Instructions to use Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF", filename="Moonlight-16B-A3B-Instruct-APEX-handroll.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 Myric/Moonlight-16B-A3B-Instruct-APEX-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 Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF # Run inference directly in the terminal: llama cli -hf Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF # Run inference directly in the terminal: llama cli -hf Myric/Moonlight-16B-A3B-Instruct-APEX-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 Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF # Run inference directly in the terminal: ./llama-cli -hf Myric/Moonlight-16B-A3B-Instruct-APEX-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 Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF
Use Docker
docker model run hf.co/Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF
- LM Studio
- Jan
- vLLM
How to use Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Myric/Moonlight-16B-A3B-Instruct-APEX-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": "Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF
- Ollama
How to use Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF with Ollama:
ollama run hf.co/Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF
- Unsloth Studio
How to use Myric/Moonlight-16B-A3B-Instruct-APEX-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 Myric/Moonlight-16B-A3B-Instruct-APEX-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 Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF with Docker Model Runner:
docker model run hf.co/Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF
- Lemonade
How to use Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Myric/Moonlight-16B-A3B-Instruct-APEX-GGUF
Run and chat with the model
lemonade run user.Moonlight-16B-A3B-Instruct-APEX-GGUF-{{QUANT_TAG}}List all available models
lemonade list
Quality gate — Moonlight-16B-A3B-Instruct (retrofit)
Applied after the fact to quants built before this repo had a quality-gate process. Read-only: the existing GGUFs below were not re-quantized, only tested — real generation on a fixed prompt suite, and (where a working chat template exists) tool-calling reliability. This does not change what's published, only documents it honestly.
No tool-calling test for this model: this model's chat template has no tool-call support at all (verified: no tools/tool_calls handling in the embedded template, no tool-related special tokens in the tokenizer, and no mention of function/tool calling anywhere in the model card — Moonlight is a Muon-optimizer research release, not trained for tool use). Not a quant defect — a limitation of the base model's chat template.
| size | wikitext PPL | vs fp | coherent |
|---|---|---|---|
| fp | 8.71 | 1.00x | — |
| handroll | 8.82 | 1.01x | yes |
| i-quality | 8.77 | 1.01x | yes |