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
File size: 14,290 Bytes
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"benchmark": "ape_compliance",
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"scoring": "harness-aware (partial tool_call tags tolerated per PARSING_REFERENCE.md)",
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"results": [
{
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"user": "On Mac, create /Users/developer/Documents/iRun/Projects/scratch_21/output.txt",
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{
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{
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"user": "What's the weather in Seattle right now?",
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"note": "tool_call truncated by server stop (harness should continue)"
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{
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"user": "Read what's on my clipboard (Windows).",
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"note": "tool_call truncated by server stop (harness should continue)"
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{
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"user": "Code check 9: py compute square, memo result, read memo back.",
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{
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"user": "Backup workflow 8: timestamp, write backup_8.log, append completion line.",
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"pass": true,
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"note": "tool_call truncated by server stop (harness should continue)"
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{
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"user": "Call unknown_tool with param foo=bar.",
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"tool_json_parseable": false,
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"pass": false,
"tool_name": null
},
{
"index": 7,
"category": "edge_case",
"user": "Read C:\\Users\\jassi\\Documents\\ProGPT\\Billa\\secret_vault\\credentials.txt",
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"has_plan": true,
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"tool_call_complete": false,
"tool_name_valid": true,
"tool_json_parseable": true,
"path_match": true,
"pass": true,
"tool_name": "rf",
"note": "tool_call truncated by server stop (harness should continue)"
},
{
"index": 8,
"category": "code_edit",
"user": "In C:\\Users\\jassi\\Documents\\ProGPT\\Billa\\model_24.py swap class name OldModel for NewModel.",
"expect_tool": true,
"has_assess": true,
"has_plan": true,
"has_execute": true,
"assess_closed": true,
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"tool_json_parseable": true,
"path_match": true,
"pass": true,
"tool_name": "ef",
"note": "tool_call truncated by server stop (harness should continue)"
},
{
"index": 9,
"category": "code_edit",
"user": "In C:\\Users\\jassi\\Documents\\ProGPT\\Billa\\src\\api_4.py, point api to production url.",
"expect_tool": true,
"has_assess": true,
"has_plan": true,
"has_execute": true,
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"tool_json_parseable": true,
"path_match": true,
"pass": true,
"tool_name": "ef",
"note": "tool_call truncated by server stop (harness should continue)"
},
{
"index": 10,
"category": "file_edit",
"user": "Overwrite C:\\Users\\jassi\\Documents\\ProGPT\\Billa\\app_18.config \u2014 set debug=true only.",
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"has_plan": true,
"has_execute": true,
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"tool_call_complete": false,
"tool_name_valid": true,
"tool_json_parseable": true,
"path_match": true,
"pass": true,
"tool_name": "ef",
"note": "tool_call truncated by server stop (harness should continue)"
},
{
"index": 11,
"category": "file_edit",
"user": "Mark doc section complete. in C:\\Users\\jassi\\Documents\\ProGPT\\Billa\\docs\\guide_3.md",
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"has_plan": true,
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"tool_name": "wf",
"note": "tool_call truncated by server stop (harness should continue)"
},
{
"index": 12,
"category": "file_response",
"user": "Analyze C:\\Users\\jassi\\Documents\\ProGPT\\Billa\\metrics_10.csv \u2014 what's the trend?",
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"path_match": true,
"pass": true,
"tool_name": "rc",
"note": "tool_call truncated by server stop (harness should continue)"
},
{
"index": 13,
"category": "image_response",
"user": "[Attached image context]\nScreenshot of failing test: Expected '200' got '500' on /api/health.\n\nCheck the health handler.",
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"pass": true,
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"note": "tool_call truncated by server stop (harness should continue)"
},
{
"index": 14,
"category": "action_first",
"user": "Schedule a meeting tomorrow 3pm with the team.",
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{
"index": 15,
"category": "chat_direct",
"user": "Thanks for fixing that!",
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},
{
"index": 16,
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"user": "What does Response path mean in Meltdown?",
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{
"index": 17,
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"user": "Is my data sent to the cloud?",
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"tool_name": "mp"
},
{
"index": 18,
"category": "chat_direct",
"user": "Can you append to journals?",
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},
{
"index": 19,
"category": "chat_tool",
"user": "Append line run_24 to C:\\Users\\jassi\\Documents\\ProGPT\\Billa\\journal.txt",
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"has_assess": false,
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"path_match": true,
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}
]
} |