gguf
Read and write ML models in GGUF for GGML
Decision gist · record as of 2026-08-14
Yes. The package has low install friction, active maintenance, no known vulnerabilities, permissive licensing, and strong adoption in the GGML/llama.cpp ecosystem. Install it if you need to work with GGUF model files programmatically or via command-line tools.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Installs cleanly with low friction—a pure Python wheel with four common dependencies (numpy, pyyaml, requests, tqdm).
- Maintained actively with recent releases and no known vulnerabilities.
License · maintenance · safety
permissive license (permissive) — Licensed under MIT (permissive), so you can use, modify, and distribute this package freely in both open and closed projects without restriction.
last release 2026-05-06 (100 days) · last repo commit 2026-08-14 · 123,926 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 6,907,816 downloads/mo, #1,816 on PyPI
Alternatives
Verify before relying
pip install gguf
from gguf import GGUFReader, GGUFWriter
import numpy as np
# Read a GGUF file
reader = GGUFReader('model.gguf')
metadata = reader.get_field('general.name')
# Write a GGUF file
writer = GGUFWriter('output.gguf')
writer.add_tensor('tensor_name', np.array([1.0, 2.0, 3.0]))- Whether the GUI extra dependency list and installation steps are current.
- Performance characteristics when handling large model files.
- Compatibility guarantees with specific GGML or llama.cpp versions.
What it is and what it does
gguf is a Python library for reading and writing files in the GGUF binary format, which is the standard container for machine learning models used by GGML and llama.cpp. It provides both a programmatic API for working with GGUF files in code and command-line tools for inspecting, editing, and converting model metadata and tensors. The package depends on numpy for tensor operations, pyyaml for configuration, requests for network operations, and tqdm for progress reporting.
The library is part of the llama.cpp ecosystem and is actively maintained. It includes utilities to dump metadata, modify key-value pairs, convert file endianness, and optionally a Qt-based GUI editor for visual inspection and editing of GGUF files. It's designed for developers working with GGML-based inference engines who need to programmatically manipulate model files or integrate GGUF handling into their workflows.
Use it for
- Convert Hugging Face models to GGUF format for use with llama.cpp and other GGML-based inference engines.
- Inspect and extract metadata, tensor names, and structure from existing GGUF model files.
- Modify model metadata (name, description, hyperparameters) in GGUF files without rebuilding them.
- Automate GGUF file generation and manipulation in model training or deployment pipelines.
- Debug or validate GGUF file integrity and endianness across different systems.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package has low install friction, active maintenance, no known vulnerabilities, permissive licensing, and strong adoption in the GGML/llama.cpp ecosystem. Install it if you need to work with GGUF model files programmatically or via command-line tools.
Install
gguf on PyPI
Before you install
Installs cleanly with low friction—a pure Python wheel with four common dependencies (numpy, pyyaml, requests, tqdm). Maintained actively with recent releases and no known vulnerabilities.
Requires Python 3.10 or later.
License in practice
Licensed under MIT (permissive), so you can use, modify, and distribute this package freely in both open and closed projects without restriction.
Quickstart
pip install gguf
from gguf import GGUFReader, GGUFWriter
import numpy as np
# Read a GGUF file
reader = GGUFReader('model.gguf')
metadata = reader.get_field('general.name')
# Write a GGUF file
writer = GGUFWriter('output.gguf')
writer.add_tensor('tensor_name', np.array([1.0, 2.0, 3.0]))Verify before relying
- Whether the GUI extra dependency list and installation steps are current.
- Performance characteristics when handling large model files.
- Compatibility guarantees with specific GGML or llama.cpp versions.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesnumpypyyamlrequeststqdm |
| Maintenance | Actively maintained 100 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 6,907,816 / month, #1,816 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3 |
Evidence: gguf-0.19.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “gguf file format reader writer”
- ggufReads and writes binary files in the GGUF (GGML Universal File)…
- libconfReads and writes configuration files in libconfig format, a format…
- pure-pcapy3Pure-Python implementation of the pcapy API for reading and writing…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also llama-cpp-python · pygexf · tomlrt · safetensors · litert-converter · abstract-hugpy-dev · treelite · panda3d-gltf · garak · unsloth