Understanding GGUF, GGML, and Safetensors: A Deep Dive into Modern Tensor Formats

In the rapidly evolving field of machine learning, efficient storage and handling of model data is crucial. Three prominent formats have emerged to address these needs: GGUF, GGML, and Safetensors. Let’s explore each of these in detail.

GGUF: GPT-Generated Unified Format

GGUF is a binary file format designed for the efficient loading and saving of large language models (LLMs). Developed by Georgi Gerganov, GGUF builds upon the foundations laid by its predecessor, GGML. Here are some key features of GGUF:

• Optimized for Inference: GGUF is designed to perform well on consumer-grade hardware, making it accessible for a wide range of users1.

• Extensible and Versatile: GGUF can incorporate new information without breaking compatibility with older models, ensuring smooth transitions to newer versions.

• Metadata Support: Unlike tensor-only formats, GGUF encodes both tensors and a standardized set of metadata, making it highly efficient for inference purposes.

• Compatibility: GGUF is compatible with various programming languages like Python and R, and supports fine-tuning for specialized applications.

GGML: Tensor Library for Machine Learning

GGML is a tensor library designed for high performance on various hardware platforms. It was the precursor to GGUF and has been widely used in the machine learning community. Key features of GGML include:

• High Performance: GGML is optimized for different hardware architectures, including Apple Silicon and x86 platforms.

• Quantization Support: GGML supports integer quantization (4-bit, 5-bit, 8-bit), which helps in reducing the model size and improving inference speed.

• Automatic Differentiation: GGML includes built-in support for automatic differentiation, making it easier to implement and train machine learning models.

• Open Source: GGML is open-source and freely available under the MIT license, encouraging community contributions and collaboration.

Safetensors: Safe and Fast Tensor Storage

Safetensors is a new format developed by Hugging Face for storing tensors safely and efficiently. It addresses some of the limitations of traditional tensor storage formats like pickle. Key features of Safetensors include:

• Safety: Safetensors ensures the confidentiality and safety of data by avoiding the use of pickle, which can be insecure.

• Speed: The format is designed for zero-copy operations, making it extremely fast for loading and saving tensors.

• Ease of Use: Safetensors is simple to use and supports various programming languages and platforms, making it a versatile choice for deep learning applications.

• Wide Adoption: Safetensors is being used by leading AI enterprises such as Hugging Face, EleutherAI, and StabilityAI.

Conclusion

GGUF, GGML, and Safetensors each offer unique advantages for storing and handling model data in machine learning. GGUF and GGML provide efficient and flexible solutions for large language models, while Safetensors offers a safe and fast alternative for tensor storage.