Novocode7b-v3-GGUF

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Description

GGUF Format model files for This project.

GGUF Specs

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.

inference

User: Tell me story about what is an quantization and what do we need to build. User 0: Sure, I'd be happy to help!

So, in the context of deep learning, quantization refers to the process of representing the weights and activations of a neural network using lower-precision data types, such as 8-bit or 16-bit integers, instead of the traditional 32- or 64-bit floating-point numbers. This can significantly reduce the size of the model and make it more efficient to train and deploy on resource-constrained devices.

To build a quantized neural network, you'll need to

Original model card