Enabling Efficient On-Device Fine-Tuning of LLMs Using Only Inference Engines

[Paper][Slides]

Step 1: Create Conda Environment and Install Packages

conda create -n PRGE python=3.10
conda activate PRGE
pip install -r requirements.txt 

Step 2: Run expriments

Detailed hyperparameter configurations can be found in the sweeps folder.

An example use of sweep for fine-tuning TinyLlama-1.1B with P-RGE on the glue dataset:

~> wandb sweep sweeps/P-RGE/glue-tinyllama.yaml
wandb: Creating sweep from: sweeps/P-RGE/glue-tinyllama.yaml
wandb: Created sweep with ID: <ID>
wandb: View sweep at: https://wandb.ai/<unique ID>
wandb: Run sweep agent with: wandb agent <unique ID>
~> wandb agent <unique ID>

Step 3: Check android Folder for On-device Expriments

Citation

@misc{gao2024enablingefficientondevicefinetuning,
      title={Enabling Efficient On-Device Fine-Tuning of LLMs Using Only Inference Engines}, 
      author={Lei Gao and Amir Ziashahabi and Yue Niu and Salman Avestimehr and Murali Annavaram},
      year={2024},
      eprint={2409.15520},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2409.15520}, 
}