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lonas-bert-base-glue
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---
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](https://aclanthology.org/2024.lrec-main.940)
- [Low-Rank Adapters Meet Neural Architecture Search for LLM Compression](https://arxiv.org/abs/2501.16372)

## Citation

```bibtex
@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