Create README.md

README.md

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+---
+language: en
+license: apache-2.0
+library_name: transformers
+---
+
+# SQFT Fine-tuned Model: sqft-sparsepeft-phi-3-mini-4k-30-math-heu
+
+- Base Model: [IntelLabs/sqft-phi-3-mini-4k-30-base](https://huggingface.co/IntelLabs/sqft-phi-3-mini-4k-30-base)
+- Sparsity: 30%
+- Quantization: No
+- Finetune Method: SQFT + SparsePEFT
+- Finetune data: 10K instruction-following math reasoning training dataset from [LLM-Adapters](https://github.com/AGI-Edgerunners/LLM-Adapters) ([math_10k.json](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/ft-training_set/math_10k.json))
+- Sub-Adapter: Heuristic
+
+### Evaluation
+
+```bash
+git clone https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning.git haaml && cd haaml/SQFT
+
+MODEL_NAME=IntelLabs/sqft-sparsepeft-phi-3-mini-4k-30-math-heu
+OUTPUT_DIR=./results
+python eval/evaluate_math.py --base_model_path ${MODEL_NAME} --output_dir ${OUTPUT_DIR}
+```
+
+Refer to our [repo](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/SQFT) for the environment information to run this command.
+
+## 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]()
+
+## 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}
+}
+```
+
+## License
+
+Apache-2.0