ArchiveStudio/MiniCPM-V-4_5

image-text-to-text model by ArchiveStudio

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A GPT-4o Level MLLM for Single Image, Multi Image and High-FPS Video Understanding on Your Phone GitHub | MiniCPM Wiki(Chinese) | CookBook | Technical Report | Demo MiniCPM-V 4.5 is the latest and most capable model in the MiniCPM-V series. The model is built on Qwen3-8B and SigLIP2-400M with a total of 8B parameters. It exhibits a significant performance improvement over previous MiniCPM-V and MiniCPM-o models, and introduces new useful features. Notable features of MiniCPM-V 4.5 include: MiniCPM-V 4.5 achieves an average score of 77.0 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks. With only 8B parameters, it surpasses widely used proprietary models like GPT-4o-latest, Gemini-2.0 Pro, and strong open-source models like Qwen2.5-VL 72B for vision-language capabilities, making it the most performant MLLM under 30B parameters. Based on LLaVA-UHD architecture, MiniCPM-V 4.5 can process high-resolution images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344), using…

ArchiveStudio/MiniCPM-V-4_5 on Hugging Face A short preview, not the full document.

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  1. MiniCPM-V 4.5
  2. Key Techniques
  3. Evaluation
  4. Inference Efficiency
  5. Examples
  6. Framework Support Matrix
  7. Usage
  8. Chat with Image
  9. Chat with Video
  10. Chat with multiple images
  11. In-context few-shot learning
  12. License

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Model overview

ArchiveStudio/MiniCPM-V-4_5 is a image-text-to-text model repository published by ArchiveStudio on Hugging Face. The source reports the transformers library.

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Model facts

Task
image-text-to-text
Library
transformers
Recent downloads (30 days)
0
Cumulative likes
0
Hugging Face trending score
0
Reported safetensors parameters
8,695,895,280
Architecture
MiniCPMV
License
apache-2.0
Access
Not gated by Hugging Face
Created
2026-10-30T06:31:41.000Z
Last modified
2026-10-30T06:31:42.000Z

Compatibility and lineage

Reported languages: multilingual

Base-model lineage was not included.

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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Source: Hugging Face Hub. Metadata observed 2026-10-30T06:49:24.903Z. Daily imports are snapshots, not real-time monitoring.

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