev_9b": {
+          "correct": 511,
+          "accuracy": 0.511
+        },
+        "qev_9b": {
+          "correct": 574,
+          "accuracy": 0.574
+        },
+        "qwen35_9b_base": {
+          "correct": 504,
+          "accuracy": 0.504
+        },
+        "qev_2b": {
+          "correct": 387,
+          "accuracy": 0.387
+        },
+        "qwen35_2b_base": {
+          "correct": 312,
+          "accuracy": 0.312
+        },
+        "qev_4b": {
+          "correct": 503,
+          "accuracy": 0.503
+        }
+      }
+    },
+    "semif_handwritten": {
+      "total": 144,
+      "models": {
+        "jev": {
+          "correct": 139,
+          "accuracy": 0.9652777777777778
+        },
+        "kev_9b": {
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+          "accuracy": 0.9097222222222222
+        },
+        "qev_9b": {
+          "correct": 134,
+          "accuracy": 0.9305555555555556
+        },
+        "qwen35_9b_base": {
+          "correct": 130,
+          "accuracy": 0.9027777777777778
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+        "qev_2b": {
+          "correct": 119,
+          "accuracy": 0.8263888888888888
+        },
+        "qwen35_2b_base": {
+          "correct": 92,
+          "accuracy": 0.6388888888888888
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+        "qev_4b": {
+          "correct": 130,
+          "accuracy": 0.9027777777777778
+        }
+      }
+    },
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+      "total": 873,
+      "models": {
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+          "correct": 657,
+          "accuracy": 0.7525773195876289
+        },
+        "kev_9b": {
+          "correct": 659,
+          "accuracy": 0.7548682703321878
+        },
+        "qev_9b": {
+          "correct": 620,
+          "accuracy": 0.7101947308132875
+        },
+        "qwen35_9b_base": {
+          "correct": 601,
+          "accuracy": 0.6884306987399771
+        },
+        "qev_2b": {
+          "correct": 628,
+          "accuracy": 0.7193585337915235
+        },
+        "qwen35_2b_base": {
+          "correct": 470,
+          "accuracy": 0.5383734249713631
+        },
+        "qev_4b": {
+          "correct": 670,
+          "accuracy": 0.7674684994272624
+        }
+      }
+    },
+    "wanli": {
+      "total": 256,
+      "models": {
+        "jev": {
+          "correct": 194,
+          "accuracy": 0.7578125
+        },
+        "kev_9b": {
+          "correct": 180,
+          "accuracy": 0.703125
+        },
+        "qev_9b": {
+          "correct": 182,
+          "accuracy": 0.7109375
+        },
+        "qwen35_9b_base": {
+          "correct": 174,
+          "accuracy": 0.6796875
+        },
+        "qev_2b": {
+          "correct": 173,
+          "accuracy": 0.67578125
+        },
+        "qwen35_2b_base": {
+          "correct": 129,
+          "accuracy": 0.50390625
+        },
+        "qev_4b": {
+          "correct": 188,
+          "accuracy": 0.734375
+        }
+      }
+    },
+    "jevbench_public": {
+      "total": 231,
+      "models": {
+        "jev": {
+          "correct": 198,
+          "accuracy": 0.8571428571428571
+        },
+        "kev_9b": {
+          "correct": 175,
+          "accuracy": 0.7575757575757576
+        },
+        "qev_9b": {
+          "correct": 192,
+          "accuracy": 0.8311688311688312
+        },
+        "qwen35_9b_base": {
+          "correct": 175,
+          "accuracy": 0.7575757575757576
+        },
+        "qev_2b": {
+          "correct": 172,
+          "accuracy": 0.7445887445887446
+        },
+        "qwen35_2b_base": {
+          "correct": 146,
+          "accuracy": 0.6320346320346321
+        },
+        "qev_4b": {
+          "correct": 190,
+          "accuracy": 0.8225108225108225
+        }
+      }
+    }
+  },
+  "jevbench_subsets": {
+    "original": {
+      "total": 72,
+      "correct": {
+        "jev": 71,
+        "kev_9b": 65,
+        "qev_9b": 68,
+        "qwen35_9b_base": 59,
+        "qev_2b": 64,
+        "qwen35_2b_base": 44,
+        "qev_4b": 69
+      }
+    },
+    "easy": {
+      "total": 48,
+      "correct": {
+        "jev": 48,
+        "kev_9b": 48,
+        "qev_9b": 48,
+        "qwen35_9b_base": 48,
+        "qev_2b": 48,
+        "qwen35_2b_base": 48,
+        "qev_4b": 48
+      }
+    },
+    "hard": {
+      "total": 111,
+      "correct": {
+        "jev": 79,
+        "kev_9b": 62,
+        "qev_9b": 76,
+        "qwen35_9b_base": 68,
+        "qev_2b": 60,
+        "qwen35_2b_base": 54,
+        "qev_4b": 73
+      }
+    }
+  },
+  "notes": [
+    "Public JevBench accuracy is not the official composite score.",
+    "MMLU-Pro: Kev answered 992/1000; its 8 unanswered items count as wrong.",
+    "SemIf: the 144 handwritten questions, excluding the 108 perturbations.",
+    "Cross-model precisions and execution implementations differ.",
+    "The Qev release is one seed, and these comparisons do not isolate architecture gains.",
+    "The 2B columns use the same clean development and 144-question SemIf subsets as the 9B comparison."
+  ]
+}

