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README.md
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## Model Sources
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## How to get this model
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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).
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## Citation
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```bash
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}
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```
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## Model Sources
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**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)
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**Paper:**
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- [SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation Models](https://arxiv.org/abs/2410.03750)
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- [Low-Rank Adapters Meet Neural Architecture Search for LLM Compression](https://arxiv.org/abs/2501.16372)
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## How to get this model
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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).
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## Citation
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```bash
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@inproceedings{munoz-etal-2024-sqft,
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title = "{SQFT}: Low-cost Model Adaptation in Low-precision Sparse Foundation Models",
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author = "Munoz, Juan Pablo and
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Yuan, Jinjie and
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Jain, Nilesh",
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editor = "Al-Onaizan, Yaser and
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Bansal, Mohit and
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Chen, Yun-Nung",
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booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
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month = nov,
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year = "2024",
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address = "Miami, Florida, USA",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2024.findings-emnlp.749",
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pages = "12817--12832",
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}
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```
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