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

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

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

Meltdown_GGUF / config.recommended.json

iRunStudio

MeltDown V1-GGUF

28788d0 verified about 1 month ago

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	{
	"model_file": "Meltdown_Q8.gguf",
	"description": "Meltdown APE agent model — Q8_0 quantized GGUF for local tool-calling harnesses",
	"base_model": "Qwen2.5-Coder-3B-Instruct (fine-tuned, exported Q8_0)",
	"quantization": "q8_0",
	"multimodal": false,
	"inference": {
	"ctx_size": 32000,
	"max_output_tokens": 16384,
	"cache_type_k": "q8_0",
	"cache_type_v": "q8_0",
	"gpu_layers": -1,
	"threads": 4,
	"temperature": 0.2,
	"top_p": 0.95,
	"top_k": 40
	},
	"harness": {
	"system_prompt_file": "system_prompt.txt",
	"append_tool_catalog_at_runtime": true,
	"append_sandbox_context_at_runtime": true,
	"tool_result_header_format": "[Tool result — {tool_name}]:",
	"max_agent_rounds": 24,
	"stop_after_tool_call_close_tag": true
	},
	"notes": [
	"Use temperature 0.1–0.3 for reliable APE tags and tool_call JSON.",
	"Append tool list and sandbox paths to the system prompt at session start.",
	"max_output_tokens should be >= 16384 during agent loops to avoid truncated tool calls.",
	"gpu_layers: -1 means offload all layers (llama.cpp convention); adjust to your VRAM."
	]
	}