# Multimodal Transformer

An image-text to text generation model based on the Transformer architecture, designed specifically for computer/GUI-related scenarios, with intelligent agent capabilities.

GIT is a Transformer-based image-to-text generation model capable of generating descriptive text from input images.

A deep learning-based investment prediction system utilizing Transformer architecture, integrating DeepSeep-V3 and LLama3 design structures for stock price trend forecasting and technical analysis.

GIT is a Transformer-based generative image-to-text model capable of converting visual content into textual descriptions.

A text-to-image model based on Multimodal Diffusion Transformer (MMDiT), utilizing Adversarial Diffusion Distillation (ADD) technology to enhance image quality, typography, and complex prompt understanding.

GIT is a Transformer-based image-to-text generation model capable of generating descriptive text from input images.

GIT is a Transformer-based generative image-to-text model capable of converting visual content into descriptive text.

GIT is a Transformer-based image-to-text generation model trained on large-scale image-text pairs, capable of performing tasks such as image captioning and visual question answering.

GIT is a dual-conditioned Transformer decoder based on CLIP image tokens and text tokens, designed for tasks such as image caption generation and visual question answering.

GIT is a Transformer-based generative image-to-text model capable of generating descriptive text from images.

GIT is a Transformer decoder conditioned on CLIP image tokens and text tokens, designed for tasks like image and video caption generation and visual question answering.

GIT is a vision-language model based on a Transformer decoder, trained with dual conditioning on CLIP image tokens and text tokens, specifically optimized for TextVQA tasks.

GIT is a Transformer decoder based on CLIP image tokens and text tokens, trained on large-scale image-text pairs, suitable for tasks like visual question answering.

GIT is a dual-conditional decoder model based on Transformer, designed for tasks such as image caption generation and visual question answering.

GIT is a Transformer decoder-based vision-language model capable of generating image captions and performing visual question answering

GIT is a Transformer-based generative image-to-text model, with the base version fine-tuned on the VATEX dataset, suitable for tasks such as image and video caption generation.

GIT is a dual-conditional Transformer decoder based on CLIP image tokens and text tokens for image-to-text generation tasks

GIT is a Transformer decoder-based vision-language model trained with CLIP image tokens and text token conditioning, suitable for tasks like image captioning and visual question answering.

GIT is a Transformer-based generative image-to-text model capable of converting visual content into descriptive text.

GIT is a Transformer decoder based on CLIP image tokens and text tokens, used for tasks such as image caption generation and visual question answering.

A general-purpose vision perceiver model pre-trained on ImageNet, utilizing convolutional preprocessing and Transformer architecture, supporting image classification tasks

A speech-to-text transformer model for end-to-end English to Spanish speech translation