README.md

---
library_name: openvino
tags:
- openvino
- int4
- quantized
- tiny-model
- minicpm
- visual-language-model
license: apache-2.0
---

# Tiny MiniCPM-o-2_6 Model (6MB INT4 Quantized)

This is a tiny random version of the MiniCPM-o-2_6 model, optimized for testing purposes.

## Model Details

- **Model Size:** ~6-7MB (INT4 quantized)
- **Original Model:** MiniCPM-o-2_6
- **Quantization:** INT4 pipeline quantization
- **Vocabulary Size:** 50,000 tokens (reduced from 151,700)
- **Format:** OpenVINO IR

## Model Architecture

- Maintains MiniCPMO class compatibility
- Full INT4 quantization for all components:
  - Language model
  - Vision embeddings
  - Resampler

## Usage

### With Optimum-Intel

```python
from optimum.intel import OVModelForVisualCausalLM
from transformers import AutoProcessor

model = OVModelForVisualCausalLM.from_pretrained(
    "M-Ziyo/tiny-random-MiniCPM-o-2_6-6mb",
    export=False,  # Already quantized
    trust_remote_code=True
)

processor = AutoProcessor.from_pretrained(
    "optimum-intel-internal-testing/tiny-random-MiniCPM-o-2_6",
    trust_remote_code=True
)
```

### Validation

```bash
python validate_tiny_minicpm.py --model-path M-Ziyo/tiny-random-MiniCPM-o-2_6-6mb
```

## Files

- OpenVINO IR files (`.xml`, `.bin`) for all model components
- Configuration files (`config.json`, `openvino_config.json`)
- Python model files for custom architecture
- Processor files

## Notes

- This is a test model with random weights
- Processor should be loaded from the original model: `optimum-intel-internal-testing/tiny-random-MiniCPM-o-2_6`
- Compatible with Optimum-Intel test suite