RadarAI· X：面壁智能 OpenBMB (@OpenBMB)·· 4 小时前AI 评分55
MiniCPM-V 4.7 本地演示：从数据与图表自动生成完整分析报告
MiniCPM-V 4.7 本地演示：数据与图表自动生成完整分析报告
AI 导读
面壁智能 OpenBMB 转发演示，MiniCPM-V 4.7 35B-A3B 可在本机完成自动化报告生成，输入结构化数据与图表，输出含记分卡、分部拆解、利润率驱动与多空观点的长篇 markdown 报告，无需 API 和云端。演示称模型一次读取图表和密集表格并关联数据推导结论，得益于 35B 总参数仅约 3B 激活的 MoE 架构。GitHub 和 Hugging Face 已提供下载。
正文 · 原文
OpenBMB· @OpenBMB · X·2026-10-20 17:26· 1 小时前AI 评分58
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面壁智能 OpenBMB 转发 @aiwithsilly 的演示，用 MiniCPM-V 4.7 35B-A3B 在本机跑自动化报告生成，输入结构化数据与图表，输出含记分卡、分部拆解、利润率驱动与多空观点的长篇 markdown 报告，无需 API 和云端。演示称模型一次读取图表和密集表格并关联数据推导结论，得益于 35B 总参数仅约 3B 激活的 MoE 架构，GitHub 和 Hugging Face 已提供下载。
正文 · 原文
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!
I built an automated report generator that runs entirely on my own machine. Input: structured data + charts. Output: a full long-form report — earnings interpretation, weekly summaries, whatever you need. No API. No cloud. No monthly bill. -> Here's MiniCPM-V 4.7 35B-A3B doing exactly that 👇 It read the charts AND the dense tables in one pass, then streamed out analyst-grade markdown: scorecard, segment breakdown, margin drivers, cash flow, even a bull/bear case. The part that got me: it didn't just OCR numbers. It connected them — input costs → margin pressure → guidance risk. That's the jump from "text extraction" to "actual analysis." Why this works locally: → 35B total params, only ~3B active (MoE) → Efficient visual encoding keeps multi-chart inputs cheap → Structured markdown output — goes straight into your doc pipeline Try it GitHub: http://github.com/OpenBMB/MiniCPM-V Hugging Face: https://huggingface.co/collections/openbmb/minicpm-v-47在 X 查看被引用的帖子
来源：OpenBMB · x.com
来源：RadarAI · aihot.news