evaluation.json

@@ -0,0 +1,86 @@
+{
+  "model": "Qev-4B",
+  "revision": "v0.1.0",
+  "full_suite": {
+    "decision_dev": {
+      "correct": 1286,
+      "total": 1468,
+      "accuracy": 0.8760217983651226,
+      "rejected": 0
+    },
+    "transfer_dev": {
+      "correct": 618,
+      "total": 764,
+      "accuracy": 0.8089005235602095,
+      "rejected": 0
+    },
+    "mmlupro": {
+      "correct": 503,
+      "total": 1000,
+      "accuracy": 0.503,
+      "rejected": 0
+    },
+    "semif": {
+      "correct": 228,
+      "total": 252,
+      "accuracy": 0.9047619047619048,
+      "rejected": 0
+    },
+    "scienthoon": {
+      "correct": 670,
+      "total": 873,
+      "accuracy": 0.7674684994272624,
+      "rejected": 0
+    },
+    "wanli": {
+      "correct": 188,
+      "total": 256,
+      "accuracy": 0.734375,
+      "rejected": 0
+    },
+    "public": {
+      "correct": 190,
+      "total": 231,
+      "accuracy": 0.8225108225108225,
+      "rejected": 0
+    }
+  },
+  "matched_subsets": {
+    "decision_dev_all": {
+      "correct": 1286,
+      "total": 1468
+    },
+    "decision_dev_clean": {
+      "correct": 1099,
+      "total": 1264
+    },
+    "transfer_dev_all": {
+      "correct": 618,
+      "total": 764
+    },
+    "transfer_dev_clean": {
+      "correct": 538,
+      "total": 656
+    },
+    "mmlupro": {
+      "correct": 503,
+      "total": 1000
+    },
+    "scienthoon": {
+      "correct": 670,
+      "total": 873
+    },
+    "wanli": {
+      "correct": 188,
+      "total": 256
+    },
+    "semif_handwritten": {
+      "correct": 130,
+      "total": 144
+    },
+    "jevbench_public": {
+      "correct": 190,
+      "total": 231
+    }
+  }
+}

finetuning_config.json

@@ -0,0 +1,45 @@
+{
+  "model": {
+    "base": "Qwen/Qwen3.5-4B-Base",
+    "revision": "710fd005d44d55ee27b7ad5147e318e546efdbfe",
+    "head_dim": 256,
+    "head_heads": 4,
+    "head_layers": 2,
+    "lora_rank": 64,
+    "lora_dropout": 0.0,
+    "weights_dtype": "bf16",
+    "attention": "sdpa",
+    "rows_per_forward": 32,
+    "layout": "state-question-candidate.v1",
+    "max_padding_ratio": 1.25,
+    "max_padded_tokens": 8192,
+    "choice_none_policy": "as-provided",
+    "candidate_interaction": "last-full-attention"
+  },
+  "limits": {
+    "max_state": 4096,
+    "max_question": 512,
+    "max_candidate": 256,
+    "max_path": 4096,
+    "max_candidates": 128
+  },
+  "training": {
+    "epochs": 2,
+    "batch_size": 1,
+    "accum": 32,
+    "seed": 17,
+    "lr": 1e-05,
+    "head_lr": 0.0001,
+    "joint_gate_lr": 0.01,
+    "weight_decay": 0.01,
+    "warmup_steps": 20,
+    "head_warmup_steps": 0,
+    "gradient_checkpointing": true,
+    "save_every": 100,
+    "autocast": "bf16",
+    "batch_assignment": "leaf-balanced-v1",
+    "prefix_execution": "tree-batched",
+    "none_insert_prob": 0.0,
+    "none_absent_prob": 0.0
+  }
+}

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joint.safetensors

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licenses/Crafter-MIT.txt

@@ -0,0 +1,17 @@
+Copyright (c) 2021 Danijar Hafner
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

licenses/HelpSteer3-CC-BY-4.0.txt

@@ -0,0 +1,156 @@
+Creative Commons Attribution 4.0 International
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+
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licenses/JevBench-MIT.txt

