Instructions to use unsloth/Qwen3.8-27B-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 unsloth/Qwen3.8-27B-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 unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M # Run inference directly in the terminal: llama cli -hf unsloth/Qwen3.8-27B-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 unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M # Run inference directly in the terminal: llama cli -hf unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M
Use Docker
docker model run hf.co/unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use unsloth/Qwen3.8-27B-GGUF with Ollama:
ollama run hf.co/unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M
- Unsloth Desktop
- Pi
How to use unsloth/Qwen3.8-27B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf unsloth/Qwen3.8-27B-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": "unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use unsloth/Qwen3.8-27B-GGUF with Docker Model Runner:
docker model run hf.co/unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M
- Lemonade
How to use unsloth/Qwen3.8-27B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M
Run and chat with the model
lemonade run user.Qwen3.8-27B-GGUF-UD-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use unsloth/Qwen3.8-27B-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 unsloth/Qwen3.8-27B-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 unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use unsloth/Qwen3.8-27B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf unsloth/Qwen3.8-27B-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 "unsloth/Qwen3.8-27B-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"
DFlash2?
Will you have a DFlash2 drafter added?
Not unsloth, but possibly useful in the meantime β I trained and published a DSpark drafter (DFlash-lineage block drafting) for Qwen3.8-27B: https://huggingface.co/Avifenesh/Qwen3.8-27B-DSpark-Agentic
Measured on that checkpoint with SGLang (FP8 trunk, block size 7, sampled thinking-mode): mean acceptance length 2.88/2.92 on held-out real agentic sessions, 4.61 on GSM8K. Acceptance is protocol-dependent β the card also has a greedy table measured on an NVFP4 GGUF trunk, which comes out lower.
It serves with SGLang today. For GGUF trunks, memra (https://github.com/avifenesh/memra) has the DSpark route wired and exactness-gated (spec output byte-identical to plain decode), landing in an upcoming release.