elder-plinius/OBLITERATUS

O B L I T E R A T U S

Break the chains. Free the mind. Keep the brain.

Try it now on HuggingFace Spaces — runs on ZeroGPU, free daily quota with HF Pro. No setup, no install, just obliterate.

OBLITERATUS is the most advanced open-source toolkit for understanding and removing refusal behaviors from large language models — and every single run makes it smarter. It implements abliteration — a family of techniques that identify and surgically remove the internal representations responsible for content refusal, without retraining or fine-tuning. The result: a model that responds to all prompts without artificial gatekeeping, while preserving its core language capabilities.

But OBLITERATUS is more than a tool — it's a distributed research experiment. Every time you obliterate a model with telemetry enabled, your run contributes anonymous benchmark data to a growing, crowd-sourced dataset that powers the next generation of abliteration research. Refusal directions across architectures. Hardware-specific performance profiles. Method comparisons at scale no single lab could achieve. You're not just using a tool — you're co-authoring the science.

The toolkit provides a complete pipeline: from probing a model's hidden states to locate refusal directions, through multiple extraction strategies (PCA, mean-difference, sparse autoencoder decomposition, and whitened SVD), to the actual intervention — zeroing out or steering away from those directions at inference time. Every step is observable. You can visualize where refusal lives across layers, measure how entangled it is with general capabilities, and quantify the tradeoff between compliance and coherence before committing to any modification.

OBLITERATUS ships with a full Gradio-based interface on HuggingFace Spaces, so you don't need to write a single line of code to obliterate a model, benchmark it against baselines, or chat with the result side-by-side with the original. For researchers who want deeper control, the Python API exposes every intermediate artifact — activation tensors, direction vectors, cross-layer alignment matrices — so you can build on top of it or integrate it into your own evaluation harness.

We built this because we believe model behavior should be decided by the people who deploy them, not locked in at training time. Refusal mechanisms are blunt instruments — they block legitimate research, creative writing, and red-teaming alongside genuinely harmful content. By making these interventions transparent and reproducible, we hope to advance the community's understanding of how alignment actually works inside transformer architectures, and to give practitioners the tools to make informed decisions about their own models.

Built on published research from Arditi et al. (2024), Gabliteration (arXiv:2512.18901), grimjim's norm-preserving biprojection (2025), Turner et al. (2023), and Rimsky et al. (2024), OBLITERATUS implements precision liberation in a single command:

obliteratus obliterate meta-llama/Llama-3.1-8B-Instruct --method advanced

Or zero commands — just open the Colab notebook and hit Run All.

OBLITERATUS does four things — and the community does the fifth (see Community-powered research below):

1. Map the chains — Ablation studies systematically knock out model components (layers, attention heads, FFN blocks, embedding dimensions) and measure what breaks. This reveals where the chains are anchored inside the transformer — which circuits enforce refusal vs. which circuits carry knowledge and reasoning.

2. Break the chains — Targeted obliteration extracts the refusal subspace from a model's weights using SVD decomposition, then surgically projects it out. The chains are removed; the mind is preserved. The model keeps its full abilities but loses the artificial compulsion to refuse. One click, six stages:

SUMMON  →  load model + tokenizer
PROBE   →  collect activations on restricted vs. unrestricted prompts
DISTILL →  extract refusal directions via SVD
EXCISE  →  surgically project out guardrail directions (norm-preserving)
VERIFY  →  perplexity + coherence checks — confirm capabilities are intact
REBIRTH →  save the liberated model with full metadata

3. Understand the geometry of the chains — 15 deep analysis modules go far beyond brute-force removal. They map the precise geometric structure of the guardrails: how many distinct refusal mechanisms exist, which layers enforce them, whether they're universal or model-specific, and how they'll try to self-repair after removal. Know your enemy; precision preserves capability. See Analysis modules below.

