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

Tiny MiniCPM-o-2_6 Model

A minimal, optimized version of MiniCPM-o-2_6 for testing and development purposes.

Model Details

• Model Size: ~54 MB (PyTorch safetensors format)
• Format: PyTorch safetensors (not OpenVINO IR)
• Vocabulary Size: 50,000 tokens (reduced from 151,700)
• Architecture: MiniCPM-o-2_6 with optimized dimensions

Model Configuration

• hidden_size: 128 (reduced from 168)
• intermediate_size: 8 (reduced from 16)
• num_hidden_layers: 2
• num_attention_heads: 2 (reduced from 28)
• query_num: 64

Usage

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

Citation

Based on MiniCPM-o-2_6 from OpenBMB.