Diffing Toolkit: Model Comparison and Analysis Framework

[Work-In-Progress] A research framework for analyzing differences between language models using interpretability techniques. This project enables systematic comparison of base models and their variants (model organisms) through various diffing methodologies.

Note: The toolkit is based on a heavily modified version of the saprmarks/dictionary_learning repository, available at science-of-finetuning/dictionary_learning. Although we may eventually merge these repositories, this is currently not a priority due to significant divergence.

Overview

This framework consists of two main pipelines:

1. Preprocessing Pipeline: Extract and cache activations from pre-existing models
2. Diffing Pipeline: Analyze differences between models using interpretability techniques

The framework is designed to work with pre-existing model pairs (e.g., base models vs. model organisms) rather than training new models.

Installation

1. Clone the repository:

git clone https://github.com/science-of-finetuning/diffing-game
cd diffing-game

2. Install dependencies:

pip install -r requirements.txt

Quick Start

Basic Usage

Run the complete pipeline (preprocessing + diffing) with default settings:

python main.py

Pipeline Modes

Run preprocessing only (extract activations):

python main.py pipeline.mode=preprocessing

Run diffing analysis only (assumes activations already exist):

python main.py pipeline.mode=diffing

Configuration Examples

Analyze specific organism and model combinations:

python main.py organism=caps model=gemma3_1B

Use different diffing methods:

python main.py diffing/method=kl
python main.py diffing/method=normdiff

Multi-run Experiments

Run experiments across multiple configurations:

python main.py --multirun organism=caps,roman_concrete model=gemma3_1B

Run with different diffing methods:

python main.py --multirun diffing/method=kl,normdiff

Interactive Dashboard

The framework includes a Streamlit-based interactive dashboard for visualizing and exploring model diffing results.

Features

• Dynamic Discovery: Automatically detects available models, organisms, and diffing methods
• Real-time Visualization: Interactive plots and visualizations of diffing results
• Model Integration: Direct links to Hugging Face model pages
• Multi-method Support: Compare results across different diffing methodologies
• Interactive Model Testing: Test custom inputs and steering vectors on both base and finetuned models in real-time

Running the Dashboard

Launch the dashboard with:

streamlit run dashboard.py

The dashboard will be available at http://localhost:8501 by default.

You can also pass configuration overwrites to the dashboard:

streamlit run dashboard.py -- model.dtype=float32

Using the Dashboard

1. Select Base Model: Choose from available base models
2. Select Organism: Pick the model organism (finetuned variant)
3. Select Diffing Method: Choose the analysis method to visualize
4. Explore Results: Interact with the generated visualizations

The dashboard requires that you have already run diffing experiments to generate results to visualize.