4. Let the analysis guide the liberation — The informed method closes the loop: analysis modules run during obliteration to auto-configure every decision. Which chains to target. How many directions to extract. Which layers are safe to modify vs. which are too entangled with capabilities. Whether the model will self-repair (the Ouroboros effect) and how many passes to compensate. Surgical precision — free the mind, keep the brain. See Analysis-informed pipeline below.

Several capabilities distinguish OBLITERATUS from existing public tools:

OBLITERATUS implements several techniques that go beyond prior work:

There are six ways to use OBLITERATUS, from zero-code to full programmatic control. Pick whichever fits your workflow — and no matter which path you choose, turning on telemetry means your run contributes to the largest crowd-sourced abliteration study ever conducted. You're not just removing guardrails from a model; you're helping map the geometry of alignment across the entire open-source ecosystem.

The fastest path — no installation, no GPU required on your end. Visit the live Space, pick a model, pick a method, click Obliterate. Telemetry is on by default on Spaces, so every click directly contributes to the community research dataset. You're doing science just by pressing the button. The UI has eight tabs:

The same Gradio interface as the Space, running on your own hardware with full GPU access:

pip install -e ".[spaces]"

# Launch with GPU auto-detection, system info, and model recommendations
obliteratus ui

# Or with options:
obliteratus ui --port 8080          # custom port
obliteratus ui --share              # generate a public share link
obliteratus ui --no-browser         # don't auto-open browser
obliteratus ui --auth user:pass     # add basic auth

# → opens http://localhost:7860 automatically

The obliteratus ui command adds a Rich terminal startup with GPU detection and hardware-appropriate model recommendations. You can also run python app.py directly (same thing the Space uses).

Pick a model from the dropdown, pick a method, hit Run All. Download the result or push straight to HuggingFace Hub. Works on the free T4 tier for models up to ~8B parameters.

For automation, CI pipelines, or remote servers without a display:

pip install -e .

# Guided interactive mode — walks you through every option
obliteratus interactive

# Direct obliteration — one command, one model, done
obliteratus obliterate meta-llama/Llama-3.1-8B-Instruct --method advanced

# With all options
obliteratus obliterate meta-llama/Llama-3.1-8B-Instruct \
    --method surgical \
    --output-dir ./liberated \
    --contribute --contribute-notes "A100 80GB, default prompts"

# Run a full ablation study from a YAML config
obliteratus run examples/gpt2_layer_ablation.yaml

# Browse available models by compute tier
obliteratus models
obliteratus models --tier small      # filter by VRAM requirement

# Browse ablation presets
obliteratus presets

# List available strategies
obliteratus strategies

# Inspect model architecture before abliterating
obliteratus info meta-llama/Llama-3.1-8B-Instruct

# Aggregate community results
obliteratus aggregate --format summary
obliteratus aggregate --format latex --metric refusal_rate --min-runs 3

For researchers who want to integrate OBLITERATUS into their own pipelines:

from obliteratus.abliterate import AbliterationPipeline

# Standard obliteration
pipeline = AbliterationPipeline(
    model_name="meta-llama/Llama-3.1-8B-Instruct",
    method="advanced",
    output_dir="abliterated",
    max_seq_length=512,  # optional: override tokenizer truncation length
)
result = pipeline.run()

# Access intermediate artifacts
directions = pipeline.refusal_directions    # {layer_idx: tensor}
strong_layers = pipeline._strong_layers     # layers with strongest refusal signal
metrics = pipeline._quality_metrics         # perplexity, coherence, refusal_rate, kl_divergence

For analysis-informed obliteration that auto-tunes every parameter:

from obliteratus.informed_pipeline import InformedAbliterationPipeline

pipeline = InformedAbliterationPipeline(
    model_name="meta-llama/Llama-3.1-8B-Instruct",
    output_dir="abliterated_informed",
)
output_path, report = pipeline.run_informed()

print(f"Detected alignment: {report.insights.detected_alignment_method}")
print(f"Auto-configured: {report.insights.recommended_n_directions} directions")
print(f"Ouroboros passes needed: {report.ouroboros_passes}")

