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openbmb
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openbmb/MiniCPM-V-4
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MiniCPM-V 4.0 is the latest efficient model in the MiniCPM-V series. The model is built based on SigLIP2-400M and MiniCPM4-3B with a total of 4.1B parameters. It inherits the strong single-image, multi-image and video understanding performance of MiniCPM-V 2.6 with largely improved efficiency. Notable features of MiniCPM-V 4.0 include:
🔥 Leading Visual Capability. With only 4.1B parameters, MiniCPM-V 4.0 achieves an average score of 69.0 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks, outperforming GPT-4.1-mini-20250414, MiniCPM-V 2.6 (8.1B params, OpenCompass 65.2) and Qwen2.5-VL-3B-Instruct (3.8B params, OpenCompass 64.5). It also shows good performance in multi-image understanding and video understanding.
🚀 Superior Efficiency. Designed for on-device deployment, MiniCPM-V 4.0 runs smoothly on end devices. For example, it devlivers less than 2s first token delay and more than 17 token/s decoding on iPhone 16 Pro Max, without heating problems. It also shows superior throughput under concurrent requests.
💫 Easy Usage. MiniCPM-V 4.0 can be easily used in various ways including llama.cpp, Ollama, vLLM, SGLang, LLaMA-Factory and local web demo etc. We also open-source iOS App that can run on iPhone and iPad. Get started easily with our well-structured Cookbook, featuring detailed instructions and practical examples.
Run locally on iPhone 16 Pro Max with iOS demo.
python
from PIL import Imageimport torchfrom transformers import AutoModel, AutoTokenizer model_path = 'openbmb/MiniCPM-V-4'model = AutoModel.from_pretrained(model_path, trust_remote_code=True, # sdpa or flash_attention_2, no eager attn_implementation='sdpa', torch_dtype=torch.bfloat16)model = model.eval().cuda()tokenizer = AutoTokenizer.from_pretrained( model_path, trust_remote_code=True) image = Image.open('./assets/single.png').convert('RGB') # First round chat question = "What is the landform in the picture?"msgs = [{'role': 'user', 'content': [image, question]}] answer = model.chat( msgs=msgs, image=image, tokenizer=tokenizer)print(answer) # Second round chat, pass history context of multi-turn conversationmsgs.append({"role": "assistant", "content": [answer]})msgs.append({"role": "user", "content": [ "What should I pay attention to when traveling here?"]}) answer = model.chat( msgs=msgs, image=None, tokenizer=tokenizer)print(answer)👏 Welcome to explore key techniques of MiniCPM-V 2.6 and other multimodal projects of our team:
VisCPM | RLHF-V | LLaVA-UHD | RLAIF-V
If you find our work helpful, please consider citing our papers 📝 and liking this project ❤️！
bib
@article{yao2024minicpm, title={MiniCPM-V: A GPT-4V Level MLLM on Your Phone}, author={Yao, Yuan and Yu, Tianyu and Zhang, Ao and Wang, Chongyi and Cui, Junbo and Zhu, Hongji and Cai, Tianchi and Li, Haoyu and Zhao, Weilin and He, Zhihui and others}, journal={Nat Commun 16, 5509 (2025)}, year={2025}}from PIL import Imageimport torchfrom transformers import AutoModel, AutoTokenizer model_path = 'openbmb/MiniCPM-V-4'model = AutoModel.from_pretrained(model_path, trust_remote_code=True, # sdpa or flash_attention_2, no eager attn_implementation='sdpa', torch_dtype=torch.bfloat16)model = model.eval().cuda()tokenizer = AutoTokenizer.from_pretrained( model_path, trust_remote_code=True) image = Image.open('./assets/single.png').convert('RGB') # First round chat question = "What is the landform in the picture?"msgs = [{'role': 'user', 'content': [image, question]}] answer = model.chat( msgs=msgs, image=image, tokenizer=tokenizer)print(answer) # Second round chat, pass history context of multi-turn conversationmsgs.append({"role": "assistant", "content": [answer]})msgs.append({"role": "user", "content": [ "What should I pay attention to when traveling here?"]}) answer = model.chat( msgs=msgs, image=None, tokenizer=tokenizer)print(answer)@article{yao2024minicpm, title={MiniCPM-V: A GPT-4V Level MLLM on Your Phone}, author={Yao, Yuan and Yu, Tianyu and Zhang, Ao and Wang, Chongyi and Cui, Junbo and Zhu, Hongji and Cai, Tianchi and Li, Haoyu and Zhao, Weilin and He, Zhihui and others}, journal={Nat Commun 16, 5509 (2025)}, year={2025}}