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README.md

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+**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!**
+
+<br>
+<br>
+
+# 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:**
+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
+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.