XVERSE-V-13B

Multimodal model

A large multimodal model for visual question answering, trained on a dataset of 2.1B image-text pairs and 8.2M instruction sequences.

GitHub

77 stars
4 watching
4 forks
Language: Python
last commit: 7 months ago

Related projects:

Repository Description Stars
xverse-ai/xverse-13b A large language model developed to support multiple languages and applications 649
xverse-ai/xverse-moe-a36b Develops and publishes large multilingual language models with advanced mixing-of-experts architecture. 36
xverse-ai/xverse-moe-a4.2b Developed by XVERSE Technology Inc. as a multilingual large language model with a unique mixture-of-experts architecture and fine-tuned for various tasks such as conversation, question answering, and natural language understanding. 36
xverse-ai/xverse-65b A large language model developed by XVERSE Technology Inc. using transformer architecture and fine-tuned on diverse data sets for various applications. 132
xverse-ai/xverse-7b A multilingual large language model developed by XVERSE Technology Inc. 50
yfzhang114/slime Develops large multimodal models for high-resolution understanding and analysis of text, images, and other data types. 137
zhourax/vega Develops a multimodal task and dataset to assess vision-language models' ability to handle interleaved image-text inputs. 33
openbmb/viscpm A family of large multimodal models supporting multimodal conversational capabilities and text-to-image generation in multiple languages 1,089
tsb0601/mmvp An evaluation framework for multimodal language models' visual capabilities using image and question benchmarks. 288
tiger-ai-lab/uniir Trains and evaluates a universal multimodal retrieval model to perform various information retrieval tasks. 110
opengvlab/multi-modality-arena An evaluation platform for comparing multi-modality models on visual question-answering tasks 467
nvlabs/eagle Develops high-resolution multimodal LLMs by combining vision encoders and various input resolutions 539
mlpc-ucsd/bliva A multimodal LLM designed to handle text-rich visual questions 269
vimalabs/vima An implementation of a general-purpose robot learning model using multimodal prompts 774
multimodal-art-projection/omnibench Evaluates and benchmarks multimodal language models' ability to process visual, acoustic, and textual inputs simultaneously. 14