SRT Showcase — watch a frozen model think

Live, token-by-token introspection of Qwen-2.5-7B + the SRT adapter.

As the model generates, every token is tinted by its predictive entropy (the validated uncertainty signal). At the highest-effort token positions, chosen by an adaptive-density scheduler, the Activation Verbalizer decodes the model's internal hidden state into natural language, and each verbalization carries a round-trip fidelity badge: it is re-encoded and compared back to the original hidden state, so the "this is what the model was thinking" claim is visibly self-validating.

Features:

• Live token stream tinted by entropy or SRT divergence, with per-token hover rollovers (entropy, divergence, reflexivity r̂, regime).
• Running entropy meter and entropy / divergence charts.
• Expand/collapse verbalization cards with round-trip fidelity badges.
• A/B panel: the same prompt with SRT injection on vs off (bare backbone), seeded identically.
• A curated example gallery covering confident recall, false premises, misconceptions, reasoning pivots, genuine uncertainty, and safety boundaries.

Honest scope

Entropy is the load-bearing uncertainty signal. The SRT side-channels (divergence, r̂, regime) and the verbalizations are shown as observational readouts of internal state. This is a window into the model, not a validated hallucination detector.

Notes

• First request is cold (~60–90 s) while ZeroGPU acquires a GPU and the ~16 GB backbone weights load; subsequent requests are warm.
• A second backbone copy is loaded for the Activation Verbalizer.

Source: https://github.com/space-bacon/SRT