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Llama 3.2 1B Instruct GGUF

Developed by Mungert
Llama-3.2-1B-Instruct is a 1B-parameter instruction-fine-tuned model based on the Llama architecture, offering multiple quantization formats to accommodate different hardware requirements.
Downloads 708
Release Time : 4/25/2025

Model Overview

This model is a lightweight large language model suitable for instruction-following and generation tasks, supporting various quantization formats to optimize operational efficiency across different hardware.

Model Features

Multi-Format Support
Offers BF16, F16, and multiple quantization formats (e.g., Q4_K, Q6_K, Q8_0, etc.) to adapt to different hardware and memory constraints.
Hardware Optimization
Supports BF16 and FP16 acceleration, suitable for high-performance inference and low-memory devices.
Ultimate Memory Efficiency
Provides ultra-low-bit quantization (e.g., IQ3_XS, IQ3_S, IQ3_M) for extremely low-memory devices.
ARM Device Optimization
The Q4_0 quantization format is specifically optimized for ARM devices, making it ideal for mobile and embedded applications.

Model Capabilities

Text Generation
Instruction Following
Low-Memory Inference
Multi-Hardware Support

Use Cases

Edge Computing
Low-Power Device Deployment
Run the model on ARM devices or low-memory environments to achieve localized text generation.
Reduces memory usage and improves operational efficiency.
High-Performance Inference
GPU-Accelerated Inference
Run the model on GPUs supporting BF16 or FP16 for high-speed text generation.
Improves inference speed and reduces latency.
Experimental Applications
AI Network Monitoring
Used for real-time network diagnostics and quantum security checks.
Enables automated network monitoring and vulnerability detection.
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