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  <div class="eyebrow">Jacob Garcia · Hugging Face Model Foundry</div>
  <h1>Kernelmind Model Machine Lab</h1>
  <p class="lead">Interactive learned-versus-reference OS transition comparison. This showcase backs up the
  trained artifacts, measured evaluation, and complete runnable source.</p>
  <div class="actions">
    <a class="button" href="https://huggingface.co/spaces/ARotting/kernelmind-model-machine-lab/tree/main">Explore every file</a>
    <a class="button alt" href="https://huggingface.co/ARotting">View the full foundry</a>
  </div>
  <div class="grid">
    <section class="card">
      <h2>Verified project card</h2>
      <pre># 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.

```bash
uv run python projects/kernelmind-model-machine/train.py
uv run pytest tests/test_kernelmind_model_machine.py
```
</pre>
      <h2>Evaluation snapshot</h2>
      <pre>{
  &quot;model&quot;: &quot;KernelMind Neural Model Machine&quot;,
  &quot;parameters&quot;: 6937,
  &quot;training_transitions&quot;: 42598,
  &quot;heldout_transitions&quot;: 5325,
  &quot;best_epoch&quot;: 4,
  &quot;test&quot;: {
    &quot;exact_transition_accuracy&quot;: 0.9996244311332703,
    &quot;state_bit_accuracy&quot;: 0.999953031539917,
    &quot;blocked_accuracy&quot;: 1.0,
    &quot;transitions&quot;: 5325
  },
  &quot;multi_step_audit&quot;: {
    &quot;rollouts&quot;: 2000,
    &quot;steps_per_rollout&quot;: 6,
    &quot;exact_step_fraction&quot;: 1.0,
    &quot;exact_six_step_rollout_fraction&quot;: 1.0
  },
  &quot;boundary&quot;: &quot;Models an eight-bit virtual state; it does not control the host OS&quot;
}</pre>
    </section>
    <section class="card">
      <h2>Backed-up artifact tree</h2>
      <input id="filter" placeholder="Filter files…" autocomplete="off">
      <ul id="files"><li><code>README.md</code></li>
<li><code>__pycache__/app.cpython-311.pyc</code></li>
<li><code>__pycache__/model.cpython-311.pyc</code></li>
<li><code>__pycache__/schema.cpython-311.pyc</code></li>
<li><code>app.py</code></li>
<li><code>artifacts/kernelmind-model-machine/evaluation.json</code></li>
<li><code>artifacts/kernelmind-model-machine/model_machine.safetensors</code></li>
<li><code>data/transitions.parquet</code></li>
<li><code>model.py</code></li>
<li><code>requirements.txt</code></li>
<li><code>schema.py</code></li>
<li><code>train.py</code></li></ul>
    </section>
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