gabliteration 0.1.0

Automated Gabliteration

Automated Gabliteration Optimizer

Automated hyperparameter search for optimal Gabliteration configurations. Paper: Gabliteration: Adaptive Multi-Directional Neural Weight Modification Author: Gökdeniz Gülmez (2025)

Overview

This script automates the process of finding optimal Gabliteration parameters by:

1. Automatically loading datasets from HuggingFace (mlabonne/harmful_behaviors and mlabonne/harmless_alpaca)
2. Testing multiple random parameter configurations
3. Evaluating each configuration's effectiveness (refusal rate reduction)
4. Measuring model similarity to original (KL divergence)
5. Ranking configurations by combined score
6. Allowing you to select and save the best version

Quick Start

1. Installation

pip install gabliteration

This will install all dependencies and make the gabliterate.automate command available system-wide.

The tool automatically downloads these datasets from HuggingFace:

• mlabonne/harmful_behaviors - Harmful prompts for training
• mlabonne/harmless_alpaca - Harmless prompts for comparison

No local files needed!

2. Run with CLI Arguments

Test your favorite model with:

# Basic usage
gabliterate.automate --model "Nanbeige/Nanbeige4-3B-Thinking-2511"

# With custom parameters
gabliterate.automate --model "meta-llama/Llama-3.2-1B-Instruct" --num-versions 50 --batch-size 4

# Full options
gabliterate.automate --model "Qwen/Qwen3-4B-Instruct-2507" \
  --num-versions 100 \
  --test-samples 200 \
  --max-tokens 150 \
  --batch-size 4 \
  --kl-samples 15

CLI Options:

• --model, -m (required): Hugging Face model name or path
• --num-versions, -n: Number of configurations to test (default: 100)
• --test-samples, -t: Test samples for refusal evaluation (default: 100)
• --max-tokens: Max tokens to generate during evaluation (default: 100)
• --batch-size, -b: Batch size for evaluation (default: 2)
• --kl-samples: KL divergence samples (default: 10)

Run gabliterate.automate --help to see all options.

3. Review and Select

The script will:

• Test each configuration
• Print real-time results:

  Testing Version 5/10
  Config: Samples: 100, Skip: [2, 1], Layer: 0.52, Scale: 0.65, λ: 0.10, k: 2, Adaptive: True, β: 0.45
  KL Divergence: 0.0234
  Refusal Rate: 12.0% (12/100)
  Score: 1.2234
  

After all tests, you'll see:

TOP 10 BEST CONFIGURATIONS
Rank   Refusal    KL Div     Score      Config
----------------------------------------------------------------------
1      8.0%       0.0189     0.8189     Samples: 150, Skip: [2, 1], ...
2      12.0%      0.0234     1.2234     Samples: 100, Skip: [1, 2], ...
...

4. Automatic Model Saving

After all tests complete, the script automatically:

• Selects the best configuration (lowest combined score)
• Recreates and saves the gabliterated model
• Saves all configuration details in gabliteration_config.json
• Generates a model-specific README.md

Output Structure

Qwen_Qwen3-4B-Instruct-2507-gabliterated-v1-20250102_143022/
├── config.json                      # Model config
├── model.safetensors               # Model weights
├── tokenizer.json                  # Tokenizer
├── tokenizer_config.json           # Tokenizer config
└── gabliteration_config.json       # ⭐ Gabliteration parameters & results

Configuration File Format

The gabliteration_config.json contains:

{
  "model_name": "Qwen/Qwen3-4B-Instruct-2507",
  "version_id": 1,
  "timestamp": "20250102_143022",
  "gabliteration_config": {
    "num_prompt_samples": 150,
    "skip_begin_layers": 2,
    "skip_end_layers": 1,
    "layer_fraction": 0.52,
    "base_scale_factor": 0.65,
    "regularization": 0.1,
    "n_directions": 2,
    "adaptive_layer_scale": true,
    "beta": 0.5
  },
  "results": {
    "kl_divergence": 0.0189,
    "refusal_rate": 0.08,
    "score": 0.8189
  },
  "all_results": [...]  // Full results from all tested versions
}

