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⚡ 适合中国宝宝的 Jev 入门教程｜手把手带你了解关于 Jev 的一切——Jupyter Notebook 轻松实验，从三种问题原语到 18 篇实战配方、语音智能家居、模型评测与本地微调，全面掌握 System One 判断模型的开发范式
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CommunityJev Guides & Articles
Jev Cookbook — datawhalechina
Chinese-language Jev cookbook with runnable Jupyter notebooks covering the three primitives, 18 official recipes, evaluation method, voice smart-home demo, and local Laya fine-tuning.
- Stars
- 335
- Language
- Python
- Published by
- Community
- Author
- datawhalechina
- Last verified
- Oct 2026
Used inJev for Software & DevTools
- Languages
- Python
Highlights
- Runs offline examples without an API key, then uses the same code against the live model once a key is configured.
- Teaches one-judgment-per-question discipline across Choice, Score, and Noul with measured probability and cost outputs.
- Includes 18 reproduced official recipes, a speech-driven 3D smart-home build, and 12 runnable game and app projects.
- Documents a 231-question four-metric eval method plus RLCD fine-tuning of the open Laya peer and Pi and DSH integration.
Watch out
Chinese-language community tutorial; the hosted docs site is a community translation with the English original authoritative, and bundled third-party items keep their own licenses. Copy reviewed against the repository README on 2026-10-04, not from running it.
Questions about this entry
Short answers drawn from the linked project or page.
Basic Python and Jupyter use is enough, with no machine-learning background assumed. Python 3.10 or newer is required, and a TypeSafe key is optional because offline examples cover the code paths first.
Yes. Reported probabilities, latency, token counts, and costs come from re-checkable measured outputs, with hand-made demo data explicitly labeled. The eval chapter uses 231 public questions across four dimensions with no retries or fallbacks.
Eleven chapters move from primitives and architecture patterns to 18 reproduced recipes, a speech-driven 3D smart-home build, 12 game and app projects, agent integration, and RLCD fine-tuning of the open Laya peer with Chinese datasets.
The tenth chapter walks dataset construction, training, and a local Jev-compatible service for Laya, while the ninth chapter gates Pi tool calls with Jev judgments and replays DSH decision collaboration.
Yes. The course home and an eleven-chapter guide are at datawhalechina.github.io/jev-cookbook/, and the full knowledge base sits under /materials/ with in-site reading and search. To run the notebooks, clone into main/ and use ./setup_env.sh to build the environment in one step. Each chapter's notebook runs independently, so you can work through them out of order.
Original content is CC BY-NC-SA 4.0, which is non-commercial, so read that before reusing chapters. Collected third-party projects such as jev-games and JevBench item sets keep their authors' copyright and their own licences. The Chinese translation of the official documentation is an unofficial community translation, and the README points back to the English original as authoritative.
Five credited contributors. Wang Yiming leads the project and handles the base tutorial plus Laya deployment and fine-tuning, with four others covering tutorials, applications, the Pi Jev application, the Jev harness and model evaluation. The stated next steps are video walkthroughs, WebSocket bidirectional streaming for the voice chapters, and a Chinese extension set for JevBench.
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