| # Licensing and attribution |
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| ## Qev materials |
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| Copyright 2026 Qiqian Fu and Qev contributors. |
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| 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. |
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| Third-party materials retain the terms listed below. The Qev license does not relicense training datasets, external services, or third-party recordings. |
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| ## Included and adapted materials |
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| | Material | Source and copyright | License and scope | |
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| | 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. | |
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| 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. |
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| 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. |
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| ## Dependencies installed separately |
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| Dependencies are installed from their own distributions and retain their licenses: |
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| - [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. |
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| 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. |
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| ## Training data |
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| 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. |
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| Qev-9B v0.3.0 also uses 2,230 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. |
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| The complete research training mixture and distillation data are not distributed. Other source datasets retain their own terms. |
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| 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. |
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