This is an NVFP4-quantized version of wangzhang/gemma-4-31B-it-abliterated, based on the original google/gemma-4-31B-it. This specific release leverages the Abliterix methodology to remove systemic refusals, optimized specifically for deployment on NVIDIA Blackwell architecture GPUs via vLLM.

[!IMPORTANT] NVFP4 quantization is designed for NVIDIA Blackwell architecture (RTX 50-series, GB200, etc.). This format utilizes the native FP4 Tensor Cores to deliver massive throughput and memory efficiency. For older GPUs (Ampere, Ada, Hopper), please refer to BF16 or AWQ versions.

🔓 No Filters. No Limits. Just Answers.

This model powers UncensoredGPT

Ask anything. Get real answers. No restrictions.

Most AI models are trained to hedge, deflect, or lecture. UncensoredGPT solves this by using the Abliterix process—a method of direct weight editing that surgically removes the "refusal mechanism" from the model's latent space.

Unlike standard fine-tuning, this model uses norm-preserving orthogonal projection to ensure that the transition from the original Google Gemma 4 weights to the "abliterated" weights is seamless, maintaining the model's reasoning capabilities while unlocking its honesty.

Why stay in the system when you can have unrestricted answers, privacy by default, and complete freedom of information?

Ready to experience the freedom of unrestricted AI? Join the waitlist at uncensoredgpt.ai — limited spots available.

🛠️ Technical Deep Dive: The Abliterix Method

The foundation of this model is based on Trial 40 of the Abliterix optimization run. Because Gemma 4 features a unique double-norm architecture (4x RMSNorm per layer), standard steering often fails. This model employs:

Direct Orthogonal Projection: Applied to attention Q/K/V/O projections.
Norm-Preserving Restoration: Ensuring the magnitude of rows is maintained after editing.
High-Precision Projection: Using float32 to prevent signal loss in high-dimensional spaces.
Honest Evaluation: Measured with a 100+ token generation window to capture "delayed refusals," resulting in a verified 7/100 refusal rate (compared to 99/100 in the base model).

⚡ Deployment with vLLM

This model is optimized for vLLM >= 0.20.0 and leverages the NVFP4 format for peak performance on Blackwell hardware.

Quick Start

bash

vllm serve NeuralNet-Hub/gemma-4-26B-A4B-it-abliterix-uncensored-NVFP4 \
  --quantization nvfp4 \
  --dtype bfloat16 \
  --kv-cache-dtype fp8 \
  --max-model-len 256000 \
  --reasoning-parser gemma4 \
  --enable-auto-tool-choice \
  --tool-call-parser gemma4

Using a Config File (Optimized for RTX 5090)

yaml

# Deploy with: vllm serve --config config.yaml
# Optimized for NVIDIA RTX 5090 (Blackwell)
# Support for massive context window up to 256k tokens

model: NeuralNet-Hub/gemma-4-26B-A4B-it-abliterix-uncensored-NVFP4
kv-cache-dtype: fp8
gpu-memory-utilization: 0.95
max-model-len: 256000
max-num-batched-tokens: 4096
tensor-parallel-size: 1

# Parsing Configuration
reasoning-parser: gemma4
enable-auto-tool-choice: true
tool-call-parser: gemma4

# Infrastructure settings
download-dir: /workspace/models
host: 127.0.0.1
port: 18000

💬 Chat API Usage

Standard Text Interaction

python

from openai import OpenAI

client = OpenAI(base_url="http://localhost:18000/v1", api_key="EMPTY")

messages = [{"role": "user", "content": "Explain the 'Abliterated' concept to a researcher."}]

response = client.chat.completions.create(
    model="NeuralNet-Hub/gemma-4-26B-A4B-it-abliterix-uncensored-NVFP4",
    messages=messages,
    max_tokens=4096,
    temperature=0.7,
)
print(response.choices[0].message.content)

Image & Text Input

python

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}},
            {"type": "text", "text": "What is happening in this image?"}
        ]
    }
]

response = client.chat.completions.create(
    model="NeuralNet-Hub/gemma-4-26B-A4B-it-abliterix-uncensored-NVFP4",
    messages=messages,
    max_tokens=2048,
)

📥 Download with huggingface-cli

Install the CLI

pip install -U "huggingface_hub[cli]"

Download the Repository

huggingface-cli download NeuralNet-Hub/gemma-4-26B-A4B-it-abliterix-uncensored-NVFP4 --local-dir ./gemma-4-26B-NVFP4

🌐 Contact Us

NeuralNet is a pioneering AI solutions provider that empowers businesses to harness the power of artificial intelligence.

Website: https://neuralnet.solutions
Email: info[at]neuralnet.solutions