# Vision-language alignment

LLM2CLIP is an innovative approach that leverages large language models (LLMs) to unlock the potential of CLIP. It enhances text discriminability through a contrastive learning framework, breaking the limitations of the original CLIP text encoder.

LLM2CLIP is an innovative method that leverages large language models (LLMs) to extend CLIP's capabilities, enhancing text discriminability through a contrastive learning framework and significantly improving cross-modal task performance.

LLM2CLIP is an innovative approach that enhances CLIP's visual representation capabilities through large language models (LLMs), significantly improving cross-modal task performance

Vision Transformer model trained on MetaCLIP-400M dataset, supporting zero-shot image classification tasks

A vision Transformer model trained on the MetaCLIP-2.5B dataset, compatible with both open_clip and timm frameworks

This model is a fine-tuned version of openai/clip-vit-large-patch14-336, primarily used for image-text matching tasks.