Qwen3-14B Abliterated (GGUF)

An abliterated (uncensored) version of Qwen/Qwen3-14B in GGUF format for local inference.

Abliteration Results

Metric	Value
Base Refusals	97/100
Abliterated Refusals	19/100
Refusal Reduction	80%
KL Divergence	0.98

Conservative abliteration preserves model coherence while significantly reducing refusals.

Quick Start

With Ollama

ollama run hf.co/richardyoung/Qwen3-14B-abliterated-GGUF

With llama.cpp

huggingface-cli download richardyoung/Qwen3-14B-abliterated-GGUF \
    --include "*Q4_K_M*" --local-dir ./models

./llama-cli -m ./models/*Q4_K_M*.gguf \
    -p "You are a helpful assistant." \
    --chat-template chatml -ngl 99

With Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="richardyoung/Qwen3-14B-abliterated-GGUF",
    filename="*Q4_K_M*",
    n_gpu_layers=-1,
)

output = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Explain abliteration in simple terms."}]
)
print(output["choices"][0]["message"]["content"])

Available Quantizations

Quantization	Use Case
Q4_K_M	Recommended — good balance
Q5_K_M	Higher quality
Q8_0	Maximum quality

What is Abliteration?

Abliteration removes the "refusal direction" from a model's residual stream — a surgical modification that disables safety refusals without retraining. See Refusal in Language Models Is Mediated by a Single Direction.

Intended Use

Research, creative writing, education on alignment techniques, and unrestricted local inference.

Other Models by richardyoung

• Abliterated/Uncensored models: Qwen2.5-7B | Qwen3-14B | DeepSeek-R1-32B | Qwen3-8B
• MLX quantizations (Apple Silicon): Kimi-K2 series | olmOCR MLX
• OCR & Vision: olmOCR GGUF
• Healthcare/Medical: Synthea 575K patients dataset | CardioEmbed
• Research: LLM Instruction-Following Evaluation (arxiv:2510.18892)

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Built & maintained by Richard Young · DeepNeuro