P

Phi 2 GGUF

Developed by Mungert
phi-2 is a text generation model employing IQ-DynamicGate ultra-low bit quantization (1-2 bits), suitable for natural language processing and code generation tasks.
Downloads 472
Release Time : 4/25/2025

Model Overview

phi-2 is an efficient text generation model optimized for memory usage through ultra-low bit quantization, ideal for memory-constrained deployment environments.

Model Features

Ultra-low bit quantization
Utilizes IQ-DynamicGate technology, supporting 1-2 bit quantization, significantly reducing memory footprint.
Precision adaptive quantization
Dynamic precision allocation strategy enhances accuracy while maintaining memory efficiency.
Key component protection
Embedding and output layers use Q5_K quantization to minimize error propagation.

Model Capabilities

Text generation
Natural language processing
Code generation

Use Cases

Memory-constrained deployment environments
CPU and edge device inference
Efficient text generation tasks on memory-limited devices.
Tolerates 1-2 bit errors while maintaining high inference speed.
Ultra-low bit quantization research
Quantization technology research
Investigates the impact of 1-2 bit quantization on model performance.
Significantly reduces perplexity while optimizing memory usage.
Featured Recommended AI Models
ยฉ 2025AIbase