# Edge computing optimization

GLM-Z1-9B-0414 is a bilingual text generation model supporting both Chinese and English, utilizing the GGUF format and suitable for various quantization levels, from BF16 to ultra-low-bit quantization (1-2 bits).

Ultra-low-bit quantization (1-2 bits) language model using IQ-DynamicGate technology, suitable for memory-constrained environments

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.

Falcon-E-1B-Base is an efficient 1.58-bit language model developed by TII, featuring a pure Transformer architecture and optimized for edge devices.

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.

InternVL2_5 - 1B is a multimodal large model based on image-text to text conversion, suitable for vision-language tasks.

The GGUF quantized version of Qwen2-VL-72B-Instruct, supporting multimodal image-text to text conversion, which can be run through LlamaEdge.

GLM-Edge-V-2B is an image-text-to-text model based on the PyTorch framework, supporting Chinese processing.

Hybrid model based on TinyLlama-1.1B-Chat-v1.0, providing a lightweight LLM solution