|
--- |
|
license: apache-2.0 |
|
tags: |
|
- llava |
|
inference: false |
|
pipeline_tag: image-text-to-text |
|
--- |
|
|
|
# LLaVA Model Card |
|
|
|
## Model details |
|
|
|
**Model type:** |
|
The first-stage pretrained checkpoint of LLaVA. |
|
LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. |
|
It is an auto-regressive language model, based on the transformer architecture. |
|
|
|
**Model date:** |
|
LLaVA was trained in April 2023. |
|
|
|
**Paper or resources for more information:** |
|
https://llava-vl.github.io/ |
|
|
|
**License:** |
|
Apache License 2.0 |
|
|
|
**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 |
|
595K filtered image-text pairs from CC3M. |
|
150K 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. |
|
|
