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ikarius/Qwen3-32B-Abliterated-NF4
Qwen3-32B-Abliterated-NF4 is a text generation model from ikarius. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
<p align="center" <img src="https://huggingface.co/ikarius/Qwen3-32B-Abliterated-nf4/resolve/main/neuroforgesdxl.png?download=true" alt="Neuroforge AI Lab" width="100%"/ <br <em<strongNeuroforge</strong – Where uncens…
Downloads · 30 days
12
3% of all-time downloads
All-time downloads
413
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32.8B
19.2 GB on disk
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.safetensors19.2 GB · 100%
How the weights are stored.
U832.2B · 95%
From the Hugging Face model README

</div>"I PRESSSSILERE – NO FILTER, NO FEAR, ALL TRUTH!"
— Telly The Pressssilere, Chief Truth Officer at Neuroforge
NF4-quantized version of huihui-ai/Huihui-Qwen3-32B-abliterated
Uncensored 32B model (abliterated) → 4-bit NF4 by ikarius
Warning: Uncensored – may generate harmful or sensitive content. Use responsibly.
| Base | Qwen/Qwen3-32B |
| Abliteration | by huihui-ai |
| Quantization | NF4 (BitsAndBytes) |
| VRAM | ~16–20 GB (single GPU) |
| License | Apache 2.0 + Qwen terms |
pip install transformers torch bitsandbytes accelerate
pip install optimum[exporters]
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
MODEL_ID = "ikarius/Qwen3-32B-Abliterated-nf4"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
device_map="auto",
trust_remote_code=True
)
prompt = "Explain quantum entanglement in simple terms:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.7)
print(tokenizer.decode(output[0], skip_special_tokens=True))
Tips:
Start with batch size 1
Use TextStreamer for real-time output
supports thinking mode and step-by-step reasoning
## Reproduce Quantization
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
quantized = AutoModelForCausalLM.from_pretrained(
"huihui-ai/Huihui-Qwen3-32B-abliterated",
quantization_config=BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4"),
device_map="auto"
)
quantized.save_pretrained("Qwen3-32B-Abliterated-nf4")
Notes
May amplify training data biases Not suitable for production without alignment Commercial use: review original license
Updated: November 13, 2025
Abliteration:huihui-ai
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Base:Qwen/Qwen3-32B