Text Generation
GGUF
English
Chinese
uncensored
abliterated
qwen3.8
imatrix
dynamic-quant
conversational
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"
My findings for this setup.
#1
by s1arsky - opened
Interesting release. I will use it now instead of Unsloth Q4 UD equivalent, for now. I use 3090 single GPU.
My findings during testing this model Q3 UD XL with the recommended settings from its description page:
- --cache-reuse 256 \ is a no-op on qwen35 (Qwen says). Fact is that I get: 'W srv load_model: cache_reuse is not supported by this context, it will be disabled'. On top of that - cache reuse is not supported when multimodal.
- Dflash2 doesnt work with with vision. the spiritbun repo linked here - is missing the draft model. z-lab Q8 draft of DFlash2 works fine as replacement. Q8 draft is significantly faster than when I used Q2 draft. I do not look at acceptance rate because in my experience that doesn't translate to T/s speed llama logs.
- MTP is build-in and works with vision. Despite showing slightly less acceptance, I find it slightly faster than DFlash2 (for me) in terms of T/s (--spec-type draft-mtp --spec-draft-n-max 3 -ngld 99 )
- Without setting --parallel 1 \ there is OOM at max context
- I achieve speed of 30-60 T/s with MTP or DFlash2 with Q3_UD_XL Unleashed.
- Uncensor is flawless for me (with reasoning on).
- Vision with this model is MUCH faster than unsloth Q4. In fact it is first local vision (dense model) that has vision decoding speed I deem usable.
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