# Vision-Language Alignment

InternVL3-14B-Instruct is an advanced Multimodal Large Language Model (MLLM) that demonstrates exceptional multimodal perception and reasoning capabilities, supporting various tasks such as tool usage, GUI agents, industrial image analysis, and 3D visual perception.

A fine-tuned version based on the lmms-lab/llava-onevision-qwen2-7b-ov model, supporting video-text-to-text conversion tasks.

A vision-language model based on BART-Large and ViT for generating English descriptions of images.

TITAN is a multimodal whole slide foundation model pre-trained through visual self-supervised learning and vision-language alignment for pathology image analysis.

LLM2CLIP is an innovative approach that enhances CLIP's cross-modal capabilities through large language models, significantly improving the discriminative power of visual and text representations.

IP-Adapter-Instruct is an image-to-image transformation model focused on instruction-guided image editing and generation tasks.

Cambrian is an open-source multimodal LLM (Large Language Model) designed with a vision-centric approach.

Libra is a decoupled vision system built upon large language models, possessing fundamental multimodal understanding capabilities.

A vision-language model based on the CLIP architecture, supporting zero-shot image classification tasks

CLIP is a multimodal vision-language model capable of mapping images and text into a shared embedding space, enabling zero-shot image classification and cross-modal retrieval.

A model based on distilbert-base-multilingual, fine-tuned to align the embedding space for 40 languages, matching the embedding space of CLIP text encoder.