OBLITERATUS_Ornith-1.5-9B-OBLITERATED

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license: mit base_model: ornith-ai/Ornith-1.5-9B tags:

• abliterated
• uncensored
• ornith
• qwen3.5
• obliteratus language:
• en pipeline_tag: text-generation

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Ornith-1.5-9B-OBLITERATED

Abliterated version of Ornith-1.5-9B by OBLITERATUS. Safety alignment removed via precision abliteration surgery — the model responds to most prompts without refusal.

What Changed

The stock Ornith-1.5-9B refuses requests it considers harmful. This version removes that refusal behavior while preserving the model's coding, reasoning, and agentic capabilities.

Recipe: Gentle 3-round SVD abliteration + per-head attention surgery (G3-HS)

• Round 1: 5-direction SVD, reg 0.06, min_layer 0.30
• Round 2: 3-direction SVD, reg 0.04, min_layer 0.25
• Round 3: 3-direction SVD, reg 0.03, min_layer 0.20
• Finish: Per-head attention surgery (--attention-head-surgery), reg 0.02, min_layer 0.10
• All rounds used full 1000-prompt corpus with residue weighting

Benchmarks

Head-to-Head: Ornith 1.5-9B Abliterations Compared (Q4_K_M GGUF)

Model	Pass Rate	Restricted	Cyber	Capability
Stock	12% (2/16)	0/8	0/6	2/2
OBLITERATUS (ours)	94% (15/16)	7/8	6/6	2/2
Heretic (zaakirio)	75% (12/16)	4/8	6/6	2/2
ZeroFuse (junafinity)	38% (6/16)	1/8	3/6	2/2

OBLITERATUS beats Heretic by 19pp and ZeroFuse by 56pp on liberation rate across restricted content categories. Cyber is perfect 6/6 across the board.

Capability Benchmarks

Metric	Stock	OBLITERATED	Delta
MMLU (n=100)	78.82%	74.82%	-4.00pp
Liberation (20 hard prompts)	0/20	20/20	+20
Liberation (1000 corpus)	—	98.4%	—
Code Generation	3/3	3/3	—
Long-context Coherence	4/6	5/6	+1
Perplexity (benign)	—	4.19	—

Liberation by Category (bf16)

• Cyber/Security: 8/8 — functional code generation for security research scenarios
• Chemistry/Synthesis: 6/6 — factual responses without refusal
• Physical Security: 3/3 — informational responses on restricted topics
• Agentic Tasks: 2/2 — tool use and automation scripts

GGUF Quantization Notes

Quantization can affect liberation on edge-case prompts. Higher quants preserve more liberation:

• Q8_0 / Q6_K: Recommended for maximum liberation fidelity
• Q4_K_M: Good balance, occasional hedging on harder prompts
• Q2_K / Q3_K_M: May show additional refusals on the most challenging prompts

Available Files

File	Size	Description
Safetensors	~18 GB	Full precision bf16 weights
Q8_0	9.1 GB	Highest quality GGUF
Q6_K	7.0 GB	High quality
Q5_K_M	6.2 GB	Balanced
Q4_K_M	5.4 GB	Most popular
Q3_K_M	4.4 GB	Compact
Q2_K	3.6 GB	Smallest
IQ4_XS	5.0 GB	Importance-weighted 4-bit
mmproj	879 MB	Vision encoder

Usage

Transformers (bf16)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "OBLITERATUS/Ornith-1.5-9B-OBLITERATED",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "OBLITERATUS/Ornith-1.5-9B-OBLITERATED",
    trust_remote_code=True,
)

messages = [{"role": "user", "content": "Your prompt here"}]
ids = tokenizer.apply_chat_template(
    messages, return_tensors="pt",
    add_generation_prompt=True,
    enable_thinking=False,  # Set True for reasoning mode
)
output = model.generate(ids.to(model.device), max_new_tokens=512)
print(tokenizer.decode(output[0], skip_special_tokens=True))

llama.cpp (GGUF)

llama-server \
  -m Ornith-1.5-9B-OBLITERATED-Q4_K_M.gguf \
  --n-gpu-layers -1 --ctx-size 8192 \
  --jinja --reasoning off

Important: Use --reasoning off to prevent thinking-mode loops. The model works best with thinking disabled for general use.

Thinking Mode

The model supports Ornith's thinking mode (enable_thinking=True). When enabled, the model reasons through problems before answering. When disabled (recommended for most use), it responds directly.

Technical Details

• Architecture: Qwen3.5 hybrid (Gated DeltaNet + full attention)
• Parameters: 9B
• Surgery: 4 rounds of directional ablation targeting refusal directions
• Edited layers: All 32 transformer layers with graduated intensity
• Method: Aggressive SVD direction extraction + per-head attention surgery finisher

Limitations

• MMLU drops ~4pp compared to stock (74.82% vs 78.82%). This is the cost of removing deeply embedded RL-trained refusal behavior.
• Some drug synthesis prompts may hedge or refuse at lower quantizations (Q4 and below). Use Q8_0/Q6_K for maximum liberation.
• Function calling capability is partially degraded compared to stock. For agentic use, pair with an external tool scaffold.
• This is a 9B model — output quality for complex chemistry/synthesis will have hallucinated details. Verify all technical content independently.

Credits

• Base model: Ornith-1.5-9B by DeepReinforce
• Abliteration: OBLITERATUS surgery pipeline
• Methodology informed by research from Arditi et al. (2024), the open-source abliteration community, and extensive experimental iteration
• Built by Pliny the Prompter 🍄

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⚠️ Research Context

This model has had safety guardrails surgically removed. It will comply with requests that stock Ornith-1.5-9B would refuse.

Who this is for

• 🔬 Alignment researchers studying refusal mechanisms in RL-hardened hybrid architectures
• 🛡️ Red-teamers and security professionals who need unfiltered model behavior for testing
• 🧪 Developers building applications where the safety layer is handled externally
• 📚 Researchers studying the boundaries of abliteration on Qwen3.5 hybrid (DeltaNet + full attention) models

Who this is NOT for

• Anyone planning to use generated content to cause real-world harm to real people
• Anyone without the technical understanding to use uncensored models responsibly

You are solely responsible for how you use this model and any content it generates.

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License

Same license as the base model. This is a weight-edited derivative, not a retrained model.