MiniCPM-V 4
MiniCPM-V Multimodal Series
MiniCPM-V 4 is a 4.1B-parameter multimodal model built on a SigLIP2-400M + MiniCPM4-3B architecture.
Key Features
- OpenCompass average score of 69.0, surpassing GPT-4.1-mini and MiniCPM-V 2.6 (8.1B)
- Efficient on-device: first-token latency <2s and generation speed >17 tokens/s on iPhone 16 Pro Max
- Outperforms Qwen2.5-VL-3B, which has a comparable parameter count
Model Download
Quick Start
Python
import torch
from transformers import AutoModel, AutoTokenizer
from PIL import Image
model = AutoModel.from_pretrained(
"openbmb/MiniCPM-V-4",
trust_remote_code=True,
torch_dtype=torch.bfloat16
).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained("openbmb/MiniCPM-V-4", trust_remote_code=True)
image = Image.open("image.jpg").convert("RGB")
msgs = [{"role": "user", "content": [image, "描述图片内容。"]}]
answer = model.chat(msgs=msgs, tokenizer=tokenizer)
print(answer)
Fine-tuning
Bash
cd MiniCPM-V
MODEL="openbmb/MiniCPM-V-4"
LLM_TYPE="qwen"
sh finetune/finetune_lora.sh
Related Resources
| Topic | Link |
|---|---|
| Fine-tuning | MiniCPM-V 4 fine-tuning guide |
| Code repository | OpenBMB/MiniCPM-V |
| Official model documentation | minicpm_v4_en.md |
| On-device apps and downloads | MiniCPM-V-Apps downloads |