OpenBMB 的 MiniCPM-V 4.7 1B 已经能在普通 iPhone 上离线识别小票、传单和截图,做端侧 AI 的开发者可以看看这个实际案例。
#OpenBMB
OpenBMB 把自家 MiniCPM-V 4.7 接进了 ghfind,输入 GitHub 用户名就能查账号是真实贡献还是刷出来的,还能顺便被毒舌点评一下,代码也开源了。
OpenBMB 把 35B 多模态模型塞进 iPhone,每秒 20+ token,比基线快 6 倍,代码开源可以直接上手玩。
有人拿 MiniCPM-V 4.7 在本地搭了个自动报告流水线,从数据到图表到成稿一条龙,不依赖云端,适合想自己复刻的看看。
OpenBMB 的 MiniCPM-V 4.7 手机上就能跑多模态模型,100 tok/s 加 3 GB 内存,llama.cpp 一装就能试。
OpenBMB 的 MiniCPM-V 4.7 进了 vLLM v0.32.0,自己搭服务的可以直接用 vLLM 跑它了,吞吐延迟都更好。
Even small 2B-scale retrievers can double mixed-corpus retrieval quality once modality bias is addressed in training, which matters for anyone building RAG over documents with screenshots, tables or charts. The paper, code and project page are open, so teams can adopt the Multi-Positive View InfoNCE recipe or swap Trident-Qwen3VL-2B into their existing embedding stack.
OpenBMB 的 MiniCPM-V 4.7 冲上 Hugging Face 热榜第二,35B-A3B MoE 手机端 3GB 内存就能跑,想玩端侧多模态的可以试试。
OpenBMB 官方转发的端侧实测,1B 参数的 MiniCPM-V 4.7 直接跑在手机上,想玩本地部署的可以看看这个演示。
清华和面壁开源了个智能体操作系统 PilotDeck 2.0,记忆能直接看能改,简单任务自动交给小模型跑,长任务开销省了 83%。
An open-source agent harness beating Claude Code on a named benchmark challenges the assumption that agent platforms must lock users into proprietary models. Developers running local or Chinese open-weight models like Qwen get a transparent alternative with verifiable file outputs rather than drafts. Availability is immediate via GitHub and desktop installer, with no subscription cost.
The core shift is system-level: LES moves the bottleneck from memory capacity to flash bandwidth and expert-prediction hit rate, letting a 35B-class model live on consumer phones without a cloud round-trip. Developers building on-device apps can fine-tune with SRFT's 4:1 task-to-replay data mix while keeping general abilities largely intact. Weights and code are on Hugging Face and GitHub, so teams can experiment today.
OpenBMB 把 35B 的多模态模型塞进了 3GB 内存,手机能离线读文档、认路牌,还给了 1B 小版本和微调方法,端侧玩法直接变多了。
OpenBMB 放出了 MiniCPM-V 4.7,3B 激活参数就能在 iPhone 上跑到 13 tokens/s,性能还能压过参数多 10 倍的模型,本地跑多模态的朋友可以试试。
MiniCPM-V-4.7-35B-A3B 出了 20GB 量化版,性能只掉 5% 左右,消费级显卡就能本地跑视觉模型了。
OpenBMB 的 MiniCPM-V-4.7(35B 总参、3B 激活)跑进了真实 agent 工作流,重点考验工具调用和长上下文,关注高效模型跑 agent 的可以看看。
OpenBMB 的 MiniCPM-V 4.7 35B-A3B 被第三方去掉了审查,拒绝率从 96.3% 降到 0,Hugging Face 上能直接下载本地跑。
OpenBMB 的 MiniCPM-V 4.7 在 3D 生成上赢了 Qwen 3.6 35B-3A,还省 10% token,本地跑视觉模型可以关注一下。
OpenBMB 的 MiniCPM-V 4.7 能在 iPhone 上跑 35B 多模态模型,3GB 内存 13 tokens/s,OCR 还压过 Qwen3.6,端侧玩家可以试试。
A 1.61 GB weight file that still hits ~70 tokens/s on a Tesla T4 shows how far small open models can be squeezed onto aging or low-power GPUs, no datacenter needed. Practitioners running local inference on modest hardware can pair it with ExLlamaV3 or TabbyAPI to serve on-device chat or agent backends. Compared with running the full-precision MiniCPM5-2B, this trade-off suits edge and consumer setups over maximum accuracy.
MiniCPM5-2B offers superior performance for edge deployment with its 2B parameters, outperforming larger models while being accessible for developers in the region.
有人把 Claude Fable 5 的思维链塞进了 MiniCPM5-1B,搞出个 657MB 的本地推理模型,128K 上下文还能看它怎么想的,部署门槛极低。