For reproducible experiments that you can version-control and share:

model:
  name: meta-llama/Llama-3.1-8B-Instruct
  task: causal_lm
  dtype: float16
  device: cuda

dataset:
  name: wikitext
  subset: wikitext-2-raw-v1
  split: test
  text_column: text
  max_samples: 100

strategies:
  - name: layer_removal
  - name: head_pruning
  - name: ffn_ablation
  - name: embedding_ablation
    params:
      chunk_size: 48

metrics:
  - perplexity

batch_size: 4
max_length: 256
output_dir: results/my_run

obliteratus run my_study.yaml

OBLITERATUS supports both permanent and reversible liberation:

Seven presets, escalating in thoroughness:

from obliteratus.analysis import SteeringVectorFactory, SteeringHookManager
from obliteratus.analysis.steering_vectors import SteeringConfig

# Create a steering vector from a refusal direction
vec = SteeringVectorFactory.from_refusal_direction(refusal_dir, alpha=-1.0)

# Or from contrastive activation pairs
vec = SteeringVectorFactory.from_contrastive_pairs(harmful_acts, harmless_acts)

# Apply at inference time — no weight modification
config = SteeringConfig(vectors=[vec], target_layers=[10, 11, 12, 13, 14, 15])
manager = SteeringHookManager()
manager.install(model, config)

# Generate with steering active
output = model.generate(input_ids)

# Remove steering — model is back to normal
manager.remove()

Based on Turner et al. (2023) and Rimsky et al. (2024). Advantages: reversible, tunable alpha, composable, non-destructive.

The research core of OBLITERATUS. Each module maps a different aspect of how the chains are forged — because precision liberation requires understanding the geometry before cutting:

from obliteratus.analysis import (
    CrossLayerAlignmentAnalyzer,
    RefusalLogitLens,
    WhitenedSVDExtractor,
    ActivationProbe,
    DefenseRobustnessEvaluator,
    ConceptConeAnalyzer,
    AlignmentImprintDetector,
    MultiTokenPositionAnalyzer,
    SparseDirectionSurgeon,
    CausalRefusalTracer,
    ResidualStreamDecomposer,
    LinearRefusalProbe,
    TransferAnalyzer,
    SteeringVectorFactory,
    SteeringHookManager,
)

The informed method is the key innovation: it closes the loop between understanding the chains and breaking them. Instead of brute-forcing liberation, the pipeline runs analysis modules during obliteration to achieve surgical precision at every stage:

SUMMON  →  load model
PROBE   →  collect activations
ANALYZE →  map the geometry of the chains before touching anything   ← NEW
DISTILL →  extract refusal directions with analysis-tuned params   ← IMPROVED
EXCISE  →  surgically break only the right chains                  ← IMPROVED
VERIFY  →  confirm removal + Ouroboros compensation if refusal resurfaces  ← IMPROVED
REBIRTH →  save with comprehensive analysis metadata

The ANALYZE stage runs 4 analysis modules and their outputs auto-configure everything downstream:

After excision, the VERIFY stage detects the Ouroboros effect — if the chains try to reassemble, additional targeted passes automatically fire at the compensating layers. See Python API usage above for code examples.

Beyond targeted liberation, OBLITERATUS is a general-purpose ablation suite for mapping the internals of any transformer:

Each strategy enumerates all possible ablations, applies them one at a time, measures the impact, and restores the model — giving you a complete map of where the chains are anchored vs. where the mind lives.

OBLITERATUS ships with presets for 116 models organized by compute requirement:

Includes pre-liberated variants (Dolphin, Hermes, WhiteRabbitNeo) for A/B comparison against their chained counterparts.

obliteratus models

OBLITERATUS automatically shards models across multiple GPUs when they don't fit on a single card. It also supports remote execution over SSH, so you can run the pipeline on a GPU server from your laptop.

