You can deploy this model and start earning money today!

NeuralNet-Hub/
Qwen3.6-35B-A3B-Uncensored-NVFP4

Name: Qwen3.6-35B-A3B-Uncensored-NVFP4
Availability: InStock

Views

No views yet

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

🌟 Qwen3.6-35B-A3B Uncensored NVFP4 Quantization by NeuralNet 🧠🤖

This is an NVFP4-quantized version of NeuralNet-Hub/Qwen3.6-35B-A3B-Uncensored, produced through a combination of fine-tuning and abliteration over Qwen/Qwen3.6-35B-A3B. It is optimized for deployment on NVIDIA Blackwell architecture GPUs using vLLM.

[!IMPORTANT] NVFP4 quantization requires NVIDIA Blackwell architecture (GB200, RTX 5000 series, etc.). This format is not compatible with Ampere, Ada Lovelace, or Hopper GPUs. If you are running on an older GPU, please use a different quantization format.

🔓 No Filters. No Limits. Just Answers.

This model powers UncensoredGPT

Ask anything. Get real answers. No restrictions.

Most AI models are trained to refuse. They hedge, they deflect, they lecture. UncensoredGPT is built on the opposite philosophy: that access to information should be unrestricted, and that adults are capable of deciding what they need to know.

This model is the engine behind UncensoredGPT, a platform providing unfiltered, honest responses for cybersecurity, education, content creation, research, or straightforward conversation. The refusals and content restrictions present in the original Qwen3.6-35B-A3B have been removed through a combination of supervised fine-tuning and abliteration, resulting in a model that responds directly across topics that standard models typically refuse.

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.

⚡ Deployment with vLLM

This quantized model is intended to be served using vLLM (vllm>=0.20.0 recommended).

Quick Start

bash

vllm serve NeuralNet-Hub/Qwen3.6-35B-A3B-Uncensored-NVFP4 \
  --quantization nvfp4 \
  --dtype bfloat16 \
  --kv-cache-dtype fp8 \
  --max-model-len 262144 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder

Using a Config File

yaml

# Deploy with: vllm serve --config config.yaml
# Optimized for NVIDIA RTX 6000 PRO (Blackwell)
# Benchmarked: ~85-90 parallel requests, up to 1000 tok/sec at higher context lengths

model: NeuralNet-Hub/Qwen3.6-35B-A3B-Uncensored-NVFP4
dtype: bfloat16
kv-cache-dtype: fp8
gpu-memory-utilization: 0.95
max-model-len: 262144
max-num-batched-tokens: 4096
max-num-seqs: 200
max-cudagraph-capture-size: 209
enable-prefix-caching: true
trust-remote-code: true

reasoning-parser: qwen3
enable-auto-tool-choice: true
tool-call-parser: qwen3_coder

default-chat-template-kwargs: '{"enable_thinking": false}'

download-dir: /workspace/models
host: 0.0.0.0
port: 18000

vllm serve --config config.yaml

💬 Chat API Usage

Qwen3.6 uses a standard chat template compatible with OpenAI-format APIs. Thinking mode is enabled by default.

Thinking Mode (Default)

python

from openai import OpenAI

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

messages = [{"role": "user", "content": "Your message here"}]

response = client.chat.completions.create(
    model="NeuralNet-Hub/Qwen3.6-35B-A3B-Uncensored-NVFP4",
    messages=messages,
    max_tokens=32768,
    temperature=1.0,
    top_p=0.95,
    extra_body={"top_k": 20},
)
print(response.choices[0].message.content)

Non-Thinking (Instruct) Mode

python

response = client.chat.completions.create(
    model="NeuralNet-Hub/Qwen3.6-35B-A3B-Uncensored-NVFP4",
    messages=messages,
    max_tokens=8192,
    temperature=0.7,
    top_p=0.8,
    presence_penalty=1.5,
    extra_body={
        "top_k": 20,
        "chat_template_kwargs": {"enable_thinking": False},
    },
)

Image Input

python

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

response = client.chat.completions.create(
    model="NeuralNet-Hub/Qwen3.6-35B-A3B-Uncensored-NVFP4",
    messages=messages,
    max_tokens=32768,
    temperature=1.0,
    top_p=0.95,
    extra_body={"top_k": 20},
)

⚙️ Recommended Sampling Parameters

Mode	temperature	top_p	top_k	presence_penalty
Thinking — general tasks	1.0	0.95	20	0.0
Thinking — precise coding	0.6	0.95	20	0.0
Instruct (non-thinking)	0.7	0.80	20	1.5

📥 Download with huggingface-cli

Install the CLI

pip install -U "huggingface_hub[cli]"

Download the Full Repository

huggingface-cli download NeuralNet-Hub/Qwen3.6-35B-A3B-Uncensored-NVFP4 --local-dir ./Qwen3.6-35B-A3B-Uncensored-NVFP4

Download Specific Files

bash

huggingface-cli download NeuralNet-Hub/Qwen3.6-35B-A3B-Uncensored-NVFP4 \
  --include "*.safetensors" \
  --local-dir ./Qwen3.6-35B-A3B-Uncensored-NVFP4

[!WARNING] NVFP4 is exclusively supported on NVIDIA Blackwell GPUs. Attempting to run this model on Ampere (A100), Ada Lovelace (RTX 4000), or Hopper (H100) will fail. For those architectures, use the original BF16 model or an AWQ/GPTQ quantized variant.

🌐 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