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!
OpenBMB highlights MiniCPM-V 4.7 running locally for automated reporting
AISummary
OpenBMB thanks @aiwithsally for demonstrating MiniCPM-V 4.7 in an automated report-generation workflow. In the demo, the 35B-A3B model reads charts and tables from structured data and streams a long-form markdown report, running on a local machine without a cloud API. The post links to the MiniCPM-V GitHub repository and Hugging Face collection.
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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
Source: OpenBMB · x.comPublished