# Low-memory inference

A lightweight instruction fine-tuned language model optimized for mobile deployment

OpenHands LM is an open-source coding model built on Qwen Coder 2.5 Instruct 32B, which performs excellently in software engineering tasks through special fine-tuning.

Falcon-E-3B-Instruct is an efficient language model based on a 1.58-bit architecture, optimized for edge devices, with excellent inference capabilities and low memory usage.

Falcon-E-1B-Instruct is an efficient language model based on a 1.58-bit architecture, optimized for edge devices with low memory footprint and high performance.

all-MiniLM-L6-v2 is a compact and efficient sentence embedding model based on the MiniLM architecture, suitable for sentence similarity computation and feature extraction tasks.

An IQ-DynamicGate ultra-low-bit quantization (1-2 bit) model based on Llama-3-8B-Instruct, utilizing precision-adaptive quantization technology to enhance inference accuracy while maintaining extreme memory efficiency.

SmolVLM2-2.2B is a lightweight multimodal model designed for analyzing video content. It can process video, image, and text inputs and generate text outputs.

A compressed version of the MPT-7B-Chat model provided by PrunaAI, optimized with llm-int8 technology to significantly reduce memory usage and energy consumption.