gguf-org/editor · Hugging Face 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 Downloads last month 270 GGUF Model size 583k params Architecture test-arch Hardware compatibility Log In to add your hardware We're not able to determine the quantization variants. View all variants Inference Providers NEW This model isn't deployed by any Inference Provider. 🙋 Ask for provider support