Llava NeXT Video 34B DPO

The LLaVA-Video model is a 7B-parameter multimodal model based on the Qwen2 language model, specializing in video understanding tasks and supporting 64-frame video input.

LLaVA-NeXT-Video is an open-source multimodal chatbot optimized through mixed training on video and image data, possessing excellent video understanding capabilities.

InternVideo2.5 is a video multimodal large language model enhanced by Long and Rich Context (LRC) modeling, built upon InternVL2.5. It significantly improves existing MLLM models by enhancing the ability to perceive fine-grained details and capture long-term temporal structures.

CogVLM2-Caption is a video caption generation model used to generate training data for the CogVideoX model.

SpaceTime GPT is a video description generation model capable of spatial and temporal reasoning, analyzing video frames and generating sentences describing video events.

Video-R1-7B is a multimodal large language model optimized based on Qwen2.5-VL-7B-Instruct, focusing on video reasoning tasks, capable of understanding video content and answering related questions.

InternVideo2.5 is a video multimodal large language model (MLLM) built upon InternVL2.5, enhanced with Long and Rich Context (LRC) modeling, capable of perceiving fine-grained details and capturing long-term temporal structures.

VideoLLM-online is a multimodal large language model based on Llama-3-8B-Instruct, focusing on online video understanding and video-text generation tasks.

VideoChat-R1_7B is a multimodal video understanding model based on Qwen2.5-VL-7B-Instruct, capable of processing video and text inputs and generating text outputs.

A camera motion analysis model fine-tuned based on Qwen2.5-VL-7B-Instruct, focusing on camera motion classification in videos and video-text retrieval tasks