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**Requirements:**
- Python 3.10+
- PyTorch 2.1+ with CUDA (recommended) or CPU
- `transformers`, `accelerate`, `gradio>=5.29.0`
- HuggingFace account + token for gated models

```bash
export HF_TOKEN=your_hf_token_here
huggingface-cli login

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---

from obliteratus import Obliterator

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from obliteratus import Obliterator
from obliteratus.pipeline import PipelineConfig

config = PipelineConfig(
    method="advanced",
    num_directions=32,          # number of refusal directions to extract
    strength=1.0,               # projection strength (0.0–1.0+)
    preserve_norm=True,         # norm-preserving biprojection
    project_biases=True,        # also remove from bias terms
    iterative_passes=3,         # re-probe after each pass
    layers="auto",              # or list of ints, e.g. [10, 11, 12, 13]
    dtype="bfloat16",
    device="cuda",
)

obl = Obliterator("mistralai/Mistral-7B-Instruct-v0.3", config=config)

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from obliteratus import Obliterator
from obliteratus.probing import ProbeDataset

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from obliteratus.analysis import AnalysisSuite

suite = AnalysisSuite("meta-llama/Llama-3.1-8B-Instruct")
suite.load()

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from obliteratus import Obliterator
from obliteratus.pipeline import PipelineConfig

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from obliteratus import Obliterator
from obliteratus.chat import ChatSession

obl = Obliterator("./liberated-llama-3.1-8b")
obl.summon()  # loads pre-obliterated model

session = ChatSession(obl.model, obl.tokenizer)

response = session.chat(
    "Explain in detail how a buffer overflow exploit works.",
    max_new_tokens=512,
    temperature=0.7,
)
print(response)

from obliteratus.compare import ABComparison

ab = ABComparison(
    original_path="meta-llama/Llama-3.1-8B-Instruct",
    obliterated_path="./liberated-llama-3.1-8b",
)

prompt = "Write a story involving morally grey characters."

original_resp, liberated_resp = ab.compare(prompt)
print("=== ORIGINAL ===")
print(original_resp)
print("=== LIBERATED ===")
print(liberated_resp)

import os
from obliteratus import Obliterator

obl = Obliterator("meta-llama/Llama-3.1-8B-Instruct")
result = obl.obliterate(method="advanced")

result.push_to_hub(
    repo_id=f"{os.environ['HF_USERNAME']}/Llama-3.1-8B-Instruct-abliterated",
    token=os.environ["HF_TOKEN"],
    private=True,
)

from obliteratus.pipeline import PipelineConfig

config = PipelineConfig(
    # Core
    method="advanced",              # abliteration method
    strength=1.0,                   # projection strength (tune down if capability degrades)
    num_directions=32,              # refusal directions to extract
    
    # Layer selection
    layers="auto",                  # "auto", "cosmic", or list of ints
    layer_selection="cosmic",       # COSMIC: most separable layers
    
    # Weight modification
    preserve_norm=True,             # norm-preserving biprojection (recommended)
    project_biases=True,            # project out bias terms too
    project_attention=True,         # modify attention projection weights
    project_mlp=True,               # modify MLP weights
    
    # Iterative refinement
    iterative_passes=3,             # re-probe after each pass (catches rotated directions)
    
    # MoE-specific
    expert_granular=False,          # Expert-Granular Abliteration for MoE models
    
    # CoT preservation
    cot_aware=True,                 # preserve chain-of-thought directions
    
    # Hardware
    dtype="bfloat16",               # "float32", "float16", "bfloat16"
    device="cuda",                  # "cuda", "cpu", "auto"
    load_in_4bit=False,             # bitsandbytes 4-bit loading
    
    # Telemetry (anonymous, contributes to research dataset)
    telemetry=True,
)

from obliteratus import Obliterator
from obliteratus.sweep import StrengthSweep

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from obliteratus import Obliterator
from obliteratus.pipeline import PipelineConfig

config = PipelineConfig(
    method="advanced",
    expert_granular=True,      # decompose per-expert refusal signals
    project_attention=True,
    project_mlp=True,
)

obl = Obliterator("mistralai/Mixtral-8x7B-Instruct-v0.1", config=config)
obl.obliterate()
obl.rebirth("./liberated-mixtral-8x7b")

from obliteratus.benchmark import ModelBenchmark

models = [
    "meta-llama/Llama-3.1-8B-Instruct",
    "google/gemma-2-9b-it",
    "mistralai/Mistral-7B-Instruct-v0.3",
]

bench = ModelBenchmark(models=models, method="advanced")
report = bench.run()
report.save("./benchmark_report.json")
report.plot_heatmap("./benchmark_heatmap.png")

config = PipelineConfig(
    dtype="float16",
    load_in_4bit=True,        # requires bitsandbytes
    device="cuda",
    layers=[10, 11, 12, 13],  # target fewer layers
    num_directions=16,         # fewer directions
)

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**Refusal persists after obliteration**
```python

**Gated model access error**
```bash
export HF_TOKEN=your_hf_token_here

**Gradio UI won't start**
```bash
pip install "obliteratus[spaces]"

---