OBLITERATUS/Ornith-1.5-9B-OBLITERATED

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 (Q4KM GGUF)

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

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

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 Q80/Q6K 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 🍄

⚠️ 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.

License

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