Create README.md

README.md

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+---
+language: en
+license: apache-2.0
+library_name: transformers
+---
+
+# SQFT Base Model: sqft-mistral-7b-v0.3-30-base
+
+- Source Model: [mistralai/Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3)
+- Sparse Method: [Wanda](https://github.com/locuslab/wanda)
+- Sparsity: 30%
+- Quantization: No
+
+## Model Sources
+
+- **Repository:** [https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/SQFT](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/SQFT)
+- **Paper:** [SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation Models]()
+
+## How to get this model
+
+Refer to the command in [SQFT/run_command/mistral-7b-v0.3/sparse_quantization.sh#11](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/SQFT/run_command/mistral-7b-v0.3/sparse_quantization.sh#11).
+
+## Citation
+
+```bash
+@article{munoz2024sqft,
+  title = {SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation Models},
+  author={J. Pablo Munoz and Jinjie Yuan and Nilesh Jain},
+  journal={},
+  year={2024}
+}
+```
+
+## Acknowledgement
+
+Thanks to the work Wanda ([paper](https://arxiv.org/abs/2306.11695), [code](https://github.com/locuslab/wanda)), which provides a simple but effective pruning approach.
+
+## License
+
+Apache-2.0