🚀 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: github.com/OpenBMB/MiniCPM-V 🤗 Hugging Face: huggingface.co/collections/o…

🚀 MiniCPM-V 4.7 1B is bringing open-source multimodal AI to everyday iPhones! Developer @mustafaergisi shared that the model is already running in OnDeviceLLM on everyday iPhones, beyond the 12GB Pro Max. 📱 What does that look like in practice? 🧾 Read receipts and ask about the total 📃 Pull information from flyers 🖼️ Understand screenshots ✈️ Run offline, even in Airplane Mode 🔒 Keep image processing on the device This is a compelling example of what small multimodal models can make possible: useful visual understanding on the phone you already carry. For developers exploring on-device AI, MiniCPM-V 4.7 1B offers a concrete example of an open-source model finding its way into a real app. 🛠️ Great to see the community turning MiniCPM-V into everyday experiences. 🙌 🚀 What will you build with MiniCPM-V 4.7? We welcome your apps, demos, and real-world use cases. Share your project and tag @OpenBMB! 👉 Check out the app: ondevicellm.app 🔗 MiniCPM-V GitHub: github.com/OpenBMB/MiniCPM-V 🤗 Model info: huggingface.co/collections/o… #MiniCPM #OnDeviceAI #OpenSource

📱 A 35B MoE model running on a phone at over 20 tokens/s. Meet Lookahead Expert Scheduling (LES), the inference technology behind MiniCPM-V 4.7’s efficient on-device performance. 🧠 The challenge: 35B total parameters, 3B activated per token, and limited device memory. Keeping experts in flash storage makes the model fit, but loading their weights can leave computation waiting. LES co-optimizes expert loading, compute scheduling, and weight access: ⚡ Expert prefetch and compute scheduling: Cross-layer routing prediction prefetches experts the next layer is likely to need. Experts selected by the actual router that are already loaded compute first, while the remaining required experts load in parallel. Prediction only guides prefetch and scheduling: every required expert is still loaded and used. No additional training is needed. 💾 Weight access optimization: Repack expert weights into contiguous storage to reduce scattered reads, and apply precise layer-wise mixed-precision quantization to reduce weight storage and transfer costs while preserving model capability. We also adapted DSpark speculative sampling to work alongside LES in the on-device inference pipeline. In our iPhone 18 Pro Max tests, the full system reaches 20.51 tokens/s, up from a 3.38 tokens/s on-demand loading baseline: 6.07× faster. 🔗 xitter.cf/OpenBMB/status/2112529… The original model architecture and expert routing stay intact. Smarter scheduling makes larger models practical on-device. 👉 MiniCPM-V GitHub: github.com/OpenBMB/MiniCPM-V 🤗 Model info: huggingface.co/collections/o… 💻 Technical Details: github.com/tc-mb/llama.cpp-o… #MiniCPM #OnDeviceAI #OpenSource

🔍 What does your GitHub profile say about your contributions? Meet ghfind, an open-source GitHub scoring and roasting platform with 12K daily visits, now powered by our MiniCPM-V 4.7 API as its main model. 💯 Enter a GitHub username to get a 0–100 value & trust score, one of five verdict tiers, a sharp roast grounded in public data, and practical suggestions for improvement. Six scoring dimensions and ten red-flag signals help distinguish sustained contributions from inflated stars, bot activity, and PR farming. 🔥 🧠 Behind the roast is a demanding workload: 30–50KB of input per analysis, code and contribution history to interpret, and structured JSON to generate. The developer reports near-100% success with MiniCPM-V 4.7 on these tasks. 🚀 What will you build with MiniCPM-V 4.7? We welcome your apps, demos, and real-world use cases. Share your project and tag @OpenBMB! Try it: ghfind.com ⭐ Explore the code: github.com/hikariming/ghfind 🔗 MiniCPM-V GitHub: github.com/OpenBMB/MiniCPM-V 🤗 Model info: huggingface.co/collections/o… #MiniCPM #OpenBMB #OpenSource #GitHub #AI

35B multimodal AI. Running locally on an iPhone. Now at over 20 tokens/sec. 🚀 MiniCPM-V 4.7 achieves 20.51 tokens/sec average decoding speed on iPhone 18 Pro Max (12GB) — a 6.07× speedup over the same device’s unoptimized SSD expert-loading baseline. ⚡ Behind the speed: Lookahead Expert Scheduling (LES), optimized expert loading, fine-grained quantization, and DSpark speculative decoding. 📱 Watch the new on-device demo below. Powerful multimodal AI, right in your pocket. Try it 👇 🔗 GitHub: github.com/OpenBMB/MiniCPM-V 🤗 Hugging Face: huggingface.co/collections/o… LES: github.com/tc-mb/llama.cpp-o… #MiniCPM #OpenBMB

From data and charts to full reports, running locally. Great to see @aiwithsally putting MiniCPM-V 4.7 to work in an automated reporting workflow. Thanks for sharing the demo!

Great to see community experiments with MiniCPM-V 4.7! Thanks @techNmak for sharing your neo-brutalist portfolio UI. Curious to see what you build next!

100 tok/s, multimodal, and running locally on ~3 GB of memory. 🔥 MiniCPM-V 4.7 makes on-device AI even more practical. Love seeing the @llama_cpp ecosystem make it easy to try!

🚀 MiniCPM-V 4.7 is now supported in vLLM v0.32.0! Thanks @vllm_project for the support. This makes it easier to serve MiniCPM-V 4.7 with high throughput and low latency.

