Instructions to use cafonez/Qwen3.8-27B-ROCmI4-MTP-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 cafonez/Qwen3.8-27B-ROCmI4-MTP-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 cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF:Q4_0_ROCMI # Run inference directly in the terminal: llama cli -hf cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF:Q4_0_ROCMI
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF:Q4_0_ROCMI # Run inference directly in the terminal: llama cli -hf cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF:Q4_0_ROCMI
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 cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF:Q4_0_ROCMI # Run inference directly in the terminal: ./llama-cli -hf cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF:Q4_0_ROCMI
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 cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF:Q4_0_ROCMI # Run inference directly in the terminal: ./build/bin/llama-cli -hf cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF:Q4_0_ROCMI
Use Docker
docker model run hf.co/cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF:Q4_0_ROCMI
- LM Studio
- Jan
- vLLM
How to use cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cafonez/Qwen3.8-27B-ROCmI4-MTP-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": "cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF:Q4_0_ROCMI
- Ollama
How to use cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF with Ollama:
ollama run hf.co/cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF:Q4_0_ROCMI
- Unsloth Desktop
- Pi
How to use cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF:Q4_0_ROCMI
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": "cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF:Q4_0_ROCMI" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF with Docker Model Runner:
docker model run hf.co/cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF:Q4_0_ROCMI
- Lemonade
How to use cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF:Q4_0_ROCMI
Run and chat with the model
lemonade run user.Qwen3.8-27B-ROCmI4-MTP-GGUF-Q4_0_ROCMI
List all available models
lemonade list
- Hermes Agent
How to use cafonez/Qwen3.8-27B-ROCmI4-MTP-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 cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF:Q4_0_ROCMI
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 cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF:Q4_0_ROCMI
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF:Q4_0_ROCMI
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 "cafonez/Qwen3.8-27B-ROCmI4-MTP-GGUF:Q4_0_ROCMI" \ --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"
Benchmarking ROCmI4 MTP on Strix Halo (gfx1151) — access request
Hey Charles — I'm really interested in testing your ROCmI4 MTP model on my hardware.
My setup: Beelink GTR 9 Pro with Radeon 8060S (Strix Halo, gfx1151), 128GB VRAM.
I'm a developer building a local AI stack and want to test whether this quantization actually delivers the ~3.5x speedup you've documented. Would you grant read access so I can benchmark it on my hardware?
Thanks!
Hey! The ROCmI4 MTP repo is public, so you should already have full read/download access to the model files without me granting any additional permissions. You should be able to download the GGUFs directly from the repo. If Hugging Face is giving you an access error, send me the error you're seeing and I'll take a look.