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
- llama.cpp
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
- Pi
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"
I wanted to ask whether you would consider.
Hi @yuxinlu1 ,
Thank you for releasing this model. I've been testing several of your Mellum releases, and they've consistently been among the most interesting reasoning-focused models available in the open-source community.
I especially appreciated the earlier abliterated Mellum release, which demonstrated that the model can remain highly capable while being more flexible for research, creative writing, roleplay, agent workflows, and advanced user customization.
Would you consider releasing an official abliterated or less-restrictive version of this Claude-tuned Mellum model as well? It would be very interesting to compare the reasoning capabilities of the original release with a research-oriented variant.
It could also be interesting to see collaboration or community contributions from people such as Huihui, Mradermacher, and Eric Hartford, who have each contributed valuable work to the open-source model ecosystem.
In any case, thank you again for continuing to develop the Mellum series and for sharing these models with the community. I'm looking forward to seeing future releases.
@ridham034 Thank you β that genuinely means a lot, especially on the Mellum line; it's one of the releases I'm most personally fond of, so I'm glad the reasoning focus landed.
On a less-restrictive / abliterated variant: I'm open to it in principle β I've done abliteration work on other models, everything I put out is Apache-2.0, and I like that direction for research, creative and agent use. Honest caveat on timing: I'm fully heads-down on v3 (the Gemma agentic line) plus a new open-source collaboration with a lab right now, so I can't promise a near-term official abliterated Mellum from me.
But here's the good part β because the Claude-tuned Mellum is Apache-2.0, nobody has to wait on me. People like Huihui, Mradermacher, and Eric Hartford can build directly on it (abliteration, imatrix quants, uncensored variants), and I'd genuinely welcome it β happy to boost anything good that comes out of it, and to compare reasoning against the original like you suggested. If any of them, or you, want to take a run at it, please do β and tag me.
Thanks again for such a thoughtful note; it's the kind of thing that keeps me building. π