🔱 Why can irrelevant text outrank an image that actually contains the answer? We find that multimodal retrievers can perform well on text-only and image-only corpora, yet degrade sharply when text and images are mixed. We call this phenomenon "Chaos in the Text." Our analysis reveals a systematic Modality Preference: text often receives higher similarity scores than semantically aligned images. We introduce Trident, which jointly trains Text / Image / Fused views with Multi-Positive View InfoNCE. On 6 visual-document retrieval benchmarks, Trident-Qwen3VL-2B improves Mix Avg NDCG@10 from 40.12 → 73.90. 📄 Paper/Project: chaos-in-texts.github.io/ 💻 Code: github.com/OpenBMB/Trident #MultimodalRetrieval #MultimodalRAG #RAG #VLM #Embedding #Trident #ChaosInTheText #OpenBMB

🥈 MiniCPM-V 4.7 just climbed to #2 on @huggingface Trending! Thank you to everyone trying the model, sharing feedback, and building with it. Your support keeps us pushing on-device multimodal AI forward. 📱 35B-A3B MoE runs smoothly on iOS and Android with ~3GB memory. The 1B model delivers strong OCR, math, and video understanding performance. Try it！ 🤗 Models: huggingface.co/collections/o… 💻 GitHub: github.com/OpenBMB/MiniCPM-V Let's keep the momentum going! 🚀

Building ideas with building blocks, powered by on-device AI! 🧱📱✨ Love this creative comparison. Thanks for putting MiniCPM-V 4.7 1B to the test! 🚀

Lisbon, here we come! 🇵🇹✈️ We are heading to Web Summit @WebSummit, Nov 9–12! Come say hi at our booth. Going too ? 👀 Reply or DM us 💬 let’s grab a coffee.☕️ Who’s up for a mini meetup? 🙌🏻 #WebSummit2026

Great to see MiniCPM-V 4.7 on ModelScope! 🚀 Thanks for the support 💙 Excited to make powerful on-device multimodal AI accessible to even more developers!

370 tokens/sec with MiniCPM-V 4.7?! 🔥 Love seeing the community push the limits of what's possible with speculative decoding. Huge shoutout to @Tech2Wild for putting this together! 🚀

Big thanks to @inteldevs for the day-0 support ! 🙌 Excited to bring MiniCPM-V 4.7’s multimodal capabilities to Intel CPUs, GPUs, and NPUs.

🚀 PilotDeck 2.0 is here! Meet the open-source AI Agent Harness built to get complex work done. Developed by Tsinghua THUNLP, @modelbest_ai, @OpenBMB , and AI9Stars, PilotDeck is a model-agnostic Agent operating system that turns natural language instructions into completed tasks — with transparent data flows and the freedom to choose your models. What’s new in 2.0? 📄 End-to-end office work: Create, edit, and verify Word, Excel, and PowerPoint files — not just generate drafts. 🔄 More reliable execution: complex, long-running tasks going through interruptions. Stress-tested with 2,000 interaction messages. 🛠️ Easy to get started: One-click desktop installation and a streamlined setup flow to connect your model and create your workspace. 🔓 Open by design: Fully open-source, model-agnostic, with data flows visible to users. On Workbuddy Bench, PilotDeck scored 79.1 with Qwen3.6-35B-A3B, outperforming WorkBuddy (74.7) and Claude Code (77.3). Build your own AI workspace for the Agent era. 🔗 GitHub: github.com/OpenBMB/PilotDeck Web: pilotdeck.openbmb.cn

Thank you @fahdmirza for the informative tutorial and in-depth review. We really appreciate the time and effort you put into testing and sharing your insights! 🙌

🎬 35B MoE multimodal AI. On your phone. With just 3GB of memory. 🤯 This is MiniCPM-V 4.7 in action: reading real-world signs, planning around constraints, parsing complex documents, and reasoning over visual data. Powered by LES (Lookahead Expert Scheduling), the 35B-A3B model brings efficient MoE inference to mobile devices, while the lightweight 1B variant delivers capable multimodal understanding on resource-constrained hardware. 🛠️ With Self-Replay Fine-Tuning (SRFT), developers can customize models for new tasks while maintaining general performance. From iOS to Android, MiniCPM-V 4.7 makes powerful multimodal AI more practical to run locally. Try it 👇 🔗 github.com/OpenBMB/MiniCPM-V 🤗 huggingface.co/collections/o… Self-Replay Fine-Tuning: openbmb.cn/en/blog/self-repl…

Appreciate you sharing this @AdinaYakup ! 🙌 It’s exciting to see the community’s interest in pushing local multimodal AI forward, from phones to edge devices.

🚀 MiniCPM-V 4.7 is now running on Apple Silicon! Thanks to our partnership with the @Nativ_AI, MiniCPM-V 4.7 (1B & 35B-A3B) now has Day 0 support for fully on-device inference on Mac. With Nativ, you can run: 🧠 MiniCPM-V 4.7-35B-A3B — 35B capability with efficient inference ⚡ MiniCPM-V 4.7-1B — lightweight vision model for edge devices 📷 Image + video understanding 🔒 Fully local — your data stays on your device Tested on Apple Silicon with Nativ v0.4.x: 1B: 24K+ prefill tokens/s, 315 tok/s decode 35B-A3B: 4K+ prefill tokens/s, 90 tok/s decode Bringing powerful multimodal models closer to everyday devices. 🍎 Huge thanks to the Nativ team for making this possible! 🔗 Nativ: github.com/Blaizzy/nativ