Tool Packs 16 GB of GGUF Quantized Models into 1.8 GB Without Data Loss

A new open‑source repository offers a way to shrink large GGUF quantized model files. The author demonstrates packing a 16 GB GGUF file down to 1.8 GB. The process works without any loss of the original quantization data. GGUF is a format used for storing quantized large language models. Reducing file size eases distribution and storage for developers. The tool repacks the file by reorganizing its internal structure. Users run the script on a local machine with standard Python dependencies. The repository includes usage instructions and example commands. The author invites community feedback to improve compression and compatibility.