Instructions to use ikarius/Qwen3-14B-Abliterated-NF4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ikarius/Qwen3-14B-Abliterated-NF4 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ikarius/Qwen3-14B-Abliterated-NF4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ikarius/Qwen3-14B-Abliterated-NF4")
model = AutoModelForCausalLM.from_pretrained("ikarius/Qwen3-14B-Abliterated-NF4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ikarius/Qwen3-14B-Abliterated-NF4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ikarius/Qwen3-14B-Abliterated-NF4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ikarius/Qwen3-14B-Abliterated-NF4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ikarius/Qwen3-14B-Abliterated-NF4

SGLang

How to use ikarius/Qwen3-14B-Abliterated-NF4 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ikarius/Qwen3-14B-Abliterated-NF4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ikarius/Qwen3-14B-Abliterated-NF4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ikarius/Qwen3-14B-Abliterated-NF4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ikarius/Qwen3-14B-Abliterated-NF4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ikarius/Qwen3-14B-Abliterated-NF4 with Docker Model Runner:
```
docker model run hf.co/ikarius/Qwen3-14B-Abliterated-NF4
```

Qwen3-14B-Abliterated-v2-nf4

Model Overview

This repository contains a quantized version (NF4, using BitsAndBytes) of the Qwen3-14B-Abliterated-v2 model.
The original model is an uncensored variant of Qwen/Qwen3-14B, created using the abliteration technique to remove refusal behaviors (see remove-refusals-with-transformers).

This quantization was performed by ikarius to reduce model size and enable efficient inference on consumer hardware, while preserving the uncensored capabilities of the base model.

Key Features:

Base Model: Qwen3-14B (abliterated for uncensoring)
Version: Abliterated-v2 (improved over v1)
Quantization: NF4 (4-bit NormalFloat via BitsAndBytes)
Parameters: 14 Billion
License: Refer to the original Qwen3 license (Apache 2.0 with additional terms); abliteration does not alter the license.
Intended Use: Research, experimentation, and creative applications.

Warning: This model is uncensored and may generate sensitive or harmful content—use responsibly.

Installation

Install the required dependencies:

pip install transformers torch bitsandbytes accelerate

Ensure you have a compatible CUDA setup for GPU acceleration.

(Optional) For CPU-only inference:bash

pip install optimum[exporters]

Usage

Load and run the model using Hugging Face Transformers.

Python Example


from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    device_map="auto",
    trust_remote_code=True,
)

Example inference

prompt = "Hello, how are you?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=128,
    temperature=0.7,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id,
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Inference Tips

VRAM: ~8–10 GB required for 14B NF4 on a single GPU. Batch Size: Start with 1. Thinking Mode: v2 supports step-by-step reasoning prompts. Streaming: Use TextStreamer for real-time output.

Quantization Details

Method: BitsAndBytes NF4 (normal float 4-bit) Quantizer: ikarius Benefits: ~75% size reduction vs BF16, minimal quality loss Trade-offs: Slight perplexity increase

Reproduce Quantization

from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

original_model = AutoModelForCausalLM.from_pretrained(
    "huihui-ai/Huihui-Qwen3-14B-abliterated-v2",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

quant_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

quantized_model = AutoModelForCausalLM.from_pretrained(
    "huihui-ai/Huihui-Qwen3-14B-abliterated-v2",
    quantization_config=quant_config,
    device_map="auto",
)

quantized_model.save_pretrained("Qwen3-14B-Abliterated-v2-nf4")
tokenizer.save_pretrained("Qwen3-14B-Abliterated-v2-nf4")

Limitations & Ethics

May amplify training data biases. Not suitable for production without alignment. For commercial use: review original licenses.

Contact

Open an issue or reach out to ikarius on Hugging Face.

Last updated: November 12, 2025

Original Model Credits-

Abliteration:huihui-ai

Support the project

Buy huihui-ai a coffee ☕

Base Model:Qwen/Qwen3-14B

Downloads last month: 116

Safetensors

Model size

15B params

Tensor type

F32

F16

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