![]() As it has been trained on data generated by ChatGPT, it cannot be used for commercial purposes due to ChatGPT’s terms of use, which prevent developers from using it to train competing commercial models.Ĭreating an AI product also comes with many challenges beyond training a model, and LLaVA is not yet a contender against GPT-4V, which is convenient, easy to use, and integrated with other OpenAI tools, such as DALL-E 3 and external plugins. However, LLaVA 1.5 does come with a caveat. More examples: /UfxgrC3E2w- Matt Shumer October 6, 2023 GPT-4-Vision has a new open-source competitor, LLaVA v1.5. ![]() Users are sharing interesting examples where LLaVA 1.5 is able to handle complex prompts. The code and dataset are also accessible, encouraging further development and customization. (It is worth noting that measuring the performance of LMMs is complicated and benchmarks might not necessarily reflect performance in real-world applications.) LLaVA 1.5 outperforms other open source LMMs on 11 multimodal benchmarks (Image Credit: ) The future of open source LLMsĪn online demo of LLaVA 1.5 is available, showcasing impressive results from a small model that can be trained and run on a tight budget. The entire training data consisted of around 600,000 examples and took about a day on eight A100 GPUs, costing only a few hundred dollars.Īccording to the researchers, LLaVA 1.5 outperforms other open-source LMMs on 11 out of 12 multimodal benchmarks. ![]() The researchers also added several open-source visual question-answering datasets to the training data, scaled the input image resolution, and gathered data from ShareGPT, an online platform where users can share their conversations with ChatGPT. LLaVA 1.5 improves upon the original by connecting the language model and vision encoder through a multi-layer perceptron (MLP), a simple deep learning model where all neurons are fully connected. This method generated 158,000 training examples to train LLaVA for visual instructions, and it proved to be very effective. Researchers provided the LLM with image descriptions and metadata, prompting it to create conversations, questions, answers, and reasoning problems based on the image content. The original LLaVA model used the text-only versions of ChatGPT and GPT-4 to generate training data for visual fine-tuning. LLaVA’s language model is Vicuna, a version of Meta’s open source LLaMA model fine-tuned for instruction-following. It is used in advanced text-to-image models like DALL-E 2. Developed by OpenAI in 2021, CLIP learns to associate images and text by training on a large dataset of image-description pairs. LLaVA 1.5 uses a CLIP (Contrastive Language–Image Pre-training) model as its visual encoder. ![]() This stage is often challenging due to its compute-intensive nature and the need for a large dataset of carefully curated examples. The second stage, visual instruction tuning, enables the model to follow and respond to prompts involving visual content. The first stage, vision-language alignment pretraining, uses image-text pairs to align the visual features with the language model’s word embedding space. Training an instruction-following LMM usually involves a two-stage process.
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