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llama.cpp
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Instructions to use Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf", filename="ΩFFΣLLIα_MXFP4_MOE_Qwable-v1.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.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 Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf:MXFP4_MOE_QWABLE # Run inference directly in the terminal: llama cli -hf Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf:MXFP4_MOE_QWABLE
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf:MXFP4_MOE_QWABLE # Run inference directly in the terminal: llama cli -hf Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf:MXFP4_MOE_QWABLE
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 Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf:MXFP4_MOE_QWABLE # Run inference directly in the terminal: ./llama-cli -hf Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf:MXFP4_MOE_QWABLE
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 Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf:MXFP4_MOE_QWABLE # Run inference directly in the terminal: ./build/bin/llama-cli -hf Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf:MXFP4_MOE_QWABLE
Use Docker
docker model run hf.co/Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf:MXFP4_MOE_QWABLE
- LM Studio
- Jan
- vLLM
How to use Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.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": "Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf:MXFP4_MOE_QWABLE
- Ollama
How to use Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf with Ollama:
ollama run hf.co/Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf:MXFP4_MOE_QWABLE
- Unsloth Studio
How to use Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.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 Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.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 Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf to start chatting
- Pi
How to use Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf:MXFP4_MOE_QWABLE
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": "Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf:MXFP4_MOE_QWABLE" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.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 Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf:MXFP4_MOE_QWABLE
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 Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf:MXFP4_MOE_QWABLE
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf with Docker Model Runner:
docker model run hf.co/Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf:MXFP4_MOE_QWABLE
- Lemonade
How to use Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Brunobkr/OFFELLIA_MXFP4_MOE_Qwable-v1.gguf:MXFP4_MOE_QWABLE
Run and chat with the model
lemonade run user.OFFELLIA_MXFP4_MOE_Qwable-v1.gguf-MXFP4_MOE_QWABLE
List all available models
lemonade list
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