Instructions to use SimpleJev/JevAny-Robotics-4B-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SimpleJev/JevAny-Robotics-4B-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("Qwen/Qwen3.5-4B") model = PeftModel.from_pretrained(base_model, "SimpleJev/JevAny-Robotics-4B-LoRA") - Notebooks
- Google Colab
- Kaggle
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Download ACKNOWLEDGEMENTS.md from SimpleJev/JevAny-Robotics-4B-LoRA: direct link, hf CLI and curl.
- Browser
- Download file 2.04 kB
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https://huggingface.co/SimpleJev/JevAny-Robotics-4B-LoRA/resolve/main/ACKNOWLEDGEMENTS.md
- Command line
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hf download hf://SimpleJev/JevAny-Robotics-4B-LoRA/ACKNOWLEDGEMENTS.md
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curl -L -o ACKNOWLEDGEMENTS.md https://huggingface.co/SimpleJev/JevAny-Robotics-4B-LoRA/resolve/main/ACKNOWLEDGEMENTS.md
2.04 kB
| # Acknowledgements | |
| JevAny includes modified infrastructure code from [Kev](https://github.com/jaredpalmer/kev), created by Jared Palmer and released under Apache-2.0. The required attribution is also recorded in `NOTICE` and in derived source files. | |
| JevAny develops a separate research direction around calibration-aware reinforcement learning for decision models. Tianxin Wei created the current training system, RLCR implementation, checkpoints, evaluation, release tooling, and product roadmap. | |
| The architecture was informed by the public analysis in [Jev's Architecture Unmasked](https://archerhume.com/posts/jevs-architecture-unmasked) and the public System One API. JevAny does not contain Jev weights, outputs or private implementation details. | |
| RLCR follows the reward proposed in *Beyond Binary Rewards: Training LMs to Reason about Their Uncertainty* by Damani et al. JevAny adapts it to a prefill-only pointer model and does not reproduce the paper's generated reasoning rollouts. | |
| Qwen3.8-27B is produced by the Qwen team. The adapters in this project require its separately distributed base weights. | |
| The Phi-4 vision conversion helper adapts configuration and weight-name mappings from Hugging Face Transformers under Apache-2.0. It merges Microsoft's pretrained vision LoRA before JevAny training. | |
| The native Phi-4 Reasoning Vision adapter follows Microsoft's [published model layout and image preprocessing](https://huggingface.co/microsoft/Phi-4-reasoning-vision-15B/tree/c3e4fac79ddace21976ced56fbf1564b8bd8c89f), released under MIT. Its weights are downloaded separately from Microsoft. | |
| The README teaser uses Lucide icons. The [source records](docs/icons/sources.json) and [license notices](docs/icons/LICENSE) accompany the editable SVG. | |
| The supported-model cards use logos from [Lobe Icons](https://github.com/lobehub/lobe-icons) under MIT. Their [source records](docs/model-logos/sources.json) and [license](docs/model-logos/LICENSE) accompany the SVG. Model and publisher marks belong to their respective owners. | |