OnPanda – Open-source token inspection＆data annotation tool from Step 5 Preview

StepFun on X: "We’ve open-sourced onPanda 🐼 — the tool we use internally for LLM data annotation and model inspection.

The workflow is simple: find an error, correct the token, and let the model continue.

✍️ Data annotation

- 52% lower median annotation time vs. manual post-editing

- SFT + preference data in one workflow, with high on-policy fidelity (ΔPPL <1% vs. the model’s resampling baseline)

- Precise token-level supervision with paired positive/negative examples, plus agent-trajectory annotation across image, audio, and video

🔎 Model inspection and debugging

- Inspect token probabilities and top-k alternatives, steer decoding token by token, and explore SVG generation, web development, and agent tasks directly in the browser.

Try it (mobile-friendly): https://t.co/57vAeJetWD

Paper: https://t.co/1MX1eVTREL"

We’ve open-sourced onPanda 🐼 — the tool we use internally for LLM data annotation and model inspection.

The workflow is simple: find an error, correct the token, and let the model continue.

✍️ Data annotation

- 52% lower median annotation time vs. manual post-editing

- SFT + preference data in one workflow, with high on-policy fidelity (ΔPPL <1% vs. the model’s resampling baseline)

- Precise token-level supervision with paired positive/negative examples, plus agent-trajectory annotation across image, audio, and video

🔎 Model inspection and debugging

- Inspect token probabilities and top-k alternatives, steer decoding token by token, and explore SVG generation, web development, and agent tasks directly in the browser.

Try it (mobile-friendly): onpanda.diyer22.com

Paper: huggingface.co/papers/2609.24… I spent two years building this interactive tool to let you steer LLMs and agents at the token level.

Introducing onPanda — a web app for token visualization & control, model inspection, data annotation, and more.

Try it online (works on mobile): onpanda.diyer22.com - We’ve open-sourced onPanda 🐼 — the tool we use internally for LLM data annotation and model inspection.

The workflow is simple: find an error, correct the token, and let the model continue.

✍️ Data annotation

- 52% lower median annotation time vs. manual post-editing

- SFT + preference data in one workflow, with high on-policy fidelity (ΔPPL <1% vs. the model’s resampling baseline)

- Precise token-level supervision with paired positive/negative examples, plus agent-trajectory annotation across image, audio, and video

🔎 Model inspection and debugging

- Inspect token probabilities and top-k alternatives, steer decoding token by token, and explore SVG generation, web development, and agent tasks directly in the browser.

Try it (mobile-friendly): onpanda.diyer22.com

Paper: huggingface.co/papers/2609.24… - I spent two years building this interactive tool to let you steer LLMs and agents at the token level.

Introducing onPanda — a web app for token visualization & control, model inspection, data annotation, and more.

Try it online (works on mobile): onpanda.diyer22.com

- Huge thanks to @diyerxx and the team for this work 🫡