Instructions to use outsourc-e/Qwen3.8-27B-Unleashed-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use outsourc-e/Qwen3.8-27B-Unleashed-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 outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M # Run inference directly in the terminal: llama cli -hf outsourc-e/Qwen3.8-27B-Unleashed-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 outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M # Run inference directly in the terminal: llama cli -hf outsourc-e/Qwen3.8-27B-Unleashed-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 outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf outsourc-e/Qwen3.8-27B-Unleashed-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 outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M
Use Docker
docker model run hf.co/outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M
- LM Studio
- Jan
- vLLM
How to use outsourc-e/Qwen3.8-27B-Unleashed-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "outsourc-e/Qwen3.8-27B-Unleashed-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": "outsourc-e/Qwen3.8-27B-Unleashed-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M
- Ollama
How to use outsourc-e/Qwen3.8-27B-Unleashed-GGUF with Ollama:
ollama run hf.co/outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M
- Unsloth Desktop
- Pi
How to use outsourc-e/Qwen3.8-27B-Unleashed-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use outsourc-e/Qwen3.8-27B-Unleashed-GGUF with Docker Model Runner:
docker model run hf.co/outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M
- Lemonade
How to use outsourc-e/Qwen3.8-27B-Unleashed-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M
Run and chat with the model
lemonade run user.Qwen3.8-27B-Unleashed-GGUF-UD-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use outsourc-e/Qwen3.8-27B-Unleashed-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 outsourc-e/Qwen3.8-27B-Unleashed-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 outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use outsourc-e/Qwen3.8-27B-Unleashed-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf outsourc-e/Qwen3.8-27B-Unleashed-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 "outsourc-e/Qwen3.8-27B-Unleashed-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"
q3 is surprisingly really good.
I've been using it for a lot of research and, you know, other projects and stuff like that, and it's actually been pretty good. It does do a lot of thinking, but that's what makes this model, I guess, so good is the thinking. It's faster with no MTP on two RTX 3080 20gb cards. I get about 1300 prompt processing and about 25 to 30 token generation. With MTP, it's more consistent at 30 to 35 token generation, but at the same time, I'm getting 800 prompt processing. So MTP not really worth it.
Also, no refusals, which is nice to be able to work on content creation in certain niches and stuff like that.
I feel exactly the same. This IQ3-XXS model is the best one I have ever tested on my 16GB single GPU in terms of both size and quality.
I also specifically recommended it in my video, and it's totally worth your time to use.
Hey is it worth using the Q3_K_XL at q4 kv cache or the iq3_xs at q8 kv cache? I keep reading comments about kv cache affecting qwen 3.8 alot yet they state the benchnarks with q4 kv cache which is interesting