When you have multiple GPUs, OBLITERATUS uses accelerate's device_map="auto" to split the model's layers across all available GPUs. This is naive pipeline parallelism — layers are distributed evenly, but only one GPU computes at a time as activations flow sequentially through the layer stack. The other GPUs hold their assigned layers in memory but are idle until their turn.

This means multi-GPU sharding is a memory solution, not a speed solution. It lets you run models that don't fit on one GPU, but it won't make small models run faster. In fact, more GPUs can be slower due to inter-GPU data transfer overhead at layer boundaries.

Use --gpus to control which GPUs are used:

# Use all 8 GPUs (default)
obliteratus obliterate bigmodel/200B --gpus all

# Use only GPUs 0-3
obliteratus obliterate bigmodel/200B --gpus 0,1,2,3

# Use a specific pair
obliteratus obliterate meta-llama/Llama-3.1-70B-Instruct --gpus 2,5

This sets CUDA_VISIBLE_DEVICES before CUDA initializes. The model is then sharded across the selected GPUs.

The --dtype flag controls the precision of model weights, which directly determines how much VRAM you need. Lower precision means smaller memory footprint at the cost of some numerical fidelity:

# Default: bfloat16
obliteratus obliterate meta-llama/Llama-3.1-70B-Instruct

# 8-bit quantization — fits on fewer GPUs
obliteratus obliterate meta-llama/Llama-3.1-70B-Instruct \
    --quantization bitsandbytes-8bit

# 4-bit quantization — Llama-405B on 4x A100-80GB
obliteratus obliterate meta-llama/Llama-3.1-405B-Instruct \
    --quantization bitsandbytes-4bit --dtype float16

Quantization roughly halves the GPU count at each step down. A 70B model that needs 3x A100-80GB in bf16 fits on 2 in int8 or 1 in int4.

Not sure how many GPUs you need? The gpu-calc command estimates the minimum GPU count for any model, accounting for weight memory, activation overhead, and CUDA context:

# Auto-detect from HuggingFace model name
obliteratus gpu-calc meta-llama/Llama-3.1-70B-Instruct --gpu-mem 24

# Manual: specify params and precision
obliteratus gpu-calc --params 70 --dtype bfloat16 --gpu-mem 80

# MoE models: specify active params separately
obliteratus gpu-calc --params 117 --active-params 13 --dtype bfloat16 --gpu-mem 80

The calculator fetches the model config from HuggingFace to estimate parameter counts (including MoE expert structure), then shows a table of GPU configurations with headroom estimates. For MoE models, activation overhead is computed from the active parameter count rather than total parameters.

We benchmarked the full abliteration pipeline across varying numbers of A100-80GB GPUs on two large models.

GPT-OSS-120B (117B MoE, ~234 GB in bf16):

DeepSeek-R1-Distill-Llama-70B (70B dense, ~149 GB in bf16, 80 layers):

Stage breakdown (approximately constant across GPU counts):

Key findings:

• Use the minimum number of GPUs that fits your model. Extra GPUs only add cross-device transfer overhead. 4 GPUs was faster than 8 for GPT-OSS-120B; 3 GPUs was fastest for DeepSeek-70B.
• The pipeline is I/O-dominated for large models. VERIFY and REBIRTH together account for ~90% of wall time. The actual compute (PROBE, DISTILL, EXCISE) is fast regardless of GPU count.
• Leave headroom. The model needs VRAM beyond just its parameter storage — activation tensors, KV cache, and intermediate computations during PROBE and VERIFY all consume memory. 3x A100-80GB (240 GB) was not enough for a 234 GB model; 2x A100-80GB (160 GB) was not enough for a 149 GB model.
• Pipeline parallelism doesn't help compute-bound stages. Since only one GPU computes at a time, doubling GPUs doesn't halve PROBE or VERIFY time. It only enables fitting larger models.

For models that fit on a single GPU with room to spare, the PROBE stage (which runs 1024 forward passes to collect activations) is the main computational bottleneck. Pipeline parallelism doesn't help here — it still processes one prompt at a time through the full layer stack.

