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---
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library_name: transformers
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license: apache-2.0
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tags:
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- vision
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- multimodal
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- tiny-model
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- minicpm
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pipeline_tag: image-to-text
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---
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# Tiny MiniCPM-o-2_6 Model
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A minimal, optimized version of MiniCPM-o-2_6 for testing and development purposes.
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## Model Details
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- **Model Size**: ~54 MB (PyTorch safetensors format)
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- **Format**: PyTorch safetensors (not OpenVINO IR)
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- **Vocabulary Size**: 50,000 tokens (reduced from 151,700)
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- **Architecture**: MiniCPM-o-2_6 with optimized dimensions
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## Model Configuration
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- **hidden_size**: 128 (reduced from 168)
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- **intermediate_size**: 8 (reduced from 16)
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- **num_hidden_layers**: 2
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- **num_attention_heads**: 2 (reduced from 28)
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- **query_num**: 64
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## Usage
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```python
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from transformers import AutoProcessor, AutoModelForCausalLM
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from PIL import Image
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# Load processor and model
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processor = AutoProcessor.from_pretrained("M-Ziyo/tiny-random-MiniCPM-o-2_6-mini", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("M-Ziyo/tiny-random-MiniCPM-o-2_6-mini", trust_remote_code=True)
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# Prepare inputs
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prompt = "<|im_start|>user\n(<image>./</image>)\nWhat is in the image?<|im_end|>\n<|im_start|>assistant\n"
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image = Image.open("your_image.jpg")
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inputs = processor([prompt], [image], return_tensors="pt")
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# Generate
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result = model.generate(**inputs, max_new_tokens=50)
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decoded = processor.tokenizer.batch_decode(result[:, inputs["input_ids"].shape[1]:])
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print(decoded)
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```
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## Model Features
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- ✅ **PyTorch format** with safetensors (not OpenVINO IR)
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- ✅ **Optimized size** (~54 MB vs original)
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- ✅ **Weight copying** from original model for better output quality
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- ✅ **Diverse output** (not just repetitive characters)
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## Notes
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- This is a minimal test model for development purposes
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- Model weights are copied from the original model for better initialization
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- Designed for testing Optimum-Intel integration
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## Citation
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Based on MiniCPM-o-2_6 from OpenBMB.
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