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