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---
language: en
license: apache-2.0
library_name: transformers
---
# SQFT Base Model: sqft-phi-3.5-mini-instruct-base-gptq
- Source Model: [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct)
- Quantization: GPTQ-INT4
## 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)
## 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 quantization method [GPTQ](https://arxiv.org/abs/2210.17323).
## License
Apache-2.0 |