Your OCR says it’s done—but did it get everything right? 🤔 In high-stakes workflows, even a small error can be costly. 🚀 Meet the MiniCPM-OCR suite: an open-source solution to parse fast and guard the results. Two new models: 🔹 MiniCPM-OCR 1.0 — SOTA OCR performance and speed at 1B scale. Compact, accurate, and fast. 🔹 MiniCPM-OCR Jev — A 2B decision model that helps assess parsing accuracy, with performance approaching that of Qwen3.5-397B-A17B on JevOCRBench. ⚡ What’s new? Small model, SOTA results: MiniCPM-OCR 1.0 scores 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2. A sharp goalkeeper 🛡️: MiniCPM-OCR Jev is model-agnostic, checking text, formulas, and tables from different OCR parsers. It achieves 93.6 on JevOCRBench, outperforming JEV-35B-A3B (90.1) and approaching Qwen3.5-397B-A17B (93.8). Up to 128 pages per minute 🤯: With DSpark, MiniCPM-OCR runs at 1.49× the speed of PaddleOCR-VL-1.6 and 3.62× that of MinerU2.5 Pro. The SDK’s concurrency optimizations further boost throughput to as much as 128 pages per minute. Deploy where you work: Day-0 support for Arm, Houmo AI, Intel, and NVIDIA, plus 10 more chip vendors through FlagOS. Ready for your stack: vLLM, SGLang, llama.cpp, and Ollama, with quantized models, fine-tuning support, and Agent Skills. 🔗 GitHub: 🤗 Hugging Face: MiniCPM-OCR 1.0： MiniCPM-OCR Jev： 👉 Try the Demo： Use the API：

🚀 Introducing MiniCPM-V 4.7, our new open-source MLLM series! Advancing the Performance–Efficiency Pareto Frontier. It brings strong multimodal capabilities to iOS and Android devices with extreme efficiency. Two new models: 🔹 MiniCPM-V 4.7 35B-A3B — Bring SOTA performance to local devices with newly introduced LES (Lookahead Expert Scheduling) technique, a new milestone of on-device multimodal AI. 🔹 MiniCPM-V 4.7 1B — Lightweight, efficient multimodal AI. Easier to deploy. ⚡ What's new? SOTA performance: Competitive results across 34 benchmarks spanning multimodal understanding, STEM reasoning, OCR and long-video comprehension. Fast on-device inference: LES predicts and preloads expert weights to reduce inference stalls, boosting 5× speedup on iPad Pro M4 (16GB) with byte-for-byte identical outputs. It enables MiniCPM-V 4.7 35B-A3B to achieve 17+ tokens/s on iPad Pro M4 (16GB) and 13+ tokens/s on iPhone 17 Pro Max, while costing only 3GB memory 🤯 Efficient visual processing: 4× and 16× hybrid visual token compression. Even at 16× compression, 35B-A3B scores 90.4 on OCRBench, 5.2 points ahead of Qwen3.6 35B-A3B. Easier customization: We introduce Self-Replay Fine-Tuning, a simple way to customize new target capabilities at low cost while maintaining general performance. Broad hardware support: Day-0 adaptations for Apple, Intel, AMD, Arm and Rockchip, with many more platforms supported through FlagOS! Download, deploy, and build something we haven't imagined yet. 👀👇 🔗 GitHub: 🤗 Hugging Face:

🚀MiniCPM5-2B hits #1# on @huggingface Trending! 🏆 Huge thanks to the community for the incredible support!💗 Ranked #1# among open-weight models under 4B parameters worldwide in the @ArtificialAnlys Intelligence Index. 🤖 Built for Agentic AI Tool calling, deep search, code generation, and more—bringing capable Agents to phones, PCs, and vehicles. 🧠 More than model weights We’re opening up training code, Agent SFT/RL data, UltraX, Meshy, and JustRL II, enabling deeper research and easier reproduction. 📱 Ready for the edge Day 0 support for Intel, AMD & Arm, plus mainstream inference and fine-tuning frameworks. Try the model here: 🤗 Hugging Face: 💻 GitHub:

🚀 Meet MiniCPM5-2B, a 2B-parameter language model bringing high intelligence density to the edge, now open source! It ranks #1# among open-source models under 4B parameters on the @ArtificialAnlys Intelligence Index, with a score of 23. It also scores 20 on the Agentic Index, bringing an early form of general-purpose agent capability to the edge. Across 34 benchmarks, MiniCPM5-2B achieves an average score of 53.9, covering coding, math, long-context understanding, tool use, and agentic tasks. And this release goes beyond the model itself. We’re opening up the data, training recipes, and RL stack behind MiniCPM5-2B. 🤗 Hugging Face: 💻 GitHub: Modelscope: Web:

🚀 MASSIVE upgrades for UltraData data stack! The tiered data management (L0-L4) framework has now fully battle-tested on MiniCPM5-1B and is ready for your models! No gatekeeping, just pure data power. What’s NEW in our latest release:👇 ✅ Ultra-FineWeb-L3 — 600B tokens (200B+ Chinese, 400B+ English) of high-density synthetic pre-training data, which expanded from Ultra-FineWeb via multi-style rewriting & QA generation, and has used in MiniCPM5-1B's decay stage. 🤗 ✅ UltraData-SFT-2605 — 15M+ post-training samples across math, code, knowledge & instruction following, with deep-thinking and non-thinking training styles, used in MiniCPM5-1B's SFT stage. 🤗