FIM-LoRA Experiments

Overview

FIM-LoRA is an advanced experiment harness that implements Fisher Information Matrix-guided adaptive LoRA rank allocation for efficient fine-tuning of large language models. This project explores optimal strategies for allocating LoRA ranks based on parameter importance metrics derived from Fisher Information.

Key Features

Fisher Information Matrix Analysis: Leverages Fisher Information to identify the most important parameters for fine-tuning
Adaptive Rank Allocation: Dynamically allocates LoRA ranks based on parameter importance scores
Efficient Fine-tuning: Reduces computational overhead while maintaining model performance
Comprehensive Experiments: Includes extensive benchmarking and ablation studies
Reproducible Research: Well-documented experiment harness with configurable parameters

Technical Implementation

Core Concepts

Fisher Information Matrix: Computes parameter importance using second-order gradient information
Adaptive LoRA: Allocates different LoRA ranks to different layers based on importance scores
Rank Optimization: Balances model capacity and computational efficiency

Experiment Framework

Systematic evaluation across multiple model architectures
Comparison with standard LoRA approaches
Analysis of rank allocation strategies
Performance metrics on downstream tasks

Results

Demonstrates significant computational savings compared to standard LoRA
Maintains or improves model performance on fine-tuning tasks
Provides insights into optimal rank allocation strategies
Comprehensive analysis of trade-offs between efficiency and performance

Code Repository

Explore the implementation on GitHub:

git clone https://github.com/Kernel-ML/fim-lora-experiments.git
cd fim-lora-experiments
pip install -r requirements.txt
jupyter notebook

Future Enhancements

Extend to other parameter-efficient fine-tuning methods
Integration with production LLM deployment pipelines
Real-time rank allocation optimization
Support for multi-task learning scenarios