Buy me a coffee if you like this project ;)

GGUF Format model files for This project.

GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:

Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information. Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models. mmap compatibility: models can be loaded using mmap for fast loading and saving. Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used. Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user. The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values. This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for inference or for identifying the model.

User: Tell me story about what is an quantization and what do we need to build.

AI: A quantization is the process of representing continuous data or signals with discrete values. It's a crucial step in digital signal processing, computer graphics, and machine learning models, where we deal with large amounts of data that are not easily manageable in their original form.

Quantization helps us convert these data points into more manageable discrete values. This process reduces the amount of information required to represent the data, which makes it easier to store, transmit, and process.

When building a quantization system, we typically need the following components: