初始化项目，由ModelHub XC社区提供模型

Model: IntelLabs/sqft-mistral-7b-v0.3-50-base
Source: Original Platform

README.md

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+---
+language: en
+license: apache-2.0
+library_name: transformers
+---
+
+# SQFT Base Model: sqft-mistral-7b-v0.3-50-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: 50%
+- 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](https://arxiv.org/abs/2410.03750)
+- [Low-Rank Adapters Meet Neural Architecture Search for LLM Compression](https://arxiv.org/abs/2501.16372)
+
+## 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/legacy/run_command/mistral-7b-v0.3/sparse_quantization.sh#11).
+
+## Citation
+
+```bash
+@inproceedings{munoz-etal-2024-sqft,
+    title = "{SQFT}: Low-cost Model Adaptation in Low-precision Sparse Foundation Models",
+    author = "Munoz, Juan Pablo  and
+      Yuan, Jinjie  and
+      Jain, Nilesh",
+    editor = "Al-Onaizan, Yaser  and
+      Bansal, Mohit  and
+      Chen, Yun-Nung",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
+    month = nov,
+    year = "2024",
+    address = "Miami, Florida, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.findings-emnlp.749",
+    pages = "12817--12832",
+}
+```
+
+## 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