The GGUF AI model file format

This article is intended for those who would like to get a deeper understanding and some background information on the GGUF AI model file format. For those who are interested in the GGUF file format specification and would like to understand which byte means what in the file, the following page is a must-read:

GGUF file format specification

Introduction to GGML

As the demand for larger and more complex language models continues to grow, the need for efficient and effective file formats has never been greater. Enter GGUF (GPT-Generated Unified Format), a revolutionary new standard for language model file formats designed to overcome the limitations of its predecessor, GGML. Developed by experts in the field, GGUF promises to streamline the user experience, boost collaboration, and unlock new possibilities for deploying language models. But what exactly is GGUF, and how does it differ from GGML? In this article, we'll delve into the key differences between these two formats, exploring the advantages, disadvantages, and implications of each, and shedding light on the future of language model file formats.

GGML (GPT-Generated Model Language)

GGML was developed by Georgi Gerganov and is a tensor library specifically designed for machine learning. It aims to facilitate the handling of large models and ensure efficient performance on various hardware platforms, such as general CPUs, NVidia GPUs and Apple Silicon. Some of the benefits of using GGML include:

Benefits of GGML

  • Early innovation in creating a file format for GPT models
  • Enables sharing models in a single file, making it convenient for users
  • Allows running models on CPUs, expanding accessibility

However, GGML also has some drawbacks:

Limitations of GGML

  • Struggled with adding extra information about the model
  • Experienced compatibility issues when introducing new features
  • Requires manual adjustments to settings like rope-freq-base, rope-freq-scale, gqa, and rms-norm-eps, which can be complex

GGUF (GPT-Generated Unified Format)

GGUF is a more recent development, introduced as a successor to GGML. It represents a significant improvement in the field of language model file formats, enabling better storage and processing of large language models like GPT. Developed by contributors from the AI community, including Georgi Gerganov, GGUF's creation aligns with the needs of large-scale AI models. Its use in contexts involving Facebook's (Meta's) LLaMA (Large Language Model Meta AI) models highlights its importance in the AI landscape. You can find more details on GGUF through the GitHub issue linked below and explore the llama.cpp project by Georgi Gerganov.

Benefits of GGUF

  • Addresses GGML's limitations and enhances user experience
  • Allows for the addition of new features while maintaining compatibility with older models
  • Focuses on stability, aiming to eliminate breaking changes and ease the transition to newer versions
  • Supports various models, extending beyond the scope of llama models

However, GGUF also has some drawbacks:

Limitations of GGUF

  • May require significant time to convert existing models to GGUF
  • Users and developers must adapt to the new format

Overall, GGUF represents an upgrade to GGML, offering greater flexibility, extensibility, and compatibility. It aims to streamline the user experience and support a broader range of models beyond llama.cpp. While GGML was a valuable initial effort, GGUF addresses its limitations, marking progress in the development of file formats for language models. As developments continue, GGUF positions itself as a standard for file formats in AI, providing a structured approach to large-scale AI models.

Conclusion

In conclusion, GGUF represents a significant upgrade to GGML, offering increased flexibility, extensibility, and compatibility. As GGUF gains traction, it may eventually supplant GGML as the preferred standard for language model file formats, inaugurating a new era of collaboration and innovation within the AI community markdown.

More information