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@@ -13,21 +13,34 @@ library_name: transformers
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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:** [SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation Models](https://arxiv.org/abs/2410.03750)
 
 
 
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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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- @article{munoz2024sqft,
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- title = {SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation Models},
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- author={J. Pablo Munoz and Jinjie Yuan and Nilesh Jain},
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- journal={The 2024 Conference on Empirical Methods in Natural Language Processing (Findings)},
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- year={2024}
 
 
 
 
 
 
 
 
 
 
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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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+
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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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