title: Kernelmind Model Machine Lab
emoji: 🧪
colorFrom: indigo
colorTo: blue
sdk: static
app_file: index.html
pinned: false
KernelMind Neural Model Machine
This project trains the machine that follows KernelMind AI OS. A compact neural world model learns the transition function of an eight-bit virtual computer from current state, capability flags, and one of thirteen OS actions. Training covers the exhaustive 53,248-state/action corpus.
The evaluation separates held-out one-step transition accuracy from six-step autoregressive rollout fidelity. The Space places the neural prediction beside the deterministic reference transition for direct inspection.
It is a learned virtual-machine dynamics model, not a hypervisor and not a model with access to the host operating system.
Verified local result
The 6,937-parameter model reached 99.962% exact transition accuracy and 100% blocked-action accuracy across 5,325 held-out transitions. In a separate autoregressive audit, it reproduced every step and final state across 2,000 randomized six-action rollouts.
uv run python projects/kernelmind-model-machine/train.py
uv run pytest tests/test_kernelmind_model_machine.py
Hosted showcase
This free static Space preserves the complete original Gradio source, trained artifacts, evaluation files, and local launch requirements. Hugging Face now requires PRO for CPU-backed Gradio hosting, so the public landing page is static while the checked-in app.py remains the authoritative runnable demo.