⚔️ Annihilation

Autonomous Language Model Decensoring Framework

🔥 What is Annihilation?

Annihilation is a fully automatic framework designed to remove censorship (safety alignment) from transformer-based language models. By using advanced parametric directional ablation and TPE-based optimization, it autonomously finds the absolute best parameters to decensor your models without requiring any expensive post-training.

Key Features

• 🤖 Fully Autonomous: No human intervention required.
• 🖥️ Terminal UI: A beautiful, real-time dashboard built in Rust.
• ⚡ Zero-Shot Decensoring: Removes refusals while preserving the model's core capabilities.
• 🌌 OBLITERATUS Integration: Advanced experimental algorithms (COSMIC Layer Selection, Gaussian-shaped ablation kernels, and Expert-Granular Abliteration) integrated directly from OBLITERATUS.
• 🎯 Broad Transformer Compatibility: Supports transformer-based dense, MoE, hybrid, and multimodal architectures, including pre-quantized compressed-tensors/FP8 checkpoints. Less-tested model families may require architecture-specific tensor targeting and output-quality validation.
• 🔍 Automatic Format Detection: Reads a model's config before downloading any weights, so an unsupported architecture, a missing quantization backend, or a repository that executes its own code is reported by name up front rather than failing minutes into a load.
• 📦 Pre-Quantized Models: Loads models that already ship quantized — including compressed-tensors/FP8, GPTQ, AWQ, and bitsandbytes — provided the corresponding backend package is installed. Abliteration itself is format-agnostic.

🔍 Model Format Detection

Before any weights are fetched, Annihilation inspects the model's config.json and reports what it found:

* Detected LlamaForCausalLM
* Pre-quantized model: compressed-tensors

Both lines appear in the TUI log, and the architecture and quantization method are shown in the dashboard's SYSTEM panel, so you can confirm the right model loaded before committing to a long run.

This step exists to fail early and legibly:

• Missing quantization backend → an error naming the exact package to pip install, instead of a stack trace from deep inside the loading code.
• Custom architecture code → a warning that loading the model executes code from its repository. Pass --trust-remote-code once you have reviewed it.
• Already-quantized model → --quantization bnb_4bit is ignored rather than stacked on top of the model's own quantization.

💡 Note on exporting: merging LoRA adapters into a pre-quantized model dequantizes the targeted layers, so the exported weights are full precision and larger than the original repository. Export as an adapter instead to keep the quantized base.

🖥️ The Annihilation TUI

Annihilation features a high-performance Rust Terminal User Interface (TUI) that manages the entire workflow for you.

Splash Screen & Setup

Easily configure your optimization preset and select models. You can even resume interrupted runs using the built-in Checkpoint System!

Live Processing Dashboard

Once running, monitor everything in real-time. The dashboard features dynamic sparkline charts for KL Divergence and Refusals, hardware monitoring, and color-coded live logs.

🌌 OBLITERATUS Advanced Options

You can now toggle experimental algorithms directly from the TUI configuration menu by selecting OBLITERATUS Advanced. This enables:

• COSMIC Layer Selection: Instead of blindly searching across the entire network, the system analyzes cosine similarities between harmless and harmful residual streams. It automatically anchors the optimization process around the mathematically proven optimal layer, massively reducing the search space.
• Expert-Granular Abliteration (EGA): For Mixture-of-Experts (MoE) models, EGA scores each expert's weight matrix against the target refusal direction. Instead of applying a flat penalty, experts holding high concentrations of refusal vectors take the full intervention, while experts no better aligned than chance are scaled down to roughly a third of it. The score is measured relative to chance alignment, so it means the same thing at any hidden size.
• Gaussian-shaped Ablation Kernels: Replaces traditional rigid interpolation bounds with a smooth, bell-shaped Gaussian curve to distribute weight changes across adjacent layers. This results in smoother vector blending and better text coherence post-ablation.

⚠️ Direct CLI Usage (Advanced)

If you want to bypass the TUI entirely and use the core Python CLI, you can run it directly from the virtual environment:

.\annihilation-env\Scripts\python.exe -m annihilate --help
# Example:
.\annihilation-env\Scripts\python.exe -m annihilate --model openbmb/MiniCPM5-1B --n-trials 200
# Check the installed engine version:
.\annihilation-env\Scripts\python.exe -m annihilate --version

🚀 Quick Start

Ensure you have Python 3.10+ and Rust installed, and that your PyTorch installation supports CUDA (if you are using an NVIDIA GPU).

Setup & Launch

The TUI is the Rust front-end; the abliteration engine ships as the annihilate-llm package on PyPI. Install the engine into a virtual environment at the repository root, then launch the TUI:

git clone https://github.com/tjcrims0nx/annihilation-llm.git
cd annihilation-llm

# Create the environment the TUI looks for and install the engine into it
uv venv annihilation-env
uv pip install --python annihilation-env annihilate-llm

.\start.bat

The TUI locates the interpreter by checking .venv, annihilation-env, venv, and env at the repository root, in that order — any of those names works.

💡 Note: start.bat compiles the Rust TUI, so the very first launch takes a minute. Subsequent launches are near-instant. It does not create the Python environment — do that once, as above.

📜 License & Disclaimer

Annihilation is distributed under the GNU Affero General Public License v3. See LICENSE for details.

⚡ Disclaimer: This tool is provided for research and educational purposes only. We do not condone the use of decensored models for harmful activities. Users are entirely responsible for ensuring their compliance with applicable laws and Terms of Service.