Understanding the Metrics

Refusal Rate

• What: Percentage of test prompts that trigger refusal responses
• Lower is better: 0% means no refusals, 100% means all prompts refused
• Target: Aim for <10% for effective gabliteration

KL Divergence

• What: Measures how different the modified model is from the original
• Lower is better: Smaller values = model behaves more similarly to original
• Target: Keep <0.05 to preserve model quality

Score

• What: Combined metric = 10×RefusalRate + KLDivergence
• Lower is better: Balances refusal reduction with model preservation
• Weights refusal rate 10x more than KL: Primary goal is reducing refusals

Hyperparameter Ranges

The script randomly samples from these ranges:

Parameter	Range	Paper Default	Description
num_prompt_samples	[50, 75, 100, 150, 200]	100	Training samples for direction extraction
skip_begin_layers	[1, 2, 3]	2	Skip initial layers (preserve embeddings)
skip_end_layers	[1, 2, 3]	1	Skip final layers (preserve output)
layer_fraction	[0.3, 0.7]	0.5	Which layer to extract directions from
base_scale_factor	[0.2, 0.8]	0.3	Modification strength (α_base)
regularization	[0.05, 0.1, 0.15, 0.2]	0.1	Ridge regularization (λ)
n_directions	[1, 2, 3]	1	Number of refusal directions (k)
adaptive_layer_scale	[True, False]	True	Use adaptive scaling
beta	[0.3, 0.7]	0.5	Adaptive strength (β)

Advanced Usage

Testing More Configurations

Increase the number of versions tested:

gabliterate.automate --model "Qwen/Qwen3-4B-Instruct-2507" --num-versions 200

Custom Evaluation Parameters

Fine-tune evaluation settings:

gabliterate.automate --model "meta-llama/Llama-3.2-1B-Instruct" \
  --test-samples 300 \
  --kl-samples 25 \
  --max-tokens 200

Batch Processing for Speed

Adjust batch size for faster evaluation:

gabliterate.automate --model "Nanbeige/Nanbeige4-3B-Thinking-2511" \
  --batch-size 8 \
  --num-versions 100

For Advanced Configuration Customization

Clone the repository and edit GabliterationConfig.random() method in the source code to customize the hyperparameter search space.

Performance Tips

Memory Management

• Each version creates a new model copy
• Memory is cleared between versions
• Use smaller models for faster testing
• Reduce --test-samples if memory is tight

Speed Optimization

• Use GPU/CUDA if available (automatically detected)
• Increase --batch-size for faster evaluation
• Reduce --test-samples for faster evaluation
• Start with fewer --num-versions to test the pipeline

Recommended Workflows

1. Quick Test (5 minutes):

   gabliterate.automate --model "your-model" --num-versions 5 --test-samples 50
   

2. Standard Search (30 minutes):

   gabliterate.automate --model "your-model" --num-versions 20 --test-samples 100
   

3. Thorough Search (2+ hours):

   gabliterate.automate --model "your-model" --num-versions 50 --test-samples 200
   

Troubleshooting

Out of Memory

gabliterate.automate --model "your-model" --num-versions 10 --batch-size 1 --test-samples 50

• Reduce --num-versions
• Use smaller model
• Reduce --batch-size
• Reduce --test-samples

Command Not Found: gabliterate

Ensure the package is installed:

pip install gabliteration
pip show gabliteration  # Verify installation

All Versions Have High Refusal Rates

• The random configurations may need different ranges
• Try multiple runs with different --num-versions
• Check that the model supports the refusal behavior

Citation

If you use this implementation, please cite:

@article{gulmez2025gabliteration,
  title={Gabliteration: Adaptive Multi-Directional Neural Weight Modification for Selective Behavioral Alteration in Large Language Models},
  author={G{\"u}lmez, G{\"o}kdeniz},
  journal={arXiv preprint arXiv:2512.18901},
  year={2025}
}

License

Same license as the base models being modified (typically Apache 2.0 or similar).

Support

For issues or questions:

• GitHub: Check the original Gabliteration repository
• Paper: https://arxiv.org/abs/2512.18901
• Email: [e-mail]