lonas-bloomz-7b-math

LoNAS Model Card: lonas-bloomz-7b-math

The super-network fine-tuned on BLOOMZ-7B with some math reasoning datasets using LoNAS.

Model Details

Information

• Model name: lonas-bloomz-7b-math
• Base model: BLOOMZ-7b
• Domain: Math
• Subnetwork version: Super-network
• NNCF Configuration: nncf_lonas_bloomz_7b.json

Adapter Configuration

• LoRA rank: 32
• LoRA alpha: 64
• LoRA target modules: query_key_value, dense_h_to_4h, dense_4h_to_h

Training Hyperparameters

• Batch size: 16
• Learning rate: 3e-4
• Epoch: 8

Training Data

Unified math reasoning dataset: math_10k.json (collected with the training sets of GSM8K, MAWPS, and AQuA).

Evaluation Data

GSM8K, AQuA, MAWPS and SVAMP

How to use

Refer to https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/LoNAS#evaluation:

CUDA_VISIBLE_DEVICES=${DEVICES} python run_math.py \
    --dataset_path None \
    --model_name_or_path bigscience/bloomz-7b1 \
    --lora \
    --lora_weights lonas-bloomz-7b-math \
    --nncf_config nncf_config/unified_math/nncf_lonas_bloomz_7b.json \
    --do_test \
    --output_dir lonas-bloomz-7b-math/results

Evaluation Results

Results of the heuristic sub-network discoverd from the super-network:

Model Sources

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

Paper:

• LoNAS: Elastic Low-Rank Adapters for Efficient Large Language Models
• Low-Rank Adapters Meet Neural Architecture Search for LLM Compression

Citation

@inproceedings{munoz-etal-2024-lonas,
    title = "{L}o{NAS}: Elastic Low-Rank Adapters for Efficient Large Language Models",
    author = "Munoz, Juan Pablo  and
      Yuan, Jinjie  and
      Zheng, Yi  and
      Jain, Nilesh",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.940",
    pages = "10760--10776",
}

License

Apache-2.0