inference: false
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.
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.
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.