AI Tools.

Search

image text to text by openbmb

MiniCPM-V-4.6

MiniCPM-V-4.6 is OpenBMB's MiniCPM-V 4.6, a lightweight on-device multimodal model optimized for image+text tasks at minimal parameter count. Version 4.6 targets improved document OCR, mathematical diagram understanding, and multilingual captioning within the constraints of mobile or edge deployment. It is compatible with deployment via llama.cpp or the MiniCPM-specific inference stack.

Summary text generated by an automated pipeline from the model card · Not individually reviewed or run by us · How this page is made

From the model card

Fields below are copied from the tags and counters on the HuggingFace repository openbmb/MiniCPM-V-4.6 at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.

Publisher (HF namespace)
openbmb
Pipeline tag
image-text-to-text
Library
Transformers
Weight formats
safetensors
License tag
apache-2.0 — read the license file in the repo before relying on it
Papers cited
arXiv:2604.27393, arXiv:2509.18154, arXiv:2408.01800, arXiv:2605.08985
Downloads (HF counter at last fetch)
523,553
Likes (HF counter at last fetch)
1,206
Model card
https://huggingface.co/openbmb/MiniCPM-V-4.6

Use cases

  • On-device document understanding and OCR on mobile hardware
  • Mathematical diagram interpretation in educational apps
  • Multilingual image captioning without cloud dependency
  • Lightweight VQA in offline-first applications
  • Fine-tuning for custom on-device multimodal tasks

Pros

  • Apache-2.0 licensed for commercial use
  • Optimized for on-device inference with low VRAM requirements
  • Improved over earlier MiniCPM-V versions on OCR and math diagram tasks
  • Actively maintained by OpenBMB with clear versioning

Cons

  • On-device scale means quality gap vs 7B+ VLMs on complex image reasoning
  • MiniCPM-specific architecture may require custom inference code outside standard Transformers
  • OCR quality degrades on poor-quality scans or small font sizes
  • Limited community integrations compared to Qwen-VL or InternVL families

Tags

transformerssafetensorsminicpmv4_6image-text-to-textminicpm-vmultimodalOn-Device Modellightweightconversationalarxiv:2604.27393arxiv:2509.18154arxiv:2408.01800arxiv:2605.08985license:apache-2.0endpoints_compatibleregion:us