editor

model editor plus tensor level quantization engine - all in one

install via pip/pip3

pip install gguf-editor

building the bundled quantizer requires a C/C++ toolchain and CMake ≥ 3.15 (on Windows: MSVC Build Tools). GPU accelerators are opt-in at build time:

CMAKE_ARGS="-DQUANTIZER_CUDA=ON"  pip install gguf-editor   # NVIDIA (CUDA)
CMAKE_ARGS="-DQUANTIZER_HIP=ON"   pip install gguf-editor   # AMD (ROCm/HIP)
CMAKE_ARGS="-DQUANTIZER_METAL=ON" pip install gguf-editor   # Apple (macOS)

*if you build it from your machine, it will automatically render your CUDA driver (assuming cuda), hence, don't need to copy the bulky .DLLs from CUDA kit to the package directory; and the model.gguf in this repo is generated just for testing purposes (see examples below)

usage

gguf-editor                 # launch the editor GUI in the browser
gguf-editor model.gguf      # …opening a file right away

editor features (as in the desktop editor / chrome extension):

• inspect and edit metadata (all value types incl. arrays), add/delete keys
• rename, delete, reorder (drag), merge tensors; add zero-filled tensors; import tensors from another GGUF
• find & replace across tensor names (literal or regex)
• per-tensor precision changes and/or a batch weight type — on save the file is rebuilt with your edits and then converted by the quantizer into a single output file
• streams tensor data disk-to-disk on save with live progress

CLI quantizer (mirrors the standalone quantizer binary):

gguf-editor quantize -m model-f16.gguf -o model-q4_k.gguf --type q4_k
gguf-editor quantize -m model.safetensors -o model-q8_0.gguf --type q8_0
gguf-editor quantize -m model.gguf -o out.gguf \
    --tensor-type-rules "attention.*weight=q4_k" --device auto
gguf_editor devices

recently supported types: f32 f16 bf16 q4_0 q4_1 q5_0 q5_1 q8_0 q1_0 q2_k q3_k q4_k q5_k q6_k iq1_s iq1_m iq2_xxs iq2_xs iq2_s iq3_xxs iq3_s iq4_nl iq4_xs tq1_0 tq2_0 mxfp4 nvfp4. Inputs may be GGUF or safetensors (auto-detected; multi-part safetensors are merged automatically).

Python API

from gguf_editor import gguf, quantizer

parsed = gguf.parse_file("model.gguf")           # header-only parse
print(parsed.version, len(parsed.tensor_infos))

quantizer.quantize("model-f16.gguf", "model-q4_k.gguf", default_type="q4_k")

or run it with gguf-connector

ggc et