Instructions to use yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-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 yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-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 yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF: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 yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF: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 yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF:Q4_K_M

Use Docker

docker model run hf.co/yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-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": "yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF:Q4_K_M

Ollama
How to use yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF with Ollama:
```
ollama run hf.co/yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF:Q4_K_M
```
Unsloth Desktop

How to use yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF:Q4_K_M

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": "yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF with Docker Model Runner:
```
docker model run hf.co/yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF:Q4_K_M
```

Lemonade

How to use yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-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 yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF:Q4_K_M

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 yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF:Q4_K_M

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 "yuxinlu1/Mellum2-12B-A2.5B-Claude-4.6-4.8-Opus-Thinking-GGUF:Q4_K_M" \
  --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"

merge ?

by malv-c - opened Jun 16

Discussion

malv-c

Jun 16

can you merge it with your gemma4 coder ?

yuxinlu1

Owner Jun 16

Yes — and v2 will go well beyond a simple merge. The plan is to fuse three things into one model:

the coding ability from this coder model with more data,
the general reasoning/conversation from the Claude-distilled Gemma 4
real agentic "terminal" coding data — multi-step tool-use sessions, not just single-function problems.
So v2 won't just be "coder + general stitched together" — it'll actually be stronger at real-world agentic/terminal
coding (tool calls, multi-step tasks) on top of the general improvements. Stay tuned!

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment