LLama-3-8b-Uncensored-Q4_0-GGUF

DevsDoCode/LLama-3-8b-Uncensored-Q4_0-GGUF

LLama-3-8b-Uncensored-Q4_0-GGUF is a text generation model from DevsDoCode. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.

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Hugging Face

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At a glance

Task: Text Generation
Library: transformers
License: apache-2.0
Access: Public
Created: May 6, 2024
Updated: May 6, 2024
SHA: b13d386e

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Model card

From the Hugging Face model README

Crafted with ❤️ by Devs Do Code (Sree)

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
q3_k_l	3-bit integers	Balance between model size reduction and accuracy preservation	Moderate impact on accuracy
q3_k_m	3-bit integers	Enhanced accuracy with mixed precision	Increased computational complexity
q3_k_s	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
q4_k_m	4-bit integers	Optimized model size and accuracy with mixed precision and structured pruning	Reduced accuracy
q4_k_s	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
q5_k_m	5-bit integers	Optimized model size and accuracy with mixed precision and structured pruning	Reduced accuracy
q5_k_s	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

Task: Text Generation
Library: transformers
License: apache-2.0
Languages: en
Created: May 6, 2024
Updated: May 6, 2024