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OEvortex/HelpingAI-Lite-GGUF

sourceHugging Facemitupdated 3y agoView on Hugging Face

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Description

Optimize your engagement with This project by seamlessly integrating GGUF Format model files. Please Subscribe to my youtube channel OEvortex

GGUF Technical Specifications

Delve into the intricacies of GGUF, a meticulously crafted format that builds upon the robust foundation of the GGJT model. Tailored for heightened extensibility and user-centric functionality, GGUF introduces a suite of indispensable features:

Single-file Deployment: Streamline distribution and loading effortlessly. GGUF models have been meticulously architected for seamless deployment, necessitating no external files for supplementary information.

Extensibility: Safeguard the future of your models. GGUF seamlessly accommodates the integration of new features into GGML-based executors, ensuring compatibility with existing models.

mmap Compatibility: Prioritize efficiency. GGUF models are purposefully engineered to support mmap, facilitating rapid loading and saving, thus optimizing your workflow.

User-Friendly: Simplify your coding endeavors. Load and save models effortlessly, irrespective of the programming language used, obviating the dependency on external libraries.

Full Information: A comprehensive repository in a single file. GGUF models encapsulate all requisite information for loading, eliminating the need for users to furnish additional data.

The differentiator between GGJT and GGUF lies in the deliberate adoption of a key-value structure for hyperparameters (now termed metadata). Bid farewell to untyped lists, and embrace a structured approach that seamlessly accommodates new metadata without compromising compatibility with existing models. Augment your model with supplementary information for enhanced inference and model identification.

QUANTIZATION_METHODS:

Method	Quantization	Advantages	Trade-offs
q2_k	2-bit integers	Significant model size reduction	Minimal impact on accuracy
q3kl	3-bit integers	Balance between model size reduction and accuracy preservation	Moderate impact on accuracy
q3km	3-bit integers	Enhanced accuracy with mixed precision	Increased computational complexity
q3ks	3-bit integers	Improved model efficiency with structured pruning	Reduced accuracy
q4_0	4-bit integers	Significant model size reduction	Moderate impact on accuracy
q4_1	4-bit integers	Enhanced accuracy with mixed precision	Increased computational complexity
q4km	4-bit integers	Optimized model size and accuracy with mixed precision and structured pruning	Reduced accuracy
q4ks	4-bit integers	Improved model efficiency with structured pruning	Reduced accuracy
q5_0	5-bit integers	Balance between model size reduction and accuracy preservation	Moderate impact on accuracy
q5_1	5-bit integers	Enhanced accuracy with mixed precision	Increased computational complexity
q5km	5-bit integers	Optimized model size and accuracy with mixed precision and structured pruning	Reduced accuracy
q5ks	5-bit integers	Improved model efficiency with structured pruning	Reduced accuracy
q6_k	6-bit integers	Balance between model size reduction and accuracy preservation	Moderate impact on accuracy
q8_0	8-bit integers	Significant model size reduction	Minimal impact on accuracy