Qnguyen3
Models by this creator
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nanoLLaVA
121
nanoLLaVA is a "small but mighty" 1B vision-language model designed to run efficiently on edge devices. It is based on the Quyen-SE-v0.1 base language model and the google/siglip-so400m-patch14-384 vision encoder. Similar models include the Qwen-VL series from Alibaba Cloud, which are large vision-language models with a range of capabilities. Model inputs and outputs Inputs Text prompt**: A text prompt describing the image to be processed Image**: An image to be analyzed and described Outputs Multimodal description**: A detailed description of the image, grounding relevant objects and their relationships Capabilities The nanoLLaVA model has demonstrated strong performance on a variety of vision-language tasks, including visual question answering, text-based VQA, science QA, and referring expression comprehension. It achieves SOTA results on several benchmarks while maintaining a compact model size suitable for edge deployment. What can I use it for? The nanoLLaVA model can be used for a variety of applications that require efficiently integrating vision and language understanding, such as: Intelligent assistants**: Providing detailed descriptions and answering questions about visual content Accessibility tools**: Generating alt text and captions for images to improve accessibility Automated reporting**: Summarizing visual observations and insights from images or documents Visual search and retrieval**: Enabling multimodal search and browsing of image databases Things to try Experiment with the nanoLLaVA model on a range of visual and multimodal tasks beyond the standard benchmarks. Explore its few-shot or zero-shot capabilities to see how it can adapt to novel scenarios without extensive fine-tuning. Additionally, investigate ways to optimize its performance and efficiency for your specific use cases.
Updated 5/27/2024
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nanoLLaVA-1.5
93
nanoLLaVA-1.5 is an improved sub-1 billion parameter vision-language model created by qnguyen3. It builds upon the previous nanoLLaVA model by utilizing a more powerful base language model, Quyen-SE-v0.1, and a high-quality vision encoder, google/siglip-so400m-patch14-384. This allows nanoLLaVA-1.5 to achieve improved performance across a variety of multimodal benchmarks compared to its predecessor, while still maintaining a compact model size suitable for edge device deployment. Model inputs and outputs Inputs Text prompt**: A text prompt describing an image, typically in a conversational format. Image**: An image that the model will use to generate a description. Outputs Image description**: A detailed textual description of the provided image, generated by the model. Capabilities nanoLLaVA-1.5 is capable of generating detailed, coherent descriptions of images across a wide range of subject matter. It has demonstrated strong performance on benchmarks such as VQA v2, TextVQA, ScienceQA, POPE, MMMU, GQA, and MM-VET, surpassing the previous nanoLLaVA model in many areas. What can I use it for? nanoLLaVA-1.5 can be used in a variety of applications that involve understanding and describing visual content, such as: Image captioning**: Automatically generating captions for images in applications like social media, e-commerce, or content management. Visual question answering**: Answering questions about the contents of an image in a conversational interface. Multimodal chatbots**: Building intelligent chatbots that can understand and respond to both text and visual inputs. Things to try One interesting aspect of nanoLLaVA-1.5 is its compact size and ability to run efficiently on edge devices. This makes it well-suited for applications where low-latency, on-device inference is important, such as in mobile apps or embedded systems. Developers can explore ways to integrate nanoLLaVA-1.5 into their projects and leverage its multimodal capabilities to create innovative user experiences.
Updated 8/7/2024