sunyuhan19981208 / Multi-LoRA-LLM

Multi-LoRA-LLM is a project that aims to enhance the performance of LLM (Language Model with Latent Retrieval and Alignment) by utilizing multiple LoRA (Latent Retrieval and Alignment) models. This README provides instructions on how to use and evaluate the Multi-LoRA-LLM project.

Multi-LoRA-LLM

Multi-LoRA-LLM is a project that aims to enhance the performance of LLM (Language Model with Latent Retrieval and Alignment) by utilizing multiple LoRA (Latent Retrieval and Alignment) models. This README provides instructions on how to use and evaluate the Multi-LoRA-LLM project.

Installation

pip install requirements.txt

Training a LoRA Model

To train a LoRA model, follow these steps:

You can download base model from HuggingFace, RoBERTa-base as an example:

git clone https://huggingface.co/roberta-base

Execute train.sh or train.py in your terminal:

bash train.sh /path/to/base_model

This will initiate the training process for the LoRA model.

Evaluating the Trained LoRA Models

To evaluate the trained LoRA models, use the eval.py script with the following command:

python eval.py --task rte --base_model /path/to/base_model --lora_model /path/to/lora_model

Make sure to replace /path/to/base_model with the path to the base model and /path/to/lora_model with the path to the LoRA model.

Merging Code

The code for merging is provided in merge.py. Use this script to perform the merging process for the relevant components of the Multi-LoRA-LLM project.

Producing Comparison Experiment

The comparison experiment can be generated using the compare_exp.py script. Run the following command:

python compare_exp.py

This will generate the comparison experience for analysis and evaluation.

Table of Experiment

ALPHA	MNLI acc	RTE acc
0.0	0.4599	0.7329
0.1	0.4542	0.7292
0.2	0.4360	0.7184
0.3	0.4105	0.7004
0.4	0.4010	0.6931
0.5	0.4218	0.6606
0.6	0.5081	0.6715
0.7	0.6943	0.6606
0.8	0.8244	0.6282
0.9	0.8662	0.6498
1.0	0.8656	0.6318

Pushing the LoRA Model to Hugging Face Model Hub

To push the LoRA model to the Hugging Face Model Hub, utilize the push_to_hub.py script with the following command:

python push_to_hub.py --task task_name --base_model /path/to/base_model --lora_model /path/to/lora_model --hub_name hub_name

Replace task_name with the relevant task name, /path/to/base_model with the path to the base model, /path/to/lora_model with the path to the LoRA model, and hub_name with the desired name for the Hugging Face Model Hub.

Pre-trained LoRA Models on Hugging Face For your convenience, two pre-trained RoBERTa LoRA models are available on the Hugging Face Model Hub:

• RoBERTa Base MNLI LoRA: https://huggingface.co/yuuhan/roberta-base-mnli-lora

• RoBERTa Base RTE LoRA: https://huggingface.co/yuuhan/roberta-base-rte-lora

Feel free to explore and utilize these models for your tasks.

Contributors

This project was developed by [Your Name] and [Other Contributors]. For any questions or concerns, please reach out to us at [contact information].

License

[Specify the license under which the project is distributed, if applicable.]

Acknowledgments

[If applicable, acknowledge any external resources, libraries, or individuals who contributed to the project.]