Create README.md

Signed-off-by: jinjieyuan <[email protected]>

README.md

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+---
+language: en
+license: apache-2.0
+---
+
+# 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_${TASK}.json \
+    --do_test \
+    --output_dir lonas-bert-base-glue/lonas-bert-base-${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