Instructions to use iRunStudio/Meltdown_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 iRunStudio/Meltdown_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 iRunStudio/Meltdown_GGUF # Run inference directly in the terminal: llama cli -hf iRunStudio/Meltdown_GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf iRunStudio/Meltdown_GGUF # Run inference directly in the terminal: llama cli -hf iRunStudio/Meltdown_GGUF
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 iRunStudio/Meltdown_GGUF # Run inference directly in the terminal: ./llama-cli -hf iRunStudio/Meltdown_GGUF
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 iRunStudio/Meltdown_GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf iRunStudio/Meltdown_GGUF
Use Docker
docker model run hf.co/iRunStudio/Meltdown_GGUF
- LM Studio
- Jan
- vLLM
How to use iRunStudio/Meltdown_GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "iRunStudio/Meltdown_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": "iRunStudio/Meltdown_GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/iRunStudio/Meltdown_GGUF
- Ollama
How to use iRunStudio/Meltdown_GGUF with Ollama:
ollama run hf.co/iRunStudio/Meltdown_GGUF
- Unsloth Studio
How to use iRunStudio/Meltdown_GGUF 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 iRunStudio/Meltdown_GGUF 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 iRunStudio/Meltdown_GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for iRunStudio/Meltdown_GGUF to start chatting
- Pi
How to use iRunStudio/Meltdown_GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf iRunStudio/Meltdown_GGUF
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": "iRunStudio/Meltdown_GGUF" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use iRunStudio/Meltdown_GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf iRunStudio/Meltdown_GGUF
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 "iRunStudio/Meltdown_GGUF" \ --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 iRunStudio/Meltdown_GGUF with Docker Model Runner:
docker model run hf.co/iRunStudio/Meltdown_GGUF
- Lemonade
How to use iRunStudio/Meltdown_GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull iRunStudio/Meltdown_GGUF
Run and chat with the model
lemonade run user.Meltdown_GGUF-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use iRunStudio/Meltdown_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 iRunStudio/Meltdown_GGUF
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 iRunStudio/Meltdown_GGUF
Run Hermes
hermes
- Atomic Chat
| # Hugging Face upload checklist | |
| Upload everything in this folder to your HF model repo (e.g. `your-user/meltdown-q8`). | |
| ## Files in `hf/` (ready now) | |
| | File | Size (approx) | Required | | |
| |---|---|---| | |
| | `README.md` | small | Yes — model card | | |
| | `system_prompt.txt` | small | Yes | | |
| | `config.recommended.json` | small | Yes | | |
| | `benchmark_comparison.png` | small | Yes | | |
| | `agent_category_chart.png` | small | Yes | | |
| | `local_vs_cloud.png` | small | Yes | | |
| | `eval_standard.json` | small | Recommended | | |
| | `eval_agent.json` | small | Recommended | | |
| | `frontier_reference.json` | small | Recommended | | |
| | `.gitattributes` | tiny | Yes (enables GGUF LFS) | | |
| ## Add before upload | |
| | File | Source | Size | | |
| |---|---|---| | |
| | `Meltdown_Q8.gguf` | `..\Meltdown_Q8.gguf` | ~3.1 GB | | |
| ```powershell | |
| Copy-Item "..\Meltdown_Q8.gguf" ".\Meltdown_Q8.gguf" | |
| ``` | |
| ## Upload commands | |
| From this `hf/` folder: | |
| ```powershell | |
| # One-time: install HF CLI + git-lfs | |
| pip install huggingface_hub | |
| git lfs install | |
| # Create repo and push (replace YOUR_USER/meltdown-q8) | |
| huggingface-cli repo create meltdown-q8 --type model | |
| git init | |
| git lfs track "*.gguf" | |
| git add . | |
| git commit -m "Add Meltdown Q8 GGUF and model card" | |
| git remote add origin https://huggingface.co/YOUR_USER/meltdown-q8 | |
| git push origin main | |
| ``` | |
| Or drag-and-drop all files in the HF web UI (enable Git LFS for the `.gguf`). | |
| ## Do not upload | |
| - `benchmarks/` scripts and logs (dev-only) | |
| - `terminals/`, `mcps/`, `agent-tools/` | |
| - Training data (`meltdown_ape_eval50.jsonl`) |