Vision language models

LLaVA is a multimodal large language model architecture designed to process and interpret both image and text inputs to generate natural language responses. It functions as a research-oriented platform for visual instruction tuning, providing a framework to align language models with human intent through training on diverse datasets of paired images and text queries. The system distinguishes itself through a specialized vision-language training pipeline that connects visual data to language models using projection layers and instruction-based fine-tuning. It supports distributed inference by

DeepSeek-VL2 is a multimodal large language model and vision-language system designed to analyze visual scenes and generate descriptive text. It functions as a visual question answering and visual grounding model, capable of extracting information from documents and locating specific objects or regions within images based on textual descriptions. The project utilizes a mixture-of-experts architecture to process combined image and text inputs. It is optimized for inference through incremental prefilling, which reduces the GPU memory requirements on hardware. The model covers multimodal data a

Qwen2-VL is a multimodal large language model and vision language model designed to process and reason across text, images, and video content. It functions as a visual reasoning engine and a visual agent framework, capable of interpreting visual data to perform object detection, document parsing, and spatial reasoning. The model is distinguished by its ability to act as a video understanding model, processing hour-long videos with second-level indexing and event recall. It further differentiates itself through a visual agent capability that interacts with software interfaces and robotic hardw

DeepSeek-VL is a multimodal large language model and image-to-text reasoning engine. It functions as a vision-language model and visual question answering system that integrates visual perception with linguistic reasoning to understand and describe images. The project enables multimodal image understanding and document image analysis, specifically processing screenshots of web pages and technical diagrams. It provides capabilities for visual conversational AI, allowing users to interact with visual data to extract insights and perform complex reasoning across different types of visual informa

MiniCPM-V is a multimodal large language model and vision-language system designed for complex visual and linguistic understanding. It functions as an on-device AI model, providing the capacity to process text, images, and video as a compact neural network. The project is specifically developed as an edge AI framework, utilizing quantization and weight sharding to run on memory-constrained mobile chipsets. This allows for the deployment of multimodal intelligence directly on mobile operating systems for local inference. Its capabilities cover multimodal content analysis of high-resolution im

LLaVA-NeXT is a multimodal large language model framework and training toolkit designed to process interleaved images and video sequences to generate text. It functions as a visual language model that combines vision encoders with language models to perform complex reasoning, question answering, and video understanding. The system is capable of analyzing high-resolution images and temporal video frames to describe events, summarize actions, and reason across multiple visual inputs. It supports the interpretation of documents and charts, spatial environment analysis, and the generation of desc

ml-ferret is a multimodal large language model framework and visual reasoning engine designed to reason about images and user interfaces. It functions as a UI grounding model and referring expression comprehension tool that maps natural language descriptions to precise pixel coordinates. The system focuses on high-resolution image analysis to identify and locate specific interface components. It employs multi-resolution image processing and region-aware visual encoding to preserve detail across different aspect ratios, enabling the model to analyze spatial relationships and functional layouts

CogVLM is a multimodal large language model designed for visual reasoning and multi-turn dialogue. It functions as a visual grounding model and a quantized vision model, combining text and image processing to perform complex understanding and maintain context across visual inputs. The project includes capabilities as a GUI automation agent, allowing it to analyze application screenshots, plan operational steps, and return precise screen coordinates for interface interaction. It further supports visual grounding by generating bounding box coordinates to map text descriptions to specific spatia

InternVL is a vision-language model framework that fuses a visual encoder with a large language model to translate image features into textual tokens for reasoning. It provides a system for multimodal inference and dialogue, enabling the processing of images and text to answer questions or generate descriptions. The project is distinguished by its high-resolution image processing, which uses dynamic tiling to maintain detail for images up to 4K resolution, and its chain-of-thought visual reasoning for solving complex mathematical and spatial problems. It also supports temporal frame sampling

Otter is a framework and toolkit for the pretraining, fine-tuning, and evaluation of vision-language models. It provides a pipeline for training large language models to process high-resolution images and video frames, integrating visual encoders with textual token spaces. The system is designed for multi-visual input processing, allowing models to interpret multiple images or video sequences within a single prompt. It supports multi-round conversation management to maintain context across interactions for detailed scene comprehension and visual reasoning. The framework covers a full develop

MiniGPT-4 is a multimodal AI framework and large language model that integrates vision encoders with language models to process and reason about combined image and text inputs. It functions as a vision-language model capable of image-based conversational AI, visual question answering, and multimodal logical reasoning. The project utilizes a pretrained vision-language integration strategy that connects a vision encoder to a language model via a linear projection layer. This approach employs frozen-backbone training to align visual representations with linguistic tokens while keeping the primar

