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| QEV (formerly Veyra) | |
| Copyright 2026 QEV contributors | |
| Copyright 2026 Veyra contributors | |
| QEV is an independent open-source research project for dynamic typed | |
| decisions over images and text. | |
| The backbone is Qwen/Qwen3.5-2B, published by the Qwen team under Apache-2.0. | |
| Model weights are not included in this repository and retain their upstream | |
| license and attribution requirements. | |
| https://huggingface.co/Qwen/Qwen3.5-2B | |
| The design takes inspiration from LAYA's typed decision interfaces and | |
| candidate scoring. No LAYA source code or model weights are included in | |
| this implementation. | |
| https://github.com/NandhaKishorM/laya | |
| TypeSafe JEV's public API is a reference for typed question and answer | |
| contracts. This repository does not contain JEV source code or weights. | |
| https://api.typesafe.ai/openapi.json | |
| Starter head training uses the MIT-licensed Beans dataset from AIR Lab, | |
| Makerere University. Its copyright and permission notice are preserved in | |
| docs/data-licenses/beans-MIT.txt. Dataset images are downloaded separately. | |
| https://github.com/AI-Lab-Makerere/ibean | |
| Foundation training also uses the following separately licensed datasets. | |
| Original records and photographs are not redistributed with the model package. | |
| TrashNet: Gary Thung and Mindy Yang, MIT. | |
| Kaggle redistribution: vminhkhoi/trashnet, version 1. Image/class byte hashes | |
| were matched against the original-author dataset before split construction. | |
| The original notice is preserved in docs/data-licenses/trashnet-MIT.txt. | |
| https://github.com/garythung/trashnet | |
| https://www.kaggle.com/datasets/vminhkhoi/trashnet | |
| SNLI: Stanford Natural Language Inference corpus, Stanford NLP Group, | |
| Samuel R. Bowman et al. (2015), CC-BY-SA-4.0. | |
| Selected premise/hypothesis pairs were converted into dynamic decision views; | |
| derived dataset material retains the applicable share-alike requirements. | |
| https://nlp.stanford.edu/projects/snli/ | |
| https://huggingface.co/datasets/stanfordnlp/snli | |
| https://creativecommons.org/licenses/by-sa/4.0/ | |
| BANKING77: PolyAI, Casanueva et al. (2020), CC-BY-4.0. | |
| Utterances and category names were converted into sampled eight-candidate | |
| questions and request-specific policy views; this is not the standard 77-way | |
| evaluation. Attribution and source revisions accompany the data protocol. | |
| https://github.com/PolyAI-LDN/task-specific-datasets | |
| https://huggingface.co/datasets/PolyAI/banking77 | |
| https://creativecommons.org/licenses/by/4.0/ | |
| CC-BY-4.0 and CC-BY-SA-4.0 legal texts in docs/data-licenses were obtained | |
| from the SPDX license-list-data text distribution. In the Hugging Face | |
| package these notices are under data-licenses/ instead of docs/data-licenses/. | |
| The LocalLLaMA/typed-decisions dataset is used for an external text benchmark. | |
| Its Apache-2.0 teacher-agreement targets are not objective ground-truth labels. | |
| https://huggingface.co/datasets/LocalLLaMA/typed-decisions | |
| Workflow v12 additionally adapts on the recorded training partition of that | |
| dataset; development, calibration and official test remain separate. Newly | |
| generated workflow tasks are controlled procedural examples, not recorded | |
| business outcomes. Their conditional probabilities follow their stated model. | |
| CIFAR-10: Alex Krizhevsky, Vinod Nair and Geoffrey Hinton. | |
| Used only as a 32-by-32 image transfer evaluation guard in Workflow v12. | |
| The uoft-cs/cifar10 dataset card marks the license as unknown. No CIFAR-10 | |
| images are redistributed. This does not establish permission for redistribution. | |
| https://www.cs.toronto.edu/~kriz/cifar.html | |
| https://huggingface.co/datasets/uoft-cs/cifar10 | |