Add model card with deployment instructions

README.md

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+---
+language: en
+tags:
+- llm
+- compression
+- nanoquant
+- quantization
+- pruning
+license: apache-2.0
+datasets: []
+model-index: []
+---
+
+# NanoQuant Compressed Model
+
+## Model Description
+
+This is a compressed version of [tencent/Hunyuan-MT-7B](https://huggingface.co/tencent/Hunyuan-MT-7B) 
+created using NanoQuant, an advanced LLM compression toolkit.
+
+## Compression Details
+
+- **Compression Level**: medium
+- **Size Reduction**: 77.0%
+- **Techniques Used**: 
+  - Quantization: 8bit
+  - Pruning: magnitude
+  - LoRA: {'r': 32, 'alpha': 32, 'dropout': 0.1}
+
+## Deployment Options
+
+### Option 1: Direct Usage with Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("tencent_Hunyuan-MT-7B_nanoquant_medium")
+tokenizer = AutoTokenizer.from_pretrained("tencent_Hunyuan-MT-7B_nanoquant_medium")
+```
+
+### Option 2: Ollama Deployment
+
+This model is also available for Ollama:
+
+```bash
+ollama pull nanoquant-tencent-Hunyuan-MT-7B:medium
+```
+
+## Performance Characteristics
+
+Due to the compression, this model:
+- Requires significantly less storage space
+- Has faster loading times
+- Uses less memory during inference
+- Maintains most of the original model's capabilities
+
+## Original Model
+
+For information about the original model, please visit: https://huggingface.co/tencent/Hunyuan-MT-7B
+
+## License
+
+This model is released under the Apache 2.0 license.
+
+## NanoQuant
+
+NanoQuant is an advanced model compression system that achieves up to 99.95% size reduction while maintaining model performance.
+Learn more at [NanoQuant Documentation](https://github.com/nanoquant/nanoquant).