阶跃星辰（StepFun）宣布开源内部使用的 LLM 数据标注与模型检查工具 onPanda，工作流为找到错误、修正 token、让模型继续生成。数据标注方面，标注时间中位数比人工后编辑降低 52%，SFT 与偏好数据可在同一流程完成（ΔPPL <1%），支持 token 级正负样本监督及图像、音频、视频上的 agent 轨迹标注。
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
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): https://onpanda.diyer22.com/在 X 查看被引用的帖子
来源：StepFun · x.com