LoNAS Model Card: lonas-bert-base-glue

The super-networks fine-tuned on BERT-base with GLUE benchmark using LoNAS.

Model Details

Information

• Model name: lonas-bert-base-glue
• Base model: bert-base-uncased
• Subnetwork version: Super-network
• NNCF Configurations: nncf_config/glue

Adapter Configuration

• LoRA rank: 8
• LoRA alpha: 16
• LoRA target modules: query, value

Training and Evaluation

GLUE benchmark

Training Hyperparameters

Task	RTE	MRPC	STS-B	CoLA	SST-2	QNLI	QQP	MNLI
Epoch	80	35	60	80	60	80	60	40
Batch size	32	32	64	64	64	64	64	64
Learning rate	3e-4	5e-4	5e-4	3e-4	3e-4	4e-4	3e-4	4e-4
Max length	128	128	128	128	128	256	128	128

How to use

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

CUDA_VISIBLE_DEVICES=${DEVICES} python run_glue.py \
    --task_name ${TASK} \
    --model_name_or_path bert-base-uncased \
    --do_eval \
    --do_search \
    --per_device_eval_batch_size 64 \
    --max_seq_length ${MAX_LENGTH} \
    --lora \
    --lora_weights lonas-bert-base-glue/lonas-bert-base-${TASK} \
    --nncf_config nncf_config/glue/nncf_lonas_bert_base_${TASK}.json \
    --output_dir lonas-bert-base-glue/lonas-bert-base-${TASK}/results

Evaluation Results

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

Method	Trainable Parameter Ratio	GFLOPs	RTE	MRPC	STS-B	CoLA	SST-2	QNLI	QQP	MNLI	AVG
LoRA	0.27%	11.2	65.85	84.46	88.73	57.58	92.06	90.62	89.41	83.00	81.46
LoNAS	0.27%	8.0	70.76	88.97	88.28	61.12	93.23	91.21	88.55	82.00	83.02

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

Citation

@article{munoz2024lonas,
  title = {LoNAS: Elastic Low-Rank Adapters for Efficient Large Language Models},
  author={J. Pablo Munoz and Jinjie Yuan and Yi Zheng and Nilesh Jain},
  journal={},
  year={2024}
}

License

Apache-2.0