- Libraries
- MLX
How to use erdemozkan/YOLO-Coder-8B with MLX:
# Download the model from the Hub
pip install huggingface_hub[hf_xet]
hf download erdemozkan/YOLO-Coder-8B --local-dir YOLO-Coder-8B
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use erdemozkan/YOLO-Coder-8B 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 erdemozkan/YOLO-Coder-8B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf erdemozkan/YOLO-Coder-8B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf erdemozkan/YOLO-Coder-8B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf erdemozkan/YOLO-Coder-8B: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 erdemozkan/YOLO-Coder-8B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf erdemozkan/YOLO-Coder-8B: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 erdemozkan/YOLO-Coder-8B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf erdemozkan/YOLO-Coder-8B:Q4_K_M
Use Docker
docker model run hf.co/erdemozkan/YOLO-Coder-8B:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use erdemozkan/YOLO-Coder-8B with Ollama:
ollama run hf.co/erdemozkan/YOLO-Coder-8B:Q4_K_M
- Unsloth Desktop
- Pi
How to use erdemozkan/YOLO-Coder-8B with Pi:
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "erdemozkan/YOLO-Coder-8B"
Configure the model in Pi
# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
"providers": {
"mlx-lm": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "erdemozkan/YOLO-Coder-8B"
}
]
}
}
}Run Pi
# Start Pi in your project directory:
pi
- Docker Model Runner
How to use erdemozkan/YOLO-Coder-8B with Docker Model Runner:
docker model run hf.co/erdemozkan/YOLO-Coder-8B:Q4_K_M
- Lemonade
How to use erdemozkan/YOLO-Coder-8B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull erdemozkan/YOLO-Coder-8B:Q4_K_M
Run and chat with the model
lemonade run user.YOLO-Coder-8B-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use erdemozkan/YOLO-Coder-8B with Hermes Agent:
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "erdemozkan/YOLO-Coder-8B"
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 erdemozkan/YOLO-Coder-8B
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use erdemozkan/YOLO-Coder-8B with OpenClaw:
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "erdemozkan/YOLO-Coder-8B"
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 "erdemozkan/YOLO-Coder-8B" \
--custom-provider-id mlx-lm \
--custom-compatibility openai \
--custom-text-input \
--accept-risk \
--skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
