llamafile lets you run open-source LLMs locally on your computer in one single executable file. No install needed. Works across operating systems and hardware without modifications. Makes LLMs dramatically more accessible.
llamafile is an open-source framework that allows packaging open-source large language models (LLMs) into a single executable binary that can run on multiple operating systems and hardware without any modifications.
📦 Bundles weights and inference code into one file
🖥️ Supports Linux, macOS, Windows out of the box
🤖 Runs on common CPU and GPU hardware without changes
⚡ Built on llama.cpp and Cosmopolitan Libc (multi-platform C runtime)
🔌 Optional web UI server for easier interaction
llamafile makes deploying and running LLMs dramatically more accessible by collapsing complexity into a portable package requiring zero setup. Developers and end users alike can simply download a llamafile matching their use case and execute it locally across common configurations.
The library handles the challenging aspects behind the scenes so you can focus on your applications, whether you want to build a custom assistant or analyze images with a multimodal LLM like LLaVA.
- 🚚 Portability and accessibility - Llamafile allows large language models to be distributed and run as a single executable file that works across multiple operating systems and hardware architectures. This makes deploying LLMs much more portable and accessible without needing to install dependencies or set up environments.
- 🔒 Offline capabilities - Since llamafile packages everything into a self-contained binary, the models can run fully offline without needing a network connection or external services. This is useful for reliability and privacy reasons.
- 🧪 Prototyping and experimentation - Llamafile provides an easy way for AI engineers to prototype and experiment with different LLMs locally by just downloading a file and running it. This enables faster iteration.
- 🔧 Customization - Llamafile is built on top of the llama.cpp runtime, which means engineers can customize and fine-tune the models packaged into llamafiles for their own needs.
- ⏳ Future-proofing - The unified file format and runtime aims to ensure models remain executable indefinitely, even as hardware and platforms change. This helps preserve accessibility going forward.
In summary, llamafile represents an innovative approach to distributing and running LLMs that solves several practical problems for AI engineers around portability, accessibility, experimentation, and preservation of their work. Its simplicity and elegance are noteworthy.
- 👷🏽♀️ Builders: Justine Tunney, Stephen Hood, Ziad Ben Hadj-Alouane, Ikko Eltociear Ashimine
- 👩🏽💼 Builders on LinkedIn: https://www.linkedin.com/in/jtunney/, https://www.linkedin.com/in/stlhood/, https://www.linkedin.com/in/ziadbha/
- 👩🏽🏭 Builders on X: https://twitter.com/justinetunney, https://twitter.com/stlhood https://twitter.com/eltociear
- 👩🏽💻 Contributors: 13
- 💫 GitHub Stars: 4.4k
- 🍴 Forks: 189
- 👁️ Watch: 45
- 🪪 License: Apache-2.0
- 🔗 Links: Below 👇🏽
- GitHub Repository: https://github.com/Mozilla-Ocho/llamafile
- Official Website: https://hacks.mozilla.org/2023/11/introducing-llamafile/
- Profile in The AI Engineer: https://github.com/theaiengineer/awesome-opensource-ai-engineering/blob/main/libraries/llamafile.md
🧙🏽 Follow The AI Engineer for more about llamafile and daily insights tailored to AI engineers. Subscribe to our newsletter. We are the AI community for hackers!
♻️ Repost this to help llamafile become more popular. Support AI Open-Source Libraries!