Create README.md

README.md

@@ -0,0 +1,43 @@
+---
+language: en
+license: apache-2.0
+library_name: transformers
+---
+
+# SQFT Fine-tuned Model: sqft-sparsepeft-mistral-7b-v0.3-40-gsm8k-heu
+
+- Base Model: [IntelLabs/sqft-mistral-7b-v0.3-40-base](https://huggingface.co/IntelLabs/sqft-mistral-7b-v0.3-40-base)
+- Sparsity: 40%
+- Quantization: No
+- Finetune Method: SQFT + SparsePEFT
+- Finetune data: [GSM8K](https://huggingface.co/datasets/openai/gsm8k)
+- Sub-Adapter: Heuristic
+
+### Evaluation
+
+```bash
+MODEL_NAME=IntelLabs/sqft-sparsepeft-mistral-7b-v0.3-40-gsm8k-heu
+lm_eval --model hf --model_args pretrained=${MODEL_NAME},add_bos_token=True,trust_remote_code=True --tasks gsm8k --batch_size auto:4
+```
+
+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