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-flint-v0.5-1B was trained in Nov 2023.
This model is an implementation of Llava using the TinyLlama 1.1b as the frozen llm model
It's designed to be able to run in low-resource environments We plan to release further versions designed for specific tasks so stay tuned.
Paper or resources for more information on the original Llava: https://llava-vl.github.io/
License Apache 2 (TinyLlama) Where to send questions or comments about the model: ask me here on huggingface :)
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. 450K academic-task-oriented VQA 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.
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