LoRA-Merge: Implementation and Evaluation of Multi-LoRA Module Fusion Techniques

The LoRA-Merge project implements three LoRA module fusion methods: Simple Averaging, TIES, and LoRAHub. It conducts experimental evaluations on tasks like MNLI, FEVER, RTE, and SciTail using the Llama3-8b-chat model, providing practical tools for efficient model adaptation and addressing key issues in merging multiple LoRA adapters.

When large language models are adapted to multiple downstream tasks, traditional full fine-tuning incurs high storage and deployment costs. LoRA enables parameter-efficient fine-tuning via low-rank matrices, but the effective merging of multiple LoRA adapters has become a new problem—this is the target problem that the LoRA-Merge project aims to solve.

1. Simple Averaging

Arithmetically average multiple LoRA parameters. Advantages: simple implementation, no additional data requirements; Limitations: assumes tasks are equally important, cannot handle conflicts.

2. TIES

Resolves parameter conflicts between tasks through parameter importance analysis, conflict detection, and selective fusion, preserving key knowledge. Suitable for scenarios with large task differences.

3. LoRAHub

Introduces task weight learning, adjusts weights in a data-driven manner, and considers task interactions. Suitable for scenarios with many and complex tasks.

Evaluation Framework

Supports merging LoRA models, evaluating performance, and comparing methods via merge_model.py; uses accuracy and task-specific metrics (precision/recall/F1).

Quick Start

• Environment setup: Create a conda environment and install dependencies
• LoRA training: python lora_training.py --task mnli --model meta-llama/Llama-3-8b-chat
• Fusion evaluation: Supports command-line operations for the three methods (Simple Averaging, TIES, LoRAHub).

Although there are no specific values, the expected trends are:

• Zero-shot baseline is the performance lower bound, and individual LoRA is the task-specific upper bound
• Comparison: Simple Averaging approaches average performance in scenarios with strong task relevance; TIES outperforms Simple Averaging; LoRAHub is the best overall but has high computational cost.

1. Multi-task dialogue systems: Merge LoRA adapters for order inquiry, product consultation, and complaint handling
2. Domain adaptation: Build universal adapters across medical, legal, and financial domains
3. Continual learning: Avoid catastrophic forgetting and support incremental fusion of new tasks.

Current Limitations

Performance degrades when the number of tasks is large; relies on positive task correlation; sensitive to hyperparameters.

Improvement Directions

Adaptive rank selection; hierarchical fusion strategy; online incremental fusion; uncertainty quantification.

LoRA-Merge provides a toolset for LoRA fusion from simple to complex. With clear code structure and explicit dependencies, it offers references for deploying multi-task AI systems in resource-constrained environments, facilitating academic research and industrial implementation of LoRA technology.