| --- |
| library_name: pytorch |
| tags: [pytorch, standalone, modernbert, decision-model] |
| pipeline_tag: text-classification |
| --- |
| |
| # Jev_CeNN — CUDA-optimized hybrid cellular attention |
| |
| The model weights, tokenizer, config and all custom Python runtime code are |
| included in this repository. The original research package is NOT required. |
| |
| Install: pip install torch transformers==4.57.6 safetensors huggingface_hub |
|
|
| Download the load_model.py file or the entire repository, then run: |
| |
| ~~~python |
| from load_model import load_model, predict |
| model, tokenizer = load_model("USERNAME/MODEL", device="cpu") |
| output = predict("USERNAME/MODEL", state={"speed": 20}, |
| question={"type": "choice", "instructions": "Select action", |
| "criteria": {"accelerate": None, "brake": None}}) |
| print(output) |
| ~~~ |
| |
| The bundled loader executes bundled runtime.py. Review/pin trusted code before use. |
| The model is a typed-decision encoder, NOT a text-generation model. |
|
|
| W1024 at 512-token input is a quadratic full-context control and |
| does NOT demonstrate a faster or attention-free replacement. |
| Consult training_report.json for actual held-out metrics. |
| |
| Optional GPU acceleration uses Triton and falls back to PyTorch on CPU. Install triton>=3.0 for NVIDIA GPU inference. Speed and numerical parity must be measured on the target GPU. |
| |
