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

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

Moonlight-16B-A3B-Instruct-APEX-GGUF / REPRODUCE.md

Myric

Upload REPRODUCE.md with huggingface_hub

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Reproducing these APEX quants

End-to-end recipe to rebuild both tiers from scratch. Everything is MIT-licensed (see NOTICE).

Pinned versions

llama.cpp — commit bbf4a8a (build b8833) or any newer build that supports the deepseek2 architecture, the kimi-k2 pre-tokenizer, and llama-quantize --tensor-type-file. Binaries used: llama-imatrix, llama-quantize, llama-perplexity.
apex-quant — commit a445a12 (https://github.com/localai-org/apex-quant), for scripts/generate_config.sh. A copy of that script is bundled here as generate_config.sh for convenience.

Inputs

Baseline (F16 GGUF): gabriellarson/Moonlight-16B-A3B-Instruct-GGUF → Moonlight-16B-A3B-Instruct-F16.gguf. (We quantize from a correctly-converted community F16 rather than converting the HF safetensors ourselves — Moonlight's Moonshot tiktoken tokenizer needs BPE merges that some llama.cpp converters drop. Verify any baseline with tokenizer.ggml.merges present and a coherent generation.)
Calibration data (for the imatrix): Bartowski calibration_datav3 — https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8

hf download gabriellarson/Moonlight-16B-A3B-Instruct-GGUF \
  Moonlight-16B-A3B-Instruct-F16.gguf --local-dir .
# save the gist as calibration_datav3.txt

1. Importance matrix (imatrix)

llama-imatrix -m Moonlight-16B-A3B-Instruct-F16.gguf \
  -f calibration_datav3.txt \
  -o Moonlight-16B-A3B-Instruct-F16.imatrix -ngl 999

2. Tensor-type configs

The final configs are included (configs/moonlight_i-quality.txt, configs/moonlight_handroll.txt). To regenerate them:

# Base map: 27 layers, layer 0 is dense (first_k_dense_replace=1)
bash generate_config.sh --profile i-quality --layers 27 --dense-layers 1 \
  -o moonlight_i-quality.base.txt

# i-quality: patch in the real MLA attention tensors, drop non-existent ones
python patch_moonlight_config.py moonlight_i-quality.base.txt moonlight_i-quality.txt

# hand-roll: same, plus pin routed down projections to IQ4_NL (1408 dim isn't
# 256-divisible, so K-quants fall back to Q8_0; IQ4_NL is block-32)
python patch_moonlight_config.py moonlight_i-quality.base.txt moonlight_handroll.txt \
  --down-exps iq4_nl

3. Quantize (base type Q6_K for unlisted tensors: token_embd, output)

llama-quantize --tensor-type-file configs/moonlight_i-quality.txt \
  --imatrix Moonlight-16B-A3B-Instruct-F16.imatrix \
  Moonlight-16B-A3B-Instruct-F16.gguf \
  Moonlight-16B-A3B-Instruct-APEX-i-quality.gguf Q6_K

llama-quantize --tensor-type-file configs/moonlight_handroll.txt \
  --imatrix Moonlight-16B-A3B-Instruct-F16.imatrix \
  Moonlight-16B-A3B-Instruct-F16.gguf \
  Moonlight-16B-A3B-Instruct-APEX-handroll.gguf Q6_K

4. Evaluate (perplexity)

# NOTE: llama-perplexity prints the final estimate to STDERR — capture 2>&1.
for f in F16 APEX-i-quality APEX-handroll; do
  ppl=$(llama-perplexity -m Moonlight-16B-A3B-Instruct-$f.gguf \
        -f wiki.test.raw -ngl 999 --chunks 200 2>&1 \
        | grep -oP 'Final estimate: PPL = \K[0-9.]+')
  echo "$f  PPL=$ppl"
done

Expected (wikitext-2 test, 200×512 windows): F16 8.836 · i-quality 8.916 (+0.90%) · hand-roll 8.959 (+1.38%).