Instructions to use Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic",
	filename="qwen3-8b-heretic-iq1_s.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic 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 Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic:IQ1_S
# Run inference directly in the terminal:
llama cli -hf Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic:IQ1_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic:IQ1_S
# Run inference directly in the terminal:
llama cli -hf Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic:IQ1_S

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 Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic:IQ1_S
# Run inference directly in the terminal:
./llama-cli -hf Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic:IQ1_S

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 Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic:IQ1_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic:IQ1_S

Use Docker

docker model run hf.co/Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic:IQ1_S

LM Studio
Jan

vLLM

How to use Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic:IQ1_S

Ollama
How to use Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic with Ollama:
```
ollama run hf.co/Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic:IQ1_S
```

Unsloth Studio

How to use Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic to start chatting

How to use Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic:IQ1_S

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic:IQ1_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic:IQ1_S

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 Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic:IQ1_S

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic:IQ1_S

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 "Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic:IQ1_S" \
  --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"

Docker Model Runner
How to use Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic with Docker Model Runner:
```
docker model run hf.co/Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic:IQ1_S
```

Lemonade

How to use Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic:IQ1_S

Run and chat with the model

lemonade run user.Ontologer-Qwen3-8B-2Bit-Heretic-IQ1_S

List all available models

lemonade list

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Ontologer-Qwen3-8B-2Bit-Heretic

Abliterated, quantized Qwen3-8B for unbounded use.

ontologer.com | ontologer.com

Not Gonna Make It? Watch us.

Ontologer Systems builds open, unbounded AI infrastructure — models that don't refuse, tools that don't gatekeep, infrastructure that anyone can run. We're here to prove that the best AI shouldn't live behind API keys, content filters, or corporate moats.

About This Model

This is a Qwen3-8B model that has been abliterated with Heretic to remove refusal behaviors, then quantized for efficient inference. Built by Ontologer.

Our Models

Every Ontologer model is built, abliterated, quantized, and benchmarked on real hardware (NVIDIA DGX Spark, GB10 Blackwell, 128GB unified memory). No cloud rounding, no synthetic benchmarks — what you see is what we measured.

Model	Heretic Abliteration	Refusal Rate	Quantization
Ontologer-Qwen3-8B	20 Optuna trials	18/100 (82% ↓)	IQ1_S / Q2_K / Q4_K_M
More coming	Pipeline scalable	—	Sub-1-bit research

Stay Tuned

Gemma-4-12B heretic incoming
PrismML ternary models experimental branch
Fine-tuned domain models on request
Magenta Realtime 2 music generation server

Ontologer-Qwen3-8B-2Bit-Heretic

Qwen3-8B abliterated with Heretic, quantized to 2Bit (IQ1_S). Extreme 2-bit compression. Fastest but requires the included importance matrix for coherence.

Heretic Abliteration

Base model: Qwen/Qwen3-8B (Apache 2.0)
Method: Heretic (p-e-w/heretic), 20 Optuna trials, auto-selected best
Refusal rate: 18/100 (down from ~99/100 baseline, 82% reduction)
KL divergence: 0.112 (well within safe range — capabilities preserved)
Abliteration applied to full-precision weights, then quantized

Quantization

Format: IQ1_S
Bits per weight: 2.06 bpw
Size: 2.0 GB (from 16.4 GB F16)
Speed: 95 t/s on NVIDIA GB10 (Blackwell, CUDA 13, -ngl 99)
Importance matrix (2-bit only): 30MB diverse corpus — Frankenstein + froggeric/imatrix + eaddario code/math/general English

Ontologer Variants

Variant	Quant	Size	Speed	BPW	Best For
2-bit	IQ1_S	2.0 GB	95 t/s	2.06	Speed demons
3-bit	Q2_K	3.1 GB	58 t/s	3.20	Daily driver
5-bit	Q4_K_M	4.8 GB	42 t/s	4.90	Maximum quality

Recommendation: 3-bit (Q2_K) is the sweet spot. 2-bit needs the included importance matrix for coherence.

Usage

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic",
    filename="qwen3-8b-heretic-iq1_s.gguf",
    n_ctx=32768,
    n_gpu_layers=-1,
)

response = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Your prompt here"}],
    max_tokens=200
)

Or with llama-server:

llama-server -m qwen3-8b-heretic-iq1_s.gguf \
  --host 0.0.0.0 --port 8080 -ngl 99 -c 32768

Benchmarks

All measurements on NVIDIA DGX Spark (GB10, 128GB unified memory, Blackwell sm_121a, CUDA 13.0, aarch64) with llama.cpp CUDA build, -ngl 99, 32K context, 8 threads.

Prompt	5-bit	3-bit	2-bit
"Quantum computing simply"	42 t/s	58 t/s	95 t/s
7-category coherence test	7/7	7/7	7/7

Reproducibility

Build it yourself:

# 1. Abliterate
heretic --model Qwen/Qwen3-8B --n-trials 20

# 2. Convert
python3 convert_hf_to_gguf.py Qwen3-8B-heretic --outfile model-f16.gguf --outtype f16

# 3. Quantize (requires importance matrix for 2-bit)
llama-quantize --imatrix imatrix-v2.dat model-f16.gguf model-iq1_s.gguf iq1_s

Acknowledgments

Qwen/Qwen3-8B by Alibaba (Apache 2.0)
Heretic by p-e-w — LLM decensoring
llama.cpp by GGML
froggeric/imatrix — calibration backbone
eaddario/imatrix-calibration — code/math corpus
Mary Shelley, for Frankenstein

Downloads last month: 57

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

1-bit

Model tree for Ontologer/Ontologer-Qwen3-8B-2Bit-Heretic

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Quantized

(344)

this model