| --- |
| title: Kernelmind Model Machine Lab |
| emoji: 🧪 |
| colorFrom: indigo |
| colorTo: blue |
| sdk: static |
| app_file: index.html |
| pinned: false |
| --- |
| |
| # KernelMind Neural Model Machine |
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| 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. |
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| 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. |
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| It is a learned virtual-machine dynamics model, not a hypervisor and not a model |
| with access to the host operating system. |
|
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| ## Verified local result |
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| 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. |
|
|
| ```bash |
| uv run python projects/kernelmind-model-machine/train.py |
| uv run pytest tests/test_kernelmind_model_machine.py |
| ``` |
|
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| ## Hosted showcase |
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| 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. |
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