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s3nh/Loquace-tiny-1.1B-GGUF
Loquace-tiny-1.1B-GGUF is a text generation model from s3nh. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as openrail.
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Downloads · 30 days
22
4% of all-time downloads
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.gguf4 GB · 100%
From the Hugging Face model README
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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: Quantization is the process of converting a complex or high-dimensional set of data into a smaller, more manageable set. In machine learning, this process involves dividing the input data into sub-chunks and then assigning each piece to one of many possible categories based on a threshold. This helps to reduce the computational complexity while maintaining a higher accuracy level.
User: Okay, that makes sense. How does quantization help with building models?
AI: Quantization can be used in machine learning to convert high-dimensional data sets into smaller and more manageable dimensions, which can then be used