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Florian Standhartinger and contributors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
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+SOFTWARE.

licenses/JevBench-THIRD-PARTY.md

@@ -0,0 +1,51 @@
+# Third-party projects, data and services
+
+MIT (see `LICENSE`) covers this harness and the 72 original public decisions in
+`datasets/public/original.jsonl`. Nothing else in this list is ours to license.
+
+## Systems evaluated
+
+Each was reached through the interface its author published. We used public
+endpoints as ordinary clients, at one request at a time, and never sent a
+provider's API key to anyone else's endpoint.
+
+| System | Author | Source |
+|---|---|---|
+| Jev 1.13.0 | TypeSafe AI | <https://docs.typesafe.ai> |
+| openjev-sglang | ekzhang | <https://github.com/ekzhang/openjev-sglang> |
+| system-one-open | mithalouni | <https://github.com/mithalouni/system-one-open> |
+| open-alternative-jev | IkerMoel | <https://github.com/ikermoel/open-alternative-jev>, <https://huggingface.co/spaces/IkerMoel/open-alternative-jev> |
+| open-jev-deberta-v3-large | Kotoba Labs | <https://huggingface.co/com-kotobalabs/open-jev-deberta-v3-large>, <https://github.com/kotoba-lang/typed-decisions> |
+| GPT-5.6 Luna | OpenAI | <https://platform.openai.com> |
+| Gemini 3.1 Flash-Lite | Google | <https://ai.google.dev> |
+| DeepSeek V4.1 Flash | DeepSeek | <https://api-docs.deepseek.com> |
+| Qwen3.8 27B | Qwen, served by Chutes | <https://chutes.ai> |
+
+Model weights, base models and each project's own code keep their own licences.
+A permissive licence on a repository is not a licence for the base model it
+fine-tunes, and we do not restate either.
+
+## Wire format
+
+The typed-decision request shape (`state`, `questions`, the `noul` / `choice` /
+`score` primitives) is TypeSafe's public HTTP API, documented at
+<https://docs.typesafe.ai/api>. Several of the open rebuilds implement it
+deliberately, which is why one adapter reaches more than one of them. JevBench is
+not affiliated with or endorsed by TypeSafe AI.
+
+## Imported decisions
+
+146 of the 242 decisions come from our own earlier auto-router experiment
+(<https://github.com/fstandhartinger/auto-model-router>): 78 routing requests and
+68 answer-adequacy judgements whose ground truth is a deterministic grader's
+verdict on a saved answer. The upstream task text is **not** redistributed here,
+because the datasets it was drawn from keep their own terms. `datasets/manifest.json`
+pins the hashes; `scripts/import_router.py` shows exactly what was taken and what
+was excluded.
+
+## Held-out decisions
+
+24 scenarios are written by us and deliberately unpublished, so the suite cannot
+be trained on in full. Only their whole-split hash and aggregate results appear
+here. They are sent to the services being evaluated, which is not the same thing
+as being public - see the limits section of the README.

licenses/Kev-Apache-2.0.txt

@@ -0,0 +1,202 @@
+
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licenses/Qwen3.5-Apache-2.0.txt

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+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
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+   See the License for the specific language governing permissions and
+   limitations under the License.

model.json

@@ -0,0 +1,31 @@
+{
+  "format": "qev.checkpoint.v1",
+  "layout": "state-question-candidate.v1",
+  "spec": {
+    "base": "Qwen/Qwen3.5-4B-Base",
+    "revision": "710fd005d44d55ee27b7ad5147e318e546efdbfe",
+    "head_dim": 256,
+    "head_heads": 4,
+    "head_layers": 2,
+    "lora_rank": 64,
+    "lora_dropout": 0.0,
+    "weights_dtype": "bf16",
+    "attention": "sdpa",
+    "rows_per_forward": 32,
+    "max_padding_ratio": 1.25,
+    "max_padded_tokens": 8192,
+    "layout": "state-question-candidate.v1",
+    "choice_none_policy": "as-provided",
+    "candidate_interaction": "last-full-attention"
+  },
+  "limits": {
+    "max_state": 4096,
+    "max_question": 512,
+    "max_candidate": 256,
+    "max_path": 4096,
+    "max_candidates": 128
+  },
+  "adapter": true,
+  "readout_precision": "fp32-head-and-joint-reductions.v1",
+  "temperature": 1.0
+}

