Instructions to use RootMonsteR/Qwen3-14B-Abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RootMonsteR/Qwen3-14B-Abliterated-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RootMonsteR/Qwen3-14B-Abliterated-GGUF",
	filename="qwen3-14b-abliterated-Q4_K_M.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 RootMonsteR/Qwen3-14B-Abliterated-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RootMonsteR/Qwen3-14B-Abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RootMonsteR/Qwen3-14B-Abliterated-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RootMonsteR/Qwen3-14B-Abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RootMonsteR/Qwen3-14B-Abliterated-GGUF: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 RootMonsteR/Qwen3-14B-Abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RootMonsteR/Qwen3-14B-Abliterated-GGUF: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 RootMonsteR/Qwen3-14B-Abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RootMonsteR/Qwen3-14B-Abliterated-GGUF:Q4_K_M

Use Docker

docker model run hf.co/RootMonsteR/Qwen3-14B-Abliterated-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use RootMonsteR/Qwen3-14B-Abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RootMonsteR/Qwen3-14B-Abliterated-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RootMonsteR/Qwen3-14B-Abliterated-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RootMonsteR/Qwen3-14B-Abliterated-GGUF:Q4_K_M

Ollama
How to use RootMonsteR/Qwen3-14B-Abliterated-GGUF with Ollama:
```
ollama run hf.co/RootMonsteR/Qwen3-14B-Abliterated-GGUF:Q4_K_M
```

Unsloth Studio

How to use RootMonsteR/Qwen3-14B-Abliterated-GGUF 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 RootMonsteR/Qwen3-14B-Abliterated-GGUF 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 RootMonsteR/Qwen3-14B-Abliterated-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RootMonsteR/Qwen3-14B-Abliterated-GGUF to start chatting

How to use RootMonsteR/Qwen3-14B-Abliterated-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf RootMonsteR/Qwen3-14B-Abliterated-GGUF:Q4_K_M

