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README.md

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+---
+library_name: transformers
+license: apache-2.0
+tags:
+- vision
+- multimodal
+- tiny-model
+- minicpm
+pipeline_tag: image-to-text
+---
+
+# 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
+
+```python
+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.
+