Instructions to use xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF",
	filename="Qwen3.6-35B-A3B-Uncensored-xCloud-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
llama cli -hf xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF:UD-Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
llama cli -hf xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF:UD-Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF:UD-Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF:UD-Q4_K_M

Use Docker

docker model run hf.co/xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF:UD-Q4_K_M

LM Studio
Jan

vLLM

How to use xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF:UD-Q4_K_M

Ollama
How to use xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF with Ollama:
```
ollama run hf.co/xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF:UD-Q4_K_M
```

Unsloth Studio

How to use xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF to start chatting

How to use xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF:UD-Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF:UD-Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF:UD-Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF:UD-Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF:UD-Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF:UD-Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF with Docker Model Runner:
```
docker model run hf.co/xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF:UD-Q4_K_M
```

Lemonade

How to use xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF:UD-Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF-UD-Q4_K_M

List all available models

lemonade list

Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF

云碩科技 · xCloudinfo　·　系列：無審查 · Abliterated / Uncensored

⚠️ 經 abliteration（aggressive） 處理，會比原版更少拒答。請僅在你被授權的範圍內使用。

xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud 的 GGUF（llama.cpp / Ollama） 量化版本。

檔案	量化	大小
`…-Q6_K.gguf`	Q6_K（接近無損）	≈ 28 GB
`…-Q4_K_M.gguf`	Q4_K_M（部署首選）	≈ 21 GB

純文字 GGUF（不含 mmproj）。需要圖文請用 merged safetensors 版。

用法

llama-server -m Qwen3.6-35B-A3B-Uncensored-xCloud-Q4_K_M.gguf -c 8192 -ngl 99

原理

依 Arditi et al. (2024)，以權重正交化移除殘差流拒絕方向（91 矩陣含 256-expert MoE、strength 1.0），不重訓。詳見 merged 版模型卡。

用途與責任

設計用途：降低過度拒絕，供授權範圍內研究與應用。
已移除安全拒絕傾向，使用者須自行加上安全防護與輸出審查，並對用途與後果負完全責任；不得用於有害或違法用途。

授權與來源

基底 Qwen/Qwen3.6-35B-A3B：Apache-2.0；本衍生同以 Apache-2.0 釋出。

由云碩科技 xCloudinfo 於自有 AI 算力資源池製作。

Downloads last month: 708

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

6-bit

Model tree for xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud

Quantized

(1)

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Collection including xCloudinfo/Qwen3.6-35B-A3B-Uncensored-xCloud-GGUF

Alibaba Qwen3.6 · 云碩衍生模型

Collection

基於 Alibaba Qwen3.6-35B-A3B（MoE 多模態）的云碩衍生模型，含 GGUF 量化。 • 2 items • Updated 3 days ago