How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Briko/Marco-Nano-Instruct-APEX
# Run inference directly in the terminal:
llama-cli -hf Briko/Marco-Nano-Instruct-APEX
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Briko/Marco-Nano-Instruct-APEX
# Run inference directly in the terminal:
llama-cli -hf Briko/Marco-Nano-Instruct-APEX
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 Briko/Marco-Nano-Instruct-APEX
# Run inference directly in the terminal:
./llama-cli -hf Briko/Marco-Nano-Instruct-APEX
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 Briko/Marco-Nano-Instruct-APEX
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Briko/Marco-Nano-Instruct-APEX
Use Docker
docker model run hf.co/Briko/Marco-Nano-Instruct-APEX
Quick Links

Marco-Nano-Instruct-APEX APEX Quantized (GGUF)

This repository contains APEX-quantized GGUF files for AIDC-AI's Marco-Nano-Instruct.

The quantization was performed using the mudler/apex-quant project, focusing on maximizing quality-to-size ratio using importance matrix (imatrix) guided quantization.

๐Ÿ“ฅ Source & Credits

Special thanks to @mradermacher for providing the high-quality imatrix file!

โš ๏ธ For technical validation only

  • Severe accuracy loss due to quantization; outputs may contain hallucinations, gibberish, or fail basic tasks.
  • Suitable only for researching quantization noise, debugging conversion scripts, or comparing compression artifacts.
  • No post-training calibration, fine-tuning, or recovery techniques were applied.
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Model size
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