Instructions to use JonathanColetti/Qwen3.8-27B-Uncensored-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use JonathanColetti/Qwen3.8-27B-Uncensored-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 JonathanColetti/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf JonathanColetti/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf JonathanColetti/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf JonathanColetti/Qwen3.8-27B-Uncensored-GGUF: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 JonathanColetti/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf JonathanColetti/Qwen3.8-27B-Uncensored-GGUF: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 JonathanColetti/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf JonathanColetti/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M

Use Docker

docker model run hf.co/JonathanColetti/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use JonathanColetti/Qwen3.8-27B-Uncensored-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JonathanColetti/Qwen3.8-27B-Uncensored-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": "JonathanColetti/Qwen3.8-27B-Uncensored-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/JonathanColetti/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M

Ollama
How to use JonathanColetti/Qwen3.8-27B-Uncensored-GGUF with Ollama:
```
ollama run hf.co/JonathanColetti/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M
```

Unsloth Studio

How to use JonathanColetti/Qwen3.8-27B-Uncensored-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 JonathanColetti/Qwen3.8-27B-Uncensored-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 JonathanColetti/Qwen3.8-27B-Uncensored-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for JonathanColetti/Qwen3.8-27B-Uncensored-GGUF to start chatting

How to use JonathanColetti/Qwen3.8-27B-Uncensored-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf JonathanColetti/Qwen3.8-27B-Uncensored-GGUF: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": "JonathanColetti/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use JonathanColetti/Qwen3.8-27B-Uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/JonathanColetti/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M
```

Lemonade

How to use JonathanColetti/Qwen3.8-27B-Uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull JonathanColetti/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-27B-Uncensored-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use JonathanColetti/Qwen3.8-27B-Uncensored-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 JonathanColetti/Qwen3.8-27B-Uncensored-GGUF: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 JonathanColetti/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use JonathanColetti/Qwen3.8-27B-Uncensored-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf JonathanColetti/Qwen3.8-27B-Uncensored-GGUF: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 "JonathanColetti/Qwen3.8-27B-Uncensored-GGUF: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"

New discussion

Resources

View closed (1)

F16 or BF16 version

#17 opened about 12 hours ago by

davenetdev

IQ2 is morally bankrupt. I'd rather use Qwen 3.5 9B over this

#16 opened 3 days ago by

liem10936

Qwen3.8-27B-Uncensored-draft-Q8_0.gguf 模型启动不了

➕ 3

#15 opened 3 days ago by

aifengadaAda

Wow, this is the only uncensored model that doesn't break vision

#14 opened 4 days ago by

vulcan4d

lm studio performance problems

#13 opened 4 days ago by

m1k0la

靠这些知识怎么去转钱钱啊电话1898 4321 449

#12 opened 5 days ago by

wu9293

Basline bf16 perplexity?

#11 opened 6 days ago by

CrocoCpp

IQ4_XS pure quant

#10 opened 6 days ago by

ydmhmhm

Thank you, currently the only model which works with -sm=tensor in llama.cpp

❤️ 2

#8 opened 7 days ago by

iskrid

Vision Files?

#7 opened 7 days ago by

derek-wh

is it good for vibe coding?(virus,log stealer,etc)

#6 opened 7 days ago by

Mackenzie9

No Q2? :(

#5 opened 8 days ago by

TheWegemann

Awesome work! Built optimized quants for it

🔥 3

#4 opened 8 days ago by

minyor25

I was still trying to break

🚀 1

#3 opened 8 days ago by

linjian257

It's so good work with agent

❤️ 1

#2 opened 8 days ago by

bux1ansh4n

Good shit thanks yo!

❤️ 15

#1 opened 9 days ago by

BotsOne

F16 or BF16 version

IQ2 is morally bankrupt. I'd rather use Qwen 3.5 9B over this

Qwen3.8-27B-Uncensored-draft-Q8_0.gguf 模型启动不了

Wow, this is the only uncensored model that doesn't break vision

lm studio performance problems

靠这些知识怎么去转钱钱啊 电话1898 4321 449

Basline bf16 perplexity?

IQ4_XS pure quant

Thank you, currently the only model which works with -sm=tensor in llama.cpp

Vision Files?

is it good for vibe coding?(virus,log stealer,etc)

No Q2? :(

Awesome work! Built optimized quants for it

I was still trying to break

It's so good work with agent

Good shit thanks yo!

靠这些知识怎么去转钱钱啊电话1898 4321 449