Publish Interactive learned-versus-reference OS transition comparison

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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>
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	<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>{
	"model": "KernelMind Neural Model Machine",
	"parameters": 6937,
	"training_transitions": 42598,
	"heldout_transitions": 5325,
	"best_epoch": 4,
	"test": {
	"exact_transition_accuracy": 0.9996244311332703,
	"state_bit_accuracy": 0.999953031539917,
	"blocked_accuracy": 1.0,
	"transitions": 5325
	},
	"multi_step_audit": {
	"rollouts": 2000,
	"steps_per_rollout": 6,
	"exact_step_fraction": 1.0,
	"exact_six_step_rollout_fraction": 1.0
	},
	"boundary": "Models an eight-bit virtual state; it does not control the host OS"
	}</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>
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