NOTE: This is a research preview of the LLaVA-Lightning based on MPT-7B-chat checkpoint. The usage of the model should comply with MPT-7B-chat license and agreements.
NOTE: Unlike other LLaVA models, this model can (should) be used directly without delta weights conversion!
LLaVA Model Card
Model details
Model type: LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna/MPT on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture.
Model date: LLaVA-Lightning-MPT was trained in May 2023.
Paper or resources for more information: https://llava-vl.github.io/
License: CC-BY-NC-SA 4.0
Where to send questions or comments about the model: https://github.com/haotian-liu/LLaVA/issues
Intended use
Primary intended uses: The primary use of LLaVA is research on large multimodal models and chatbots.
Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
Training dataset
558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. 80K GPT-generated multimodal instruction-following data.
Evaluation dataset
A preliminary evaluation of the model quality is conducted by creating a set of 90 visual reasoning questions from 30 unique images randomly sampled from COCO val 2014 and each is associated with three types of questions: conversational, detailed description, and complex reasoning. We utilize GPT-4 to judge the model outputs. We also evaluate our model on the ScienceQA dataset. Our synergy with GPT-4 sets a new state-of-the-art on the dataset. See https://llava-vl.github.io/ for more details.