π¦ UltraData Collection | π UltraData | π Tech Report (Coming Soon) | π€ UltraData-Code | π€ UltraData-Code-L2 Classifier
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## π Introduction **UltraData-Stack-v3-L2** is a selected code corpus of approximately 240B tokens for the 11 programming languages in [The Stack v3](https://huggingface.co/datasets/HuggingFaceCode/stack-v3-train), obtained through quality-control cleaning and fine-grained selection with [UltraData-Code-L2-Classifier](https://huggingface.co/openbmb/UltraData-Code-L2-Classifier). This dataset transfers the language-adaptive L2 selection framework proposed by **UltraData-Code** to another source corpus, in order to examine whether file-level algorithm-related relevance selection can generalize to data sources beyond UltraData-Code-L1. Construction has two stages. First, source records are cleaned by removing URL-only entries, overly short records, and records containing garbled or malformed text. Second, a language-specific selector combines file-role prediction, algorithmic-relevance scoring, an `EXCLUDE` veto, and a shared code-quality constraint. The relevance threshold is calibrated separately for each language. The resulting dataset retains **20.10%** of the cleaned 11-language candidate pool, and is intended to concentrate interpretable computation and problem-solving logic while preserving broad language coverage. ## π’ What's New - **[2026.10.12]** The [***UltraData-Stack-v3-L2***](https://huggingface.co/datasets/openbmb/UltraData-Stack-v3-L2) dataset is released! πππ - **[2026.09.07]** The [***UltraData-Code***](https://huggingface.co/datasets/openbmb/UltraData-Code) dataset is released! It is a complete implementation of the [UltraData](https://ultradata.openbmb.cn/) [L0-L4 tiered data management framework](https://arxiv.org/pdf/2602.09003). It covers four code data states from L0 through L3, with each level corresponding to a distinct construction stage. This release currently open-sources **L2** (\~400B tokens) and **L3** (\~150B tokens) across 11 programming languages. πππ - **[2026.09.07]** ***[MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B) is released!***, the second model in the MiniCPM5 series after MiniCPM5-1B. It is a dense 2B Transformer that scales up the same training recipe, built for on-device, local deployment, and resource-constrained scenarios. It reaches **2B-class open-source SOTA**, remains competitive with 4B-class models, and shows particular advantages in coding, mathematics, long-context understanding, tool use, and agentic tasks. UltraData-RL-2609 serves as the core RL dataset for MiniCPM5-2B. πππ - **[2026.02.08]** The [***UltraData***](https://ultradata.openbmb.cn/) platform is now live, introducing the [L0-L4 tiered data management framework](https://arxiv.org/pdf/2602.09003). πππ ## π‘ Highlights - **Transferable algorithmic-relevance selection:** The same selection objective and model interfaces used for UltraData-Code-L2 are adapted to Stack-v3-train, with language-specific evidence and thresholds. - **Strong performance gains:** Under the same 1B base model and 10B-token continual-pretraining setup, UltraData-Stack-v3-L2 reaches **30.00** average pass@1 on EvalPlus and **22.75** on MultiPL-E, compared with **25.37** and **17.30** for Stack-v3-train. ## π Dataset Statistics Token counts are estimated with the tokenizer used in the technical report. | Language | After cleaning | After selection | Retention | Token share | Tokens | |:--|--:|--:|--:|--:|--:| | Python | 96,961,008 | 24,671,724 | 25.44% | 15.86% | 38.94B | | Java | 165,346,828 | 41,963,467 | 25.38% | 28.76% | 70.60B | | JavaScript | 110,967,790 | 14,521,755 | 13.09% | 12.92% | 31.73B | | C++ | 67,981,897 | 17,990,102 | 26.46% | 12.78% | 31.38B | | PHP | 65,565,149 | 5,500,141 | 8.39% | 10.26% | 25.19B | | Ruby | 32,667,620 | 6,389,441 | 19.56% | 2.39% | 5.86B | | Shell | 26,704,436 | 5,045,813 | 18.90% | 8.83% | 21.67B | | C# | 17,309,141 | 3,548,274 | 20.50% | 3.64% | 8.93B | | Go | 29,683,899 | 3,138,105 | 10.57% | 1.65% | 4.04B | | Rust | 11,674,463 | 2,907,231 | 24.90% | 2.13% | 5.24B | | R | 7,640,182 | 1,456,974 | 19.07% | 0.78% | 1.92B | | **Total** | **632,502,413** | **127,133,027** | **20.10%** | **100%** | **245.50B** | ## π Evaluation Results The 10B-token comparisons use the same 1B foundation model, training settings, decontamination, and evaluation protocol. **Python results:** UltraData-Stack-v3-L2-py ranks second among the evaluated L2 datasets by average EvalPlus pass@1 and obtains the highest MBPP and MBPP+ scores at L2.
