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# LLaVA GPTQ Model Card

**NOTE: To use the GPTQ quantized LLaVA checkpoints, you need to use `text-generation-webui`, and the support for LLaMA-2 is WIP. We are working on the PR.**

You can try it out [here](https://github.com/haotian-liu/text-generation-webui).

Instructions and detailed stories here: https://github.com/haotian-liu/LLaVA/issues/310#issuecomment-1657552223

PR: https://github.com/oobabooga/text-generation-webui/pull/3377

These files are GPTQ model files for [LLaVA-LLaMA-2-13B-Chat-Preview](https://huggingface.co/liuhaotian/llava-llama-2-13b-chat-lightning-preview).

## Model details

**Model type:**
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-LLaMA-2-13B-Chat-Preview was trained in July 2023.

**Paper or resources for more information:**
https://llava-vl.github.io/

## License
Llama 2 is licensed under the LLAMA 2 Community License, 
Copyright (c) Meta Platforms, Inc. All Rights Reserved.

**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.