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1CUDA_VISIBLE_DEVICES=${DEVICES} python run_math.py \
2 --dataset_path None \
3 --model_name_or_path bigscience/bloomz-7b1 \
4 --lora \
5 --lora_weights lonas-bloomz-7b-math \
6 --nncf_config nncf_config/unified_math/nncf_lonas_bloomz_7b.json \
7 --do_test \
8 --output_dir lonas-bloomz-7b-math/results| Method | Total Params. | TFLOPs | GSM8K | AQuA | MAWPS | SVAMP | Average |
|---|---|---|---|---|---|---|---|
| LoRA | 7.1B | 1.8 | 17.4 | 21.3 | 70.2 | 41.0 | 37.5 |
| LoNAS | 6.1B | 1.5 | 18.6 | 22.0 | 76.5 | 31.8 | 37.2 |
1@inproceedings{munoz-etal-2024-lonas,
2 title = "{L}o{NAS}: Elastic Low-Rank Adapters for Efficient Large Language Models",
3 author = "Munoz, Juan Pablo and
4 Yuan, Jinjie and
5 Zheng, Yi and
6 Jain, Nilesh",
7 editor = "Calzolari, Nicoletta and
8 Kan, Min-Yen and
9 Hoste, Veronique and
10 Lenci, Alessandro and
11 Sakti, Sakriani and
12 Xue, Nianwen",
13 booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
14 month = may,
15 year = "2024",
16 address = "Torino, Italia",
17 publisher = "ELRA and ICCL",
18 url = "https://aclanthology.org/2024.lrec-main.940",
19 pages = "10760--10776",
20}