from transformers import AutoProcessor, AutoModelForCausalLM
from PIL import Image

# Load processor and model
processor = AutoProcessor.from_pretrained("M-Ziyo/tiny-random-MiniCPM-o-2_6-mini", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("M-Ziyo/tiny-random-MiniCPM-o-2_6-mini", trust_remote_code=True)

# Prepare inputs
prompt = "<|im_start|>user\n(<image>./</image>)\nWhat is in the image?<|im_end|>\n<|im_start|>assistant\n"
image = Image.open("your_image.jpg")

inputs = processor([prompt], [image], return_tensors="pt")

# Generate
result = model.generate(**inputs, max_new_tokens=50)
decoded = processor.tokenizer.batch_decode(result[:, inputs["input_ids"].shape[1]:])
print(decoded)

Model Features

✅ PyTorch format with safetensors (not OpenVINO IR)
✅ Optimized size (~54 MB vs original)
✅ Weight copying from original model for better output quality
✅ Diverse output (not just repetitive characters)

Notes

This is a minimal test model for development purposes
Model weights are copied from the original model for better initialization
Designed for testing Optimum-Intel integration

M-Ziyo/tiny-random-MiniCPM-o-2_6-mini

Tiny MiniCPM-o-2_6 Model

Model Details

Model Configuration

Usage

Model Features

Notes

Citation

M-Ziyo/
tiny-random-MiniCPM-o-2_6-mini