sqft-mistral-7b-v0.3-50-gptq-math-heu-adapter

SQFT Fine-tuned Model: sqft-mistral-7b-v0.3-50-gptq-math-heu-adapter

• Base Model: IntelLabs/sqft-mistral-7b-v0.3-50-base-gptq
• Sparsity: 50%
• Quantization: INT4 (GPTQ)
• Finetune Method: SQFT
• Finetune data: 10K instruction-following math reasoning training dataset from LLM-Adapters (math_10k.json)
• Sub-Adapter: Heuristic

Evaluation

git clone https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning.git haaml && cd haaml/SQFT

BASE_MODEL_NAME=IntelLabs/sqft-mistral-7b-v0.3-50-base-gptq
ADAPTER_MODEL_NAME=IntelLabs/sqft-mistral-7b-v0.3-50-gptq-math-heu-adapter
OUTPUT_DIR=./results
python eval/evaluate_math.py --base_model_path ${BASE_MODEL_NAME} --adapter_model_path ${ADAPTER_MODEL_NAME} --output_dir ${OUTPUT_DIR}

Refer to our repo for the environment information to run this command.

Model Sources

Repository: https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/SQFT

Paper:

• SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation Models
• Low-Rank Adapters Meet Neural Architecture Search for LLM Compression

Citation

@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",
}

License

Apache-2.0