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://onpanda.diyer22.com

Paper: https://huggingface.co/papers/2609.24983