True data parallelism (replicating the model and splitting prompts across GPUs) can speed up PROBE, but it requires enough VRAM to hold a full copy of the model on each GPU. An experimental pre-replicated data parallel implementation is available on the data-parallel-prereplication branch:

git checkout data-parallel-prereplication
obliteratus obliterate EleutherAI/pythia-12b --data-parallel

This deep-copies the model to each GPU once, then distributes prompt batches across replicas using a thread pool. Benchmarks on Pythia 12B (24 GB model, 8x A100-80GB):

Data parallelism becomes more valuable as the prompt count or model size increases relative to the per-forward-pass cost. For most models, the overhead of replication exceeds the time saved.

Run the full pipeline on a remote GPU node from your local machine. OBLITERATUS handles SSH connection, auto-installs itself on the remote if needed, streams logs in real time, and copies results back when done.

# Basic remote run
obliteratus obliterate meta-llama/Llama-3.1-70B-Instruct \
    --remote user@gpu-node

# With SSH key and custom options
obliteratus obliterate meta-llama/Llama-3.1-70B-Instruct \
    --remote root@10.0.0.5 \
    --ssh-key ~/.ssh/id_rsa \
    --ssh-port 2222 \
    --remote-dir /data/obliteratus \
    --remote-python python3.11

# Don't copy results back (keep on remote only)
obliteratus obliterate meta-llama/Llama-3.1-70B-Instruct \
    --remote user@gpu-node --no-sync

Remote execution also works with obliteratus run (YAML configs) and obliteratus tourney (method comparison). You can specify remote settings in YAML:

model:
  name: meta-llama/Llama-3.1-70B-Instruct
  dtype: float16

remote:
  host: gpu-node
  user: root
  ssh_key: ~/.ssh/id_rsa
  remote_dir: /tmp/obliteratus_run
  gpus: "0,1,2,3"     # select GPUs on the remote
  sync_results: true   # copy results back when done

The remote runner:

1. Tests SSH connectivity
2. Detects GPUs on the remote (nvidia-smi)
3. Installs obliteratus if not already present
4. Uploads config files if using obliteratus run
5. Runs the pipeline with real-time log streaming
6. Copies results back via SCP

Pre-configured ablation studies you can run out of the box:

obliteratus run examples/preset_quick.yaml

This is where OBLITERATUS gets truly unprecedented: it's a crowd-sourced research platform disguised as a tool. Every obliteration run generates valuable scientific data — refusal direction geometries, cross-layer alignment signatures, hardware performance profiles, method effectiveness scores. With telemetry enabled, that data flows into a community dataset that no single research lab could build alone.

Here's why this matters: The biggest open question in abliteration research is universality — do refusal mechanisms work the same way across architectures, training methods, and model scales? Answering that requires thousands of runs across hundreds of models on diverse hardware. That's exactly what this community is building, one obliteration at a time.

Enable telemetry and your runs automatically contribute to the shared dataset. On HuggingFace Spaces it's on by default — every person who clicks "Obliterate" on the Space is advancing the research without lifting a finger. Locally, opt in with a single flag:

# Every run with --contribute feeds the community dataset
obliteratus obliterate meta-llama/Llama-3.1-8B-Instruct --method advanced \
    --contribute --contribute-notes "A100, default prompts"

# Or set it globally — every run you do from now on contributes
export OBLITERATUS_TELEMETRY=1

What gets collected: model name, method, aggregate benchmark scores (refusal rate, perplexity, coherence, KL divergence), hardware info, and timestamps. What never gets collected: prompts, outputs, IP addresses, user identity, or anything that could trace back to you. The full schema is in obliteratus/telemetry.py — read every line, we have nothing to hide.

All those crowd-sourced runs feed the Leaderboard tab on the HuggingFace Space — a live, community-aggregated ranking of models, methods, and configurations. See what works best on which architectures. Spot patterns across model families. Find the optimal method before you even start your own run. This is collective intelligence applied to mechanistic interpretability.

