| inference: false | |
| license: apache-2.0 | |
| library_name: LLaVA | |
| pipeline_tag: image-text-to-text | |
| tags: | |
| - llava | |
| <br> | |
| <br> | |
| # LLaVA Model Card | |
| ## Model details | |
| **Model type:** | |
| LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. | |
| It is an auto-regressive language model, based on the transformer architecture. | |
| Base LLM: [NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) | |
| **Model date:** | |
| LLaVA-v1.6-34B was trained in December 2023. | |
| **Paper or resources for more information:** | |
| https://llava-vl.github.io/ | |
| ## License | |
| [NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) license. | |
| **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. | |
| - 158K GPT-generated multimodal instruction-following data. | |
| - 500K academic-task-oriented VQA data mixture. | |
| - 50K GPT-4V data mixture. | |
| - 40K ShareGPT data. | |
| ## Evaluation dataset | |
| A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs. |