LARK is a development toolkit for training, fine-tuning, and deploying large language models and multimodal models based on PaddlePaddle. It functions as a comprehensive framework that includes an LLM training orchestrator, an inference server, and a multimodal model framework for processing text, image, and video inputs. The project features a retrieval-augmented generation system for building conversational applications that integrate web search and private knowledge bases. It provides specific capabilities for multimodal reasoning and complex logic, enabling the extraction of structured da

This project provides a foundational framework and reference implementation for executing causal language modeling and multimodal reasoning on local systems. It includes a set of core components for managing model assets, a fine-tuning framework, and structural definitions required to instantiate transformer-based architectures. The system is distinguished by its ability to process combined text and image inputs through multimodal transformer models for visual reasoning and document analysis. It also supports the deployment of quantized models, reducing memory footprints through low-precision

This project is a research framework and toolkit designed for training large-scale vision transformers and multimodal language models. It provides a comprehensive suite for vision-language pretraining, enabling the development of models that map images and text into shared latent spaces. The framework is distinguished by its capabilities in high-fidelity image generation and multimodal research, utilizing normalizing flows and variational autoencoders to produce images from text prompts or class labels. It supports the development of both generative and contrastive models, allowing for a wide

OmniParser is a multimodal interaction engine designed to function as a desktop automation agent. It interprets visual screen information to execute complex, multi-step tasks across operating system environments by bridging visual interface perception with language models. Through a continuous cycle of observation and command execution, the system grounds high-level natural language instructions into precise, coordinate-based actions. The project distinguishes itself by utilizing vision-based parsing to interact with software interfaces without requiring access to underlying application progr

CogVLM is a multimodal large language model designed to integrate visual and textual data for reasoning about images and generating natural language. It functions as a visual question answering system that analyzes image content to provide detailed descriptions or answer specific questions. The project includes a visual grounding model capable of mapping text descriptions to precise bounding box coordinates within an image. It also features a vision-based automation agent that analyzes screen captures to generate execution plans and interaction coordinates for software interfaces. The system

VisualGLM-6B is a bilingual multimodal large language model and vision-language model designed for conversational tasks and visual understanding. It functions as a bilingual AI model capable of processing and generating responses in both Chinese and English. The system is a quantized large language model supporting 4-bit and 8-bit precision to reduce memory usage and hardware requirements during local deployment. It is also a parameter-efficient fine-tuning model, allowing for weight adjustments to adapt the system to specific downstream tasks without full retraining. The project covers mult

This project is a vision language model framework and vision-to-text pipeline designed for deploying and optimizing models that process both images and text. It provides an on-device inference engine and a vision language model framework to run quantized models locally on mobile and desktop hardware accelerators. The framework features a model quantization toolkit to reduce weight precision for lower memory footprints and increased execution speed on specialized silicon. It also includes an efficient vision encoder utilizing a hybrid encoding system to compress image tokens, which reduces pro

OmniGen2 is a unified image generation model and multimodal large language model designed to handle text-to-image generation, image-to-image tasks, and image editing within a single framework. It functions as a causal language model visual engine capable of generating and editing images based on combined text and visual inputs. The system features in-context visual composition and subject-driven generation, allowing it to extract subjects from reference images and place them into new scenes. It also supports instruction-based image editing, where specific objects or styles are modified via na

Donut is an OCR-free document transformer and end-to-end document parser. It functions as a neural network that converts unstructured document images directly into structured data or text without the use of an external optical character recognition engine. The project includes a synthetic document generator to create artificial images and ground-truth labels for training. It employs a transformer model to perform visual question answering and document image classification based on visual layout and text. The system covers several document understanding capabilities, including structured info

This project is a comprehensive framework and toolkit for developing, optimizing, and deploying transformer-based models across multimodal, document intelligence, and natural language processing tasks. It provides a unified neural architecture that processes text, vision, audio, and document layout data through a shared set of weights, enabling researchers and developers to build foundational models that align cross-modal representations. The platform distinguishes itself through advanced training and inference strategies designed for large-scale deep learning. It incorporates specialized mec

Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing and tokenization to distributed training and inference. The library features extensive support for model optimization and

Sglang is a high-performance inference engine and serving system designed for large language and multimodal models. It provides a programmable interface for orchestrating complex generation workflows, enabling developers to coordinate multi-turn dialogues, tool invocations, and reasoning chains through a domain-specific language. The platform is built to support production-scale deployments, offering an OpenAI-compatible API that allows for integration with existing application ecosystems. The system distinguishes itself through a disaggregated architecture that separates compute-intensive pr

AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end pipeline from data preprocessing to high-accuracy model training and validation. It functions as an automated model trainer for tabular, image, text, and time series data, as well as a tool for time series forecasting and foundation model finetuning. The project is distinguished by its ability to jointly process and fuse different data types, allowing for the construction of multimodal neural networks that integrate images, text, and structured tables. It supports zero-shot inferenc

LLaMA-Factory is a comprehensive suite for dataset preparation, model fine-tuning, memory optimization, and standardized API deployment. It provides a unified platform for the supervised and reward-based fine-tuning of large language models and vision-language models. The framework includes a specialized toolkit for training vision-language models and a model serving interface that deploys trained models through high-performance APIs. It utilizes precision tuning and quantization techniques to reduce the hardware requirements and memory footprint of large models. The system covers data pipel

This project is a comprehensive framework for the entire lifecycle of transformer-based language models, supporting everything from foundational pretraining to specialized deployment. It provides a modular toolkit for defining neural network architectures, managing data preparation pipelines, and executing training routines across various scales. The framework is designed to handle the full model development process, including supervised fine-tuning, behavioral alignment, and the integration of agentic capabilities. What distinguishes this framework is its focus on efficient training and adva

LLaDA is a masked diffusion language model and conditional text generator. It generates text by iteratively refining masked tokens through a diffusion process rather than predicting the next token in a sequence. The project functions as a vision-language diffusion model, converting visual inputs into text responses. It also serves as a preference optimization framework that uses log-likelihood estimation and evidence lower bounds to tune model responses. The system supports multi-round conversational AI and text sequence evaluation. It integrates vision-language embedding for cross-modal con

CLIP is a neural network architecture designed to map visual and textual data into a shared latent vector space. By utilizing transformer-based feature extraction and multi-modal tokenization, the system aligns images and natural language strings, enabling cross-modal similarity analysis and semantic classification. The project functions as a zero-shot classification engine, identifying image content by calculating the cosine similarity between visual features and arbitrary text labels without requiring task-specific retraining. Beyond inference, it serves as a research toolkit for evaluating

YOLO-World is a vision-language framework and open-vocabulary object detection model. It identifies objects in images and video based on free-form text prompts without requiring predefined category labels. The system enables the identification of arbitrary objects by fusing image features with text embeddings. It includes a specialized tool for automated image labeling, which generates bounding box annotations for custom datasets using text-based prompts. The project provides a deployment pipeline for converting models into quantized ONNX and TFLite formats, supporting real-time inference on

BLIP is a vision-language model framework that combines contrastive, matching, and language modeling objectives to align images with text. Built on a multimodal encoder-decoder architecture, it supports distributed data-parallel training with cosine learning rate scheduling and sliding-window metric tracking for training stability. The framework provides capabilities for image captioning, visual question answering, and cross-modal retrieval, scoring semantic alignment between images and text through learned embeddings. It includes toolkits for fine-tuning pre-trained models on custom datasets

多模态中文LLaMA&Alpaca大语言模型（VisualCLA）

The official repo of MiniMax-Text-01 and MiniMax-VL-01, large-language-model & vision-language-model based on Linear Attention

ICLR'24 spotlight Chinese and English Multimodal Large Model Series (Chat and Paint) | 基于CPM基础模型的中英双语多模态大模型系列

This repository serves as a centralized collection of state-of-the-art deep learning architectures and reference implementations designed for research and application development. It provides a comprehensive toolkit for computer vision and natural language processing, offering pre-built models and training pipelines for tasks ranging from image classification and object detection to complex sequence modeling. The project distinguishes itself by providing a flexible execution harness that manages the entire training lifecycle, including data ingestion and backpropagation. It supports scalable

Qwen3-VL is a multimodal vision-language model designed to process and reason across images, videos, and text. It functions as a computer vision framework capable of identifying objects, extracting structured data from documents, and interpreting spatial elements within visual media. The system operates as an automated user interface interaction agent, interpreting screen data to navigate software and mobile applications. By utilizing a unified transformer architecture, it performs complex visual reasoning to execute user-defined tasks without manual input. Beyond interface navigation, the m

The official repo for "Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models".

LAVIS is a multimodal large language model framework and vision-language model library. It provides tools for training and evaluating models that integrate visual, textual, and audio data, serving as a cross-modal feature extractor and a zero-shot visual reasoning engine. The framework distinguishes itself by using frozen-backbone integration, where pretrained encoders remain non-trainable while lightweight adapter layers are updated. It employs cross-modal feature alignment to map different representations into a shared embedding space and utilizes a modular model wrapper to swap vision and

This repository contains the code for SFT, RLHF, and DPO, designed for vision-based LLMs, including the LLaVA models and the LLaMA-3.2-vision models.

R1-Vision: Let's first take a look at the image