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
File size: 2,043 Bytes
fac5b68 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # 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.
|