README.md

metadata

title: SRT introspect
emoji: 🧭
colorFrom: indigo
colorTo: pink
sdk: gradio
sdk_version: 4.44.1
app_file: app.py
python_version: '3.10'
pinned: true
short_description: Adaptive-density reasoning traces over a frozen Qwen-2.5-7B
hardware: zero-a10g
models:
  - Qwen/Qwen2.5-7B
  - RiverRider/srt-adapter-v1.0
  - RiverRider/srt-nla-av-v1
tags:
  - srt
  - semiotic-reflexive-transformer
  - interpretability
  - introspection
  - uncertainty
  - visualization
  - llm
thumbnail: >-
  https://huggingface.co/spaces/RiverRider/srt-introspect/resolve/main/thumbnail.png

SRT · introspect

Live demo of the SRT-Adapter (Stage 3) + Activation Verbalizer (Stage 4) applied to a frozen Qwen-2.5-7B backbone.

Enter a prompt → the model generates a continuation, every token is tinted by the adapter's per-step divergence, and the scheduler picks the most novel positions to narrate via the AV.

• Code: https://github.com/space-bacon/SRT
• Adapter weights: RiverRider/srt-adapter-v1.0
• AV weights: RiverRider/srt-nla-av-v1

The first request on a fresh ZeroGPU slice takes ~60–90 s (weight download). Subsequent generations are ~7–10 s.