- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use xStagg/radio-finetuned 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 xStagg/radio-finetuned:BF16
# Run inference directly in the terminal:
llama cli -hf xStagg/radio-finetuned:BF16
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf xStagg/radio-finetuned:BF16
# Run inference directly in the terminal:
llama cli -hf xStagg/radio-finetuned:BF16
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 xStagg/radio-finetuned:BF16
# Run inference directly in the terminal:
./llama-cli -hf xStagg/radio-finetuned:BF16
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 xStagg/radio-finetuned:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf xStagg/radio-finetuned:BF16
Use Docker
docker model run hf.co/xStagg/radio-finetuned:BF16
- LM Studio
- Jan
- Ollama
How to use xStagg/radio-finetuned with Ollama:
ollama run hf.co/xStagg/radio-finetuned:BF16
- Unsloth Desktop
- Pi
How to use xStagg/radio-finetuned with Pi:
Start the llama.cpp server
# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf xStagg/radio-finetuned:BF16
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": "xStagg/radio-finetuned:BF16"
}
]
}
}
}Run Pi
# Start Pi in your project directory:
pi
- Docker Model Runner
How to use xStagg/radio-finetuned with Docker Model Runner:
docker model run hf.co/xStagg/radio-finetuned:BF16
- Lemonade
How to use xStagg/radio-finetuned with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull xStagg/radio-finetuned:BF16
Run and chat with the model
lemonade run user.radio-finetuned-BF16
List all available models
lemonade list
- Hermes Agent
How to use xStagg/radio-finetuned with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf xStagg/radio-finetuned:BF16
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 xStagg/radio-finetuned:BF16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use xStagg/radio-finetuned with OpenClaw:
Start the llama.cpp server
# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf xStagg/radio-finetuned:BF16
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 "xStagg/radio-finetuned:BF16" \
--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"
