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Llama 3.1 8B Instruct GGUF

Developed by Mungert
Llama-3.1-8B-Instruct is an instruction-tuned version based on Llama-3-8B, utilizing IQ-DynamicGate technology for ultra-low-bit quantization (1-2 bits), enhancing accuracy while maintaining memory efficiency.
Downloads 1,073
Release Time : 3/16/2025

Model Overview

This model is the 8B parameter instruction-tuned version in Meta's Llama-3 series, optimized for various inference tasks, particularly suitable for memory-constrained environments.

Model Features

IQ-DynamicGate Ultra-low-bit Quantization
Employs a hierarchical strategy for 1-2 bit quantization: the first/last 25% layers use IQ4_XS, while the middle 50% layers use IQ2_XXS/IQ3_S, significantly reducing perplexity.
Key Component Protection
Embedding and output layers use Q5_K quantization, reducing error propagation by up to 38%.
Memory Efficiency Optimization
Multiple quantization options (IQ1_S to Q8_0) cater to different memory needs, with the smallest model requiring only 2.1GB.

Model Capabilities

Text generation
Instruction following
Low-memory inference
CPU/edge device deployment

Use Cases

Memory-constrained Deployment
Edge Device Inference
Run large language models on memory-limited edge devices
IQ1_S quantized version requires only 2.1GB memory
Research Applications
Ultra-low-bit Quantization Research
Study the effects and optimization methods of 1-2 bit quantization
IQ1_M reduces perplexity by 43.9%
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