agentlans/Llama3.1-Dark-Enigma-GGUF

Llama3.1-Dark-Enigma Llama.cpp Quantizations

Model Description

Llama3.1-Dark-Enigma is a hybrid AI text model developed for versatile applications including research analysis, content creation, interactive storytelling, and software development.

Quantization Overview

The model has been quantized to reduce computational requirements while maintaining performance. Three quantization levels are available:

• 3 bits per word (bpw): This is the most compact representation, which minimizes memory usage but may lead to some degradation in model accuracy.
• 4 bpw: A balance between compression ratio and model performance, providing a good trade-off between efficiency and accuracy.
• 5 bpw: The highest precision level, offering optimal model performance at the cost of slightly increased memory requirements.

Key Features

• Hybrid architecture for broad applicability
• Multiple quantization options for flexible deployment
• Optimized for diverse use cases from academic research to creative writing