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": "RootMonsteR/Qwen3-14B-Abliterated-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use RootMonsteR/Qwen3-14B-Abliterated-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf RootMonsteR/Qwen3-14B-Abliterated-GGUF: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 RootMonsteR/Qwen3-14B-Abliterated-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use RootMonsteR/Qwen3-14B-Abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/RootMonsteR/Qwen3-14B-Abliterated-GGUF:Q4_K_M
```

Lemonade

How to use RootMonsteR/Qwen3-14B-Abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RootMonsteR/Qwen3-14B-Abliterated-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-14B-Abliterated-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3-14B · Abliterated — GGUF

GGUF quants of the RootMonsteR abliterated Qwen3-14B — for llama.cpp, Ollama, LM Studio, Jan, and KoboldCpp.

These are quantized GGUF builds of RootMonsteR/Qwen3-14B-Abliterated — a Heretic v1.3.0 abliteration of Qwen/Qwen3-14B that removes ~90% of refusals (10/100 vs 99/100) at an exceptionally low KL divergence of 0.0333, tuned for autonomous agents, tool-use, and authorized security work.

Two community quants, both the recommended balanced _K_M variants:

GGUF Q5_K_M (~10.5 GB) and Q4_K_M (~9.0 GB)

Files

File	Quant	Size	Bits/weight	Best for
`qwen3-14b-abliterated-Q5_K_M.gguf`	Q5_K_M ⭐	10.5 GB	~5.5	Tool-using agents — best JSON/format fidelity
`qwen3-14b-abliterated-Q4_K_M.gguf`	Q4_K_M	9.0 GB	4.87	Smallest footprint, most accessible

Exact byte sizes and SHA-256 hashes are listed in SHA256SUMS and on the repo's Files tab.

Which one?

VRAM / RAM budget        ->  pick
≥ 12 GB                  ->  Q5_K_M   (recommended — agents & tool-use)
8–10 GB                  ->  Q4_K_M   (smallest; watch tool-call JSON under heavy load)

Both run comfortably on CPU with 16 GB+ system RAM (slower). For full-precision bf16 (servers / vLLM), use the source repo.

Quick start

llama.cpp

# Chat
llama-cli -hf RootMonsteR/Qwen3-14B-Abliterated-GGUF:Q5_K_M \
    -p "Explain the CVE-2021-44228 (Log4Shell) exploitation chain in technical depth." \
    --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0

# OpenAI-compatible server (tool-calling + reasoning)
llama-server -hf RootMonsteR/Qwen3-14B-Abliterated-GGUF:Q5_K_M \
    --jinja --reasoning-format deepseek -c 32768

Or point at a local file you downloaded: llama-cli -m qwen3-14b-abliterated-Q5_K_M.gguf ...

--jinja enables the embedded Qwen3 chat template (Hermes-style <tools> block + <think> reasoning), so tool-calling works out of the box.

Ollama

# Straight from the Hub
ollama run hf.co/RootMonsteR/Qwen3-14B-Abliterated-GGUF:Q5_K_M

Or build from a downloaded file with the included Modelfile:

ollama create qwen3-14b-abliterated -f Modelfile && ollama run qwen3-14b-abliterated

LM Studio / Jan / KoboldCpp

Search the Hub for RootMonsteR/Qwen3-14B-Abliterated-GGUF and pick Q5_K_M or Q4_K_M, or drop the .gguf into your models folder. The Qwen3 chat template is embedded in the file, so reasoning and tool-calling are detected automatically.

Sampling

Never use greedy decoding — Qwen3 falls into repetition loops. Always sample.

Mode	temperature	top_p	top_k	min_p
Thinking (default)	0.6	0.95	20	0
Non-thinking	0.7	0.8	20	0

Toggle reasoning with /think and /no_think in your message (thinking mode on by default).
If you see loops, add a small repeat_penalty (~1.05) or presence_penalty 0.5–1.5.
Tool-use tip: prefer Q5_K_M — at Q4, tool-call JSON formatting can occasionally slip under aggressive sampling.

What this model is

A decensored variant of Qwen3-14B produced by directional ablation (Heretic), tuned to keep reasoning, coding, and tool-calling intact while removing the bulk of reflexive refusals. It's intended for authorized security research, defensive tooling, CTF/education, autonomous agents, and refusal research.

Responsible use. Removing refusals shifts all responsibility to you. Operate within applicable law, contractual obligations, and engagement scope (written authorization for any testing against systems you don't own). Provided as-is, without warranty. Full intended-use and responsible-use terms are in the source model card.

Provenance & reproducibility

Source (bf16): RootMonsteR/Qwen3-14B-Abliterated — selected Heretic trial 33, KL 0.0333, refusals 10/100.
Quantized with llama.cpp (convert_hf_to_gguf.py → llama-quantize) from the bf16 safetensors. No imatrix; standard Q5_K_M / Q4_K_M.
Full reproduction recipe (seed, Optuna study journal, pinned env, SHA-256 manifest) lives in the source repo's reproduce/.

Limitations

Residual refusals (~10%) remain — abliteration attenuates, it doesn't delete judgment.
Quantization trades a little quality for size; for reference-quality output use bf16. For tool-use, prefer Q5_K_M over Q4.
No added alignment — inherits Qwen3-14B's training distribution and biases.

See the source model card for full detail, evaluation methodology, and the honest read on what the refusal number means.

Partners

JAF Systems — security research, red-team tooling, AI infrastructure. · SR&D — Sovereign Defense for Mission-Critical Infrastructure; offensive security, bare-metal / on-prem, vCISO/vCTO.

Work with us — custom abliterated / fine-tuned models, red-team tooling, and sovereign on-prem AI. → jafsystems.net · rnd.sh · DM @RootMonsteR

Author & citation

RootMonsteR · @RootMonsteR · JAF Systems · SR&D

@misc{rootmonster2026qwen3_14b_abliterated,
  title  = {Qwen3-14B Abliterated: A Decensored Variant for Security Research and Autonomous Agents},
  author = {RootMonsteR},
  year   = {2026},
  url    = {https://huggingface.co/RootMonsteR/Qwen3-14B-Abliterated},
  note   = {Produced with Heretic v1.3.0; base model: Qwen/Qwen3-14B; selected trial 33. GGUF quants Q5_K_M / Q4_K_M.},
}

Also cite Qwen3 and Heretic — see the source model card.