Instructions to use rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX 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 rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX 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 rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX:Q4_0_ROCMFP # Run inference directly in the terminal: llama cli -hf rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX:Q4_0_ROCMFP
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX:Q4_0_ROCMFP # Run inference directly in the terminal: llama cli -hf rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX:Q4_0_ROCMFP
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 rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX:Q4_0_ROCMFP # Run inference directly in the terminal: ./llama-cli -hf rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX:Q4_0_ROCMFP
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 rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX:Q4_0_ROCMFP # Run inference directly in the terminal: ./build/bin/llama-cli -hf rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX:Q4_0_ROCMFP
Use Docker
docker model run hf.co/rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX:Q4_0_ROCMFP
- LM Studio
- Jan
- Ollama
How to use rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX with Ollama:
ollama run hf.co/rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX:Q4_0_ROCMFP
- Unsloth Studio
How to use rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX 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 rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX 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 rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX to start chatting
- Pi
How to use rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX:Q4_0_ROCMFP
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": "rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX:Q4_0_ROCMFP" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX:Q4_0_ROCMFP
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 "rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX:Q4_0_ROCMFP" \ --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"
- Docker Model Runner
How to use rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX with Docker Model Runner:
docker model run hf.co/rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX:Q4_0_ROCMFP
- Lemonade
How to use rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX:Q4_0_ROCMFP
Run and chat with the model
lemonade run user.protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX-Q4_0_ROCMFP
List all available models
lemonade list
- Hermes Agent
How to use rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX:Q4_0_ROCMFP
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 rcmorano/protolabsAI-ThinkingCap-Qwen3.6-27B-MTP-ROCMFPX:Q4_0_ROCMFP
Run Hermes
hermes
- Atomic Chat
Readme?
Please allocate a few tokens to provide a comprehensive README with precise instructions, benchmarks, and other valuable resources for each release.
Any tips and tricks to manage the models will be welcome!
Grazie per il tuo lavoro.
hey, sorry, I missed this notification! You can essentially follow upstream model recommendations. This applies to all my ROCmFPX quantizations, they do not require any special configuration, they are mere requantz from the [B]F16 original models into ROCm-optimized formats!