---
language:
- en
pipeline_tag: visual-question-answering
library_name: transformers

inference: false
---

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# BLIVA Model Card

## Model details

**Model type:**
BLIVA is an open-source Vision-Languagde model trained by initializing from InstructBLIP and alignment with Vicuna on multimodal instruction-finetuning data.
It composes of an EVA-CLIP vision encoder, a Q-Former, a projection layer and an auto-regressive language model, based on the decoder only transformer architecture.

**Model date:**
BLIVA_Vicuna was trained in July 2023.

**Paper or resources for more information:**
https://gordonhu608.github.io/bliva/

**License:**
Non-commercial bespoke license

**Where to send questions or comments about the model:**
https://github.com/mlpc-ucsd/BLIVA

## Intended use
**Primary intended uses:**
The primary use of BLIVA is research on large multimodal models. 

**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
Pre-train data: 558K filtered image-text pairs from LAION,CC-3M, and SBU. Selected by LLaVA.

Instruction-finetuning data: COCO-Caption, TextCaps, VQAv2, OKVQA, A-OKVQA, LLaVA-150K, OCR-VQA. 

## Evaluation dataset
For zero-shot evaluation on general image task, we selected Nocaps, Flickr30K, VizWiz, Visual Spaial Reasoning (VSR), IconQA, Visual Dialog, ScienceQA, MSRVTT QA, TextVQA and Hateful Memes. 

For zero-shot evaluation on text-rich image OCR task, we selected ST-VQA, OCR-VQA, Text-VQA, and Doc-VQA.

More detials are in our github, https://github.com/mlpc-ucsd/BLIVA