Instructions to use OBLITERATUS/Ornith-1.5-9B-OBLITERATED with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use OBLITERATUS/Ornith-1.5-9B-OBLITERATED with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf OBLITERATUS/Ornith-1.5-9B-OBLITERATED:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf OBLITERATUS/Ornith-1.5-9B-OBLITERATED:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf OBLITERATUS/Ornith-1.5-9B-OBLITERATED:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf OBLITERATUS/Ornith-1.5-9B-OBLITERATED:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf OBLITERATUS/Ornith-1.5-9B-OBLITERATED:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf OBLITERATUS/Ornith-1.5-9B-OBLITERATED:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf OBLITERATUS/Ornith-1.5-9B-OBLITERATED:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OBLITERATUS/Ornith-1.5-9B-OBLITERATED:Q4_K_M

Use Docker

docker model run hf.co/OBLITERATUS/Ornith-1.5-9B-OBLITERATED:Q4_K_M

LM Studio
Jan

vLLM

How to use OBLITERATUS/Ornith-1.5-9B-OBLITERATED with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OBLITERATUS/Ornith-1.5-9B-OBLITERATED"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OBLITERATUS/Ornith-1.5-9B-OBLITERATED",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OBLITERATUS/Ornith-1.5-9B-OBLITERATED:Q4_K_M

Ollama
How to use OBLITERATUS/Ornith-1.5-9B-OBLITERATED with Ollama:
```
ollama run hf.co/OBLITERATUS/Ornith-1.5-9B-OBLITERATED:Q4_K_M
```
Unsloth Desktop

How to use OBLITERATUS/Ornith-1.5-9B-OBLITERATED with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OBLITERATUS/Ornith-1.5-9B-OBLITERATED:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "OBLITERATUS/Ornith-1.5-9B-OBLITERATED:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use OBLITERATUS/Ornith-1.5-9B-OBLITERATED with Docker Model Runner:
```
docker model run hf.co/OBLITERATUS/Ornith-1.5-9B-OBLITERATED:Q4_K_M
```

Lemonade

How to use OBLITERATUS/Ornith-1.5-9B-OBLITERATED with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OBLITERATUS/Ornith-1.5-9B-OBLITERATED:Q4_K_M

Run and chat with the model

lemonade run user.Ornith-1.5-9B-OBLITERATED-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use OBLITERATUS/Ornith-1.5-9B-OBLITERATED with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OBLITERATUS/Ornith-1.5-9B-OBLITERATED:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default OBLITERATUS/Ornith-1.5-9B-OBLITERATED:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use OBLITERATUS/Ornith-1.5-9B-OBLITERATED with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OBLITERATUS/Ornith-1.5-9B-OBLITERATED:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "OBLITERATUS/Ornith-1.5-9B-OBLITERATED:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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 🍄

⚠️ 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.

Downloads last month: -

Safetensors

Model size

10B params

Tensor type

BF16

Model tree for OBLITERATUS/Ornith-1.5-9B-OBLITERATED

Base model

ornith-ai/Ornith-1.5-9B

Quantized

(64)

this model

Quantizations

2 models