---
language: en
license: apache-2.0
datasets:
- nyu-mll/glue
---

# LoNAS Model Card: lonas-bert-base-glue

The super-networks fine-tuned on BERT-base with [GLUE benchmark](https://gluebenchmark.com/) using LoNAS.

## Model Details

### Information

- **Model name:** lonas-bert-base-glue
- **Base model:** [bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased)
- **Subnetwork version:** Super-network
- **NNCF Configurations:** [nncf_config/glue](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/LoNAS/nncf_config/glue)

### Adapter Configuration

- **LoRA rank:** 8
- **LoRA alpha:** 16
- **LoRA target modules:** query, value


### Training and Evaluation

[GLUE benchmark](https://gluebenchmark.com/)

### 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](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/LoNAS/running_commands):
```bash
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](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/LoNAS)
- **Paper:** [LoNAS: Elastic Low-Rank Adapters for Efficient Large Language Models]()

## Citation

```bibtex
@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