Energy Pocket is a compact class-conditional energy-based image model trained with persistent contrastive divergence. Positive digit examples are assigned low energy while replay-buffer negatives are refined with Langevin dynamics and pushed upward. A discriminative energy loss anchors the ten class landscapes.

The evaluation measures frozen-judge fidelity, within-class variance, quantized uniqueness, nearest-training-image distance, exact-copy rate, and the energy gap between training examples and generated samples.

The 17,226-parameter model reached 99.8% frozen-judge fidelity across 1,000 Langevin samples. Every quantized sample was unique, the exact training-copy rate was zero, and mean nearest-training-image MSE was 0.03345. Generated samples fell 0.38 energy units below real examples and 59.76% of pixels saturated near a boundary, a measured warning that extended Langevin descent over-optimizes this small learned landscape.

The Space performs actual gradient-based Langevin sampling. Local verification does not imply that the Hugging Face repository is currently deployed.

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.