# High-precision Semantic Understanding

ViT image encoder based on SigLIP 2, designed for timm, suitable for vision-language tasks

SigLIP 2 vision-language model trained on WebLI dataset, suitable for zero-shot image classification tasks.

A SigLIP 2 vision-language model trained on the WebLI dataset, suitable for zero-shot image classification tasks.

SigLIP 2 is an improved model based on the SigLIP pretraining objective, integrating multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

Qwen2.5-7B-Instruct is a 7B-parameter-scale Chinese instruction fine-tuned large language model based on the Qwen2.5 architecture, suitable for text generation and reasoning tasks.

Qwen2.5-1.5B-Instruct is a 1.5 billion parameter Chinese instruction fine-tuned model based on the Qwen2.5 architecture, suitable for text generation and reasoning tasks.

BERTIS is a text classification model based on the BERT architecture, specifically designed to categorize input texts into 14 predefined image schema categories.