# Multimodal Processing

This model is based on the transformers library, and its specific purpose and functionality require further information to determine.

A robust image encoder model based on the Transformers library, focused on image feature extraction tasks

MixTex base_ZhEn is an image-to-text model supporting both Chinese and English, released under the MIT License.

Optimized model based on Gemma 3 4B, using the same data mixing scheme as Glitter 12b

Ola-7B is a multimodal large language model jointly developed by Tencent, Tsinghua University, and Nanyang Technological University. Based on the Qwen2.5 architecture, it supports processing text, image, video, and audio inputs and generates text outputs.

A document visual question answering model fine-tuned based on Florence-2-base, specifically designed for handling QA tasks in document images.

LongVU is a spatio-temporal adaptive compression technology designed for long video language understanding, aiming to efficiently process long video content and enhance language comprehension.

Oryx-1.5-7B is a 7B-parameter model developed based on the Qwen2.5 language model, supporting a 32K token context window and specializing in efficiently processing visual inputs of arbitrary spatial dimensions and durations.

LongVU is a spatio-temporal adaptive compression technology for long video language understanding, designed to efficiently process long video content.

An 800M-parameter vision-language model from H2O.ai, specializing in OCR and document understanding with excellent performance

A version fine-tuned for 1 day using the Docmatix dataset (5% data volume) based on Microsoft's Florence-2 model, suitable for image-text understanding tasks

This model is based on the transformers library, and its specific functions and uses require further information for confirmation.

A mask generation model implemented in PyTorch, integrated and pushed to the Hub via PytorchModelHubMixin

A pretrained Transformer model for robotic control tasks, supporting basic functions such as motion planning and object grasping

Donut is a Transformer-based image-to-text model capable of extracting and generating textual content from images.

This is an image caption generation model based on the BLIP architecture, specifically designed to generate text descriptions for icons or simple images.

A fine-tuned version of the microsoft/layoutlmv3-base model for document image classification tasks, achieving an F1 score of 0.8177 on the evaluation set

This model can convert medical images (such as X-rays) into descriptive text to assist in medical diagnosis.

A document understanding model fine-tuned based on microsoft/layoutlmv3-base, excelling in document layout analysis tasks

Handwriting recognition model fine-tuned based on naver-clova-ix/donut-base

LLaVA is a large language and vision assistant model capable of handling multimodal tasks involving images and text.

DocLLM_reimplementation is a large language model implementation project for document understanding tasks, aimed at reimplementing and improving document comprehension capabilities.

This model is designed to convert charts into structured tables, built on the UniChart architecture, with generated tables using specific delimiters to represent row and column structures.

This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the generator dataset, suitable for document understanding and layout analysis tasks.

Fine-tuned image-to-text model based on microsoft/git-base

Fine-tuned image-to-text model based on microsoft/git-base

This model is outdated. It is recommended to use the official Nougat model. Nougat is an advanced vision-language model focused on document understanding and analysis.

An image-to-text model fine-tuned from microsoft/git-base, specialized for the fashion domain

Model fine-tuned based on naver-clova-ix/donut-base, specific purpose not clearly stated

A model fine-tuned based on naver-clova-ix/donut-base, specific uses and functions require more information

A multimodal fusion model based on WavLM and BERT, suitable for joint speech and text task processing.

DePlot is a visual-language reasoning model capable of converting chart images into linearized tables, enabling few-shot reasoning when combined with large language models

A document understanding model fine-tuned from naver-clova-ix/donut-base, suitable for image folder datasets

Model fine-tuned based on naver-clova-ix/donut-base, specific purpose not explicitly stated

This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased for document visual question answering tasks, focusing on processing text and layout information in document images.

A document understanding model fine-tuned on the nielsr/funsd-layoutlmv3 dataset based on microsoft/layoutlmv3-base

A model capable of converting image content into textual descriptions, suitable for various vision-language tasks.

VisionEncoderDecoder model fine-tuned on the CORD-v2 dataset for document understanding tasks

A document understanding model fine-tuned on the CORD dataset based on LayoutLMv3, excelling in document token classification tasks

A document understanding model fine-tuned on the FUNSD dataset based on the LayoutLMv3-base model, excelling in token classification tasks for forms and documents