README.md

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title: README
emoji: 🐢
colorFrom: yellow
colorTo: green
sdk: static
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• Data-Juicer is a one-stop system to process text and multimodal data for and with foundation models (typically LLMs).
• You can try it in the playground with a managed JupyterLab.
• More details can be found in our homepage or documents.

News

• 🛠️ [2025-06-04] How to process feedback data in the "era of experience"? We propose Trinity-RFT: A General-Purpose and Unified Framework for Reinforcement Fine-Tuning of LLMs, which leverages Data-Juicer for its data pipelines tailored for RFT scenarios.
• 🎉 [2025-06-04] Our Data-Model Co-development Survey has been accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)! Welcome to explore and contribute the awesome-list.
• 🔎 [2025-06-04] We introduce DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? A synthetic benchmark revealing notable performance drops despite large models' proficiency with short descriptions.
• 🎉 [2025-05-06] Our work of Data-Juicer Sandbox has been accepted as a ICML'25 Spotlight (top 2.6% of all submissions)!
• 💡 [2025-03-13] We propose MindGYM: What Matters in Question Synthesis for Thinking-Centric Fine-Tuning? A new data synthesis method that enables large models to self-synthesize high-quality, low-variance data for efficient fine-tuning, (e.g., 16% gain on MathVision using only 400 samples).
• 🤝 [2025-02-28] DJ has been integrated in Ray's official Ecosystem and Example Gallery. Besides, our patch in DJ2.0 for the streaming JSON reader has been officially integrated by Apache Arrow.
• 🎉 [2025-02-27] Our work on contrastive data synthesis, ImgDiff, has been accepted by CVPR'25!
• 💡 [2025-02-05] We propose a new data selection method, Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data. It is theoretically informed, via treating diversity as a reward, achieves better overall performance across 7 benchmarks when post-training SOTA LLMs.
• 🚀 [2025-01-11] We release our 2.0 paper, Data-Juicer 2.0: Cloud-Scale Adaptive Data Processing for Foundation Models. It now can process 70B data samples within 2.1h, using 6400 CPU cores on 50 Ray nodes from Alibaba Cloud cluster, and deduplicate 5TB data within 2.8h using 1280 CPU cores on 8 Ray nodes.
• 🛠️ [2025-01-03] We support post-tuning scenarios better, via 20+ related new OPs, and via unified dataset format compatible to LLaMA-Factory and ModelScope-Swift.