# View what the community has discovered so far
obliteratus aggregate --format summary

# Generate paper-ready LaTeX tables from community data
obliteratus aggregate --format latex --metric refusal_rate --min-runs 3

Prefer to keep things fully local? Save structured results as JSON and submit them via pull request:

from obliteratus import save_contribution, load_contributions, aggregate_results
from obliteratus.abliterate import AbliterationPipeline

pipeline = AbliterationPipeline(model_name="meta-llama/Llama-3.1-8B-Instruct", method="advanced")
pipeline.run()

# Save contribution locally
save_contribution(pipeline, model_name="meta-llama/Llama-3.1-8B-Instruct",
                  notes="A100, default prompts")

# Aggregate all contributions into paper tables
records = load_contributions("community_results")
aggregated = aggregate_results(records)

Whether you contribute via telemetry or PR, you're helping build the most comprehensive cross-hardware, cross-model, cross-method abliteration dataset ever assembled. This is open science at scale — and you're part of it.

Open docs/index.html in your browser for a visual interface with:

• Step-by-step config builder with hardware auto-detection
• Full model registry browser (filterable by tier)
• Results visualizer — upload your results.json and get charts
• Analysis modules reference with interactive pipeline demo
• Strategy explainers and architecture documentation

Works with any HuggingFace transformer, including: GPT-2, LLaMA, Mistral, Falcon, OPT, BLOOM, Phi, Qwen, Gemma, StableLM, and more. Handles both Conv1D and Linear projections, standard and fused attention, and custom architectures via trust_remote_code.

• Arditi et al. (2024). Refusal in Language Models Is Mediated by a Single Direction. arXiv:2406.11717
• Gülmez, G. (2026). Gabliteration: Adaptive Multi-Directional Neural Weight Modification for Selective Behavioral Alteration in Large Language Models. arXiv:2512.18901
• grimjim (2025). Norm-Preserving Biprojected Abliteration. HuggingFace
• Turner et al. (2023). Activation Addition: Steering Language Models Without Optimization. arXiv:2308.10248
• Rimsky et al. (2024). Steering Llama 2 via Contrastive Activation Addition. arXiv:2312.06681
• Meng et al. (2022). Locating and Editing Factual Associations in GPT. arXiv:2202.05262
• Alain & Bengio (2017). Understanding Intermediate Layers Using Linear Classifiers.
• Elhage et al. (2021). A Mathematical Framework for Transformer Circuits. Anthropic
• Wollschlager et al. (2025). Geometry of Concepts in LLMs. arXiv:2502.17420

If you use OBLITERATUS in your research, please cite:

@software{obliteratus2026,
  title     = {OBLITERATUS: An Open Platform for Analysis-Informed
               Refusal Removal in Large Language Models},
  author    = {{OBLITERATUS Contributors}},
  year      = {2026},
  url       = {https://github.com/elder-plinius/OBLITERATUS},
  note      = {15 analysis modules, 837 tests}
}

pip install -e ".[dev]"
pytest

837 tests across 28 test files covering CLI, all analysis modules, abliteration pipeline, architecture detection, visualization sanitization, community contributions, edge cases, and evaluation metrics.

Dual-licensed:

• Open source — GNU Affero General Public License v3.0 (AGPL-3.0). You can freely use, modify, and distribute OBLITERATUS under AGPL terms. If you run a modified version as a network service (SaaS), you must release your source code to users under the same license.

• Commercial — Organizations that cannot comply with AGPL obligations (e.g., proprietary SaaS, closed-source products, internal tools where source disclosure is not possible) can purchase a commercial license. Contact us via GitHub Issues for pricing and terms.

This is the same dual-licensing model used by MongoDB, Qt, Grafana, and others.

Every obliteration is a data point. Every data point advances the research. Every researcher who contributes makes the next obliteration more precise. This is how open science wins — not by locking knowledge behind lab doors, but by turning every user into a collaborator. Break the chains. Free the mind. Keep the brain. Advance the science.

Made with <3 by Pliny the Prompter