provenance.json

@@ -0,0 +1,70 @@
+{
+  "model": "Qev-4B",
+  "repo_id": "AustinFu/Qev-4B",
+  "revision": "v0.1.0",
+  "base": "Qwen/Qwen3.5-4B-Base",
+  "base_revision": "710fd005d44d55ee27b7ad5147e318e546efdbfe",
+  "step": 2786,
+  "initialization": "Qwen base with fresh LoRA and decision head",
+  "training": {
+    "questions": 44576,
+    "epochs": 2,
+    "global_batch": 32,
+    "world_size": 4,
+    "seed": 17,
+    "objective": "teacher-probability cross entropy",
+    "teacher_temperature": 1.563437713227029,
+    "student_temperature": 1.0,
+    "teacher": "Separate 9B research checkpoint with additional web and rule training, step 2658; not the public Qev-9B v0.2.0",
+    "late_split": null,
+    "online_option_augmentation": false
+  },
+  "evaluation": {
+    "execution": "reference",
+    "backbone_dtype": "bf16",
+    "head_dtype": "fp32",
+    "temperature": 1.0,
+    "max_state": 4096,
+    "max_path": 4096,
+    "recorded_at": "2026-10-02",
+    "rejected_questions": 0
+  },
+  "matched_subsets": {
+    "decision_dev_all": {
+      "correct": 1286,
+      "total": 1468
+    },
+    "decision_dev_clean": {
+      "correct": 1099,
+      "total": 1264
+    },
+    "transfer_dev_all": {
+      "correct": 618,
+      "total": 764
+    },
+    "transfer_dev_clean": {
+      "correct": 538,
+      "total": 656
+    },
+    "mmlupro": {
+      "correct": 503,
+      "total": 1000
+    },
+    "scienthoon": {
+      "correct": 670,
+      "total": 873
+    },
+    "wanli": {
+      "correct": 188,
+      "total": 256
+    },
+    "semif_handwritten": {
+      "correct": 130,
+      "total": 144
+    },
+    "jevbench_public": {
+      "correct": 190,
+      "total": 231
+    }
+  }
+}

tokenizer/tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
+size 19989325

tokenizer/tokenizer_config.json

@@ -0,0 +1,32 @@
+{
+  "add_prefix_space": false,
+  "audio_bos_token": "<|audio_start|>",
+  "audio_eos_token": "<|audio_end|>",
+  "audio_token": "<|audio_pad|>",
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "image_token": "<|image_pad|>",
+  "is_local": true,
+  "local_files_only": true,
+  "model_max_length": 131072,
+  "model_specific_special_tokens": {
+    "audio_bos_token": "<|audio_start|>",
+    "audio_eos_token": "<|audio_end|>",
+    "audio_token": "<|audio_pad|>",
+    "image_token": "<|image_pad|>",
+    "video_token": "<|video_pad|>",
+    "vision_bos_token": "<|vision_start|>",
+    "vision_eos_token": "<|vision_end|>"
+  },
+  "pad_token": "<|endoftext|>",
+  "pretokenize_regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>"
+}

training_config.json

@@ -0,0 +1,51 @@
+{
+  "model": {
+    "base": "Qwen/Qwen3.5-4B-Base",
+    "revision": "710fd005d44d55ee27b7ad5147e318e546efdbfe",
+    "head_dim": 256,
+    "head_heads": 4,
+    "head_layers": 2,
+    "lora_rank": 64,
+    "lora_dropout": 0.0,
+    "weights_dtype": "bf16",
+    "attention": "sdpa",
+    "rows_per_forward": 32,
+    "layout": "state-question-candidate.v1",
+    "max_padding_ratio": 1.25,
+    "max_padded_tokens": 8192,
+    "choice_none_policy": "as-provided",
+    "candidate_interaction": "last-full-attention"
+  },
+  "limits": {
+    "max_state": 4096,
+    "max_question": 512,
+    "max_candidate": 256,
+    "max_path": 4096,
+    "max_candidates": 128
+  },
+  "training": {
+    "epochs": 2,
+    "batch_size": 8,
+    "accum": 1,
+    "seed": 17,
+    "lr": 2e-05,
+    "head_lr": 0.0001,
+    "joint_gate_lr": 0.01,
+    "weight_decay": 0.01,
+    "warmup_steps": 20,
+    "head_warmup_steps": 100,
+    "reset_training_rng": true,
+    "gradient_checkpointing": true,
+    "save_every": 800,
+    "autocast": "bf16",
+    "batch_assignment": "leaf-balanced-v1",
+    "prefix_execution": "tree-batched",
+    "none_insert_prob": 0.0,
+    "none_absent_prob": 0.0,
+    "distillation": {
+      "cache": "data/teacher-logits-4b",
+      "weight": 1.0,
+      "temperature": 1.563437713227029
+    }
+  }
+}