---
language: en
license: apache-2.0
library_name: transformers
---

# SQFT Fine-tuned Model: sqft-sparsepeft-mistral-7b-v0.3-60-gsm8k-heu

- Base Model: [IntelLabs/sqft-mistral-7b-v0.3-60-base](https://huggingface.co/IntelLabs/sqft-mistral-7b-v0.3-60-base)
- Sparsity: 60%
- 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-v0.3-60-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](https://arxiv.org/abs/2410.03750)

## 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={The 2024 Conference on Empirical Methods in Natural Language Processing (Findings)},
  year={2024}
}
```

## License

Apache-2.0