---
license: apache-2.0
base_model: openbmb/MiniCPM-o-2_6
tags:
- vision
- text-generation
- multimodal
- minicpm
- tiny-model
- testing
- optimum-intel
pipeline_tag: text-generation
library_name: transformers
---

# tiny-random-MiniCPM-o-2_6

A minimal, randomly initialized version of MiniCPM-o-2_6 designed for testing and development purposes. This model maintains the same architecture as the original MiniCPM-o-2_6 but with drastically reduced dimensions to create a lightweight test model.

## Model Details

### Model Description

This is a tiny, randomly initialized version of the MiniCPM-o-2_6 multimodal model. It was created by scaling down the original model's dimensions while preserving the architecture structure. The model is intended for:

- Testing and development workflows
- Integration testing with Optimum-Intel
- Quick prototyping and experimentation
- CI/CD pipelines requiring lightweight models

**⚠️ Important:** This model is randomly initialized and should NOT be used for production inference. It is designed solely for testing purposes.

### Model Architecture

The model maintains the same architecture as MiniCPM-o-2_6 but with reduced dimensions:

**Language Model (LLM):**
- `hidden_size`: 40
- `num_hidden_layers`: 1
- `num_attention_heads`: 4
- `num_key_value_heads`: 2
- `intermediate_size`: 16
- `max_position_embeddings`: 128
- `vocab_size`: 151,700

**Vision Component:**
- `hidden_size`: 16
- `num_hidden_layers`: 1
- `num_attention_heads`: 4
- `intermediate_size`: 8
- `patch_size`: 14

**Audio/TTS Components:**
- Audio: Disabled (`init_audio: false`)
- TTS: Disabled (`init_tts: false`)

### Model Size

- **Total Parameters**: ~6.17M
- **Model Size**: ~12.4 MB (on disk)
- **Precision**: bfloat16

## Usage

### Basic Usage

```python
from transformers import AutoModel, AutoTokenizer, AutoProcessor
import torch
from PIL import Image

# Load model and tokenizer
model_id = "notlikejoe/tiny-random-MiniCPM-o-2_6"
model = AutoModel.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

# Prepare inputs
text = "Hello, how are you?"
image = Image.new('RGB', (224, 224), color='red')  # Dummy image

# Process inputs
inputs = processor(text=text, images=image, return_tensors="pt")

# Forward pass
model.eval()
with torch.no_grad():
    outputs = model(**inputs)
```

### With Optimum-Intel

This model is compatible with Optimum-Intel for OpenVINO optimization:

```python
from optimum.intel import OVModelForCausalLM
from transformers import AutoTokenizer

model_id = "notlikejoe/tiny-random-MiniCPM-o-2_6"

# Export to OpenVINO format
ov_model = OVModelForCausalLM.from_pretrained(
    model_id,
    export=True,
    trust_remote_code=True
)

# Use for inference
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
```

## Model Validation

The model has been validated to ensure:

✅ Model loads successfully from Hugging Face  
✅ Config, tokenizer, and processor load correctly  
✅ Model structure matches expected architecture  
✅ Compatible with Optimum-Intel export  
✅ Forward pass completes without errors  
✅ **OpenVINO compatibility fix applied**: Resampler `num_heads=0` issue resolved

### OpenVINO Compatibility Fix

This model includes a fix for the OpenVINO loading issue where `num_heads=0` would occur with small `embed_dim` values. The resampler's `num_heads` calculation has been patched to ensure it's always at least 1:

```python
# Original: num_heads = embed_dim // 128  # Would be 0 when embed_dim=40
# Fixed:    num_heads = 1 if embed_dim < 128 else max(1, embed_dim // 128)
```

The `modeling_minicpmo.py` file included with this model contains this fix, ensuring compatibility with Optimum-Intel OpenVINO export and loading.  

## Limitations

1. **Random Initialization**: This model is randomly initialized and will not produce meaningful outputs
2. **Reduced Dimensions**: The model dimensions are minimal and may not capture complex patterns
3. **Testing Only**: This model is intended for testing and development, not production use
4. **Limited Vocabulary**: The vocabulary has been reduced to 2000 entries for size optimization

## Training Details

This model was not trained. It is a randomly initialized, dimensionally-reduced version of MiniCPM-o-2_6 created for testing purposes.

### Training Data

N/A - Model is randomly initialized.

## Evaluation

This model is not intended for evaluation on standard benchmarks as it is randomly initialized.

## Citation

If you use this model, please cite the original MiniCPM-o-2_6 model:

```bibtex
@misc{minicpm-o-2_6,
  title={MiniCPM-o-2_6},
  author={OpenBMB},
  year={2024},
  howpublished={\url{https://huggingface.co/openbmb/MiniCPM-o-2_6}}
}
```

## Model Card Contact

For questions or issues related to this model, please open an issue in the repository.

## License

This model is licensed under the Apache 2.0 License, same as the base model.