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Llava-hf

Models by this creator

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llava-v1.6-mistral-7b-hf

llava-hf

Total Score

119

The llava-v1.6-mistral-7b-hf model is a multimodal chatbot AI model developed by the llava-hf team. It builds upon the previous LLaVA-1.5 model by using the Mistral-7B language model as its base and training on a more diverse and higher-quality dataset. This allows for improved OCR, common sense reasoning, and overall performance compared to the previous version. The model combines a pre-trained large language model with a pre-trained vision encoder, enabling it to handle multimodal tasks like image captioning, visual question answering, and multimodal chatbots. It is an evolution of the LLaVA-1.5 model, with enhancements such as increased input image resolution and improved visual instruction tuning. Similar models include the nanoLLaVA, a sub-1B vision-language model designed for efficient edge deployment, and the llava-v1.6-34b which uses the larger Nous-Hermes-2-34B language model. Model inputs and outputs Inputs Image**: The model can accept images as input, which it then processes and combines with the text prompt to generate a response. Text prompt**: The text prompt should follow the format [INST] \nWhat is shown in this image? [/INST] and describe the desired task, such as image captioning or visual question answering. Outputs Text response**: The model generates a text response based on the input image and text prompt, providing a description, answer, or other relevant information. Capabilities The llava-v1.6-mistral-7b-hf model has enhanced capabilities compared to its predecessor, LLaVA-1.5, due to the use of the Mistral-7B language model and improved training data. It can more accurately perform tasks like image captioning, visual question answering, and multimodal chatbots, leveraging its improved OCR and common sense reasoning abilities. What can I use it for? You can use the llava-v1.6-mistral-7b-hf model for a variety of multimodal tasks, such as: Image captioning**: Generate natural language descriptions of images. Visual question answering**: Answer questions about the contents of an image. Multimodal chatbots**: Build conversational AI assistants that can understand and respond to both text and images. The model's performance on these tasks makes it a useful tool for applications in areas like e-commerce, education, and customer service. Things to try One interesting aspect of the llava-v1.6-mistral-7b-hf model is its ability to handle diverse and high-quality data, which has led to improvements in its OCR and common sense reasoning capabilities. You could try using the model to caption images of complex scenes, or to answer questions that require understanding the broader context of an image rather than just its contents. Additionally, the model's use of the Mistral-7B language model, which has better commercial licenses and bilingual support, could make it a more attractive option for commercial applications compared to the previous LLaVA-1.5 model.

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Updated 5/16/2024

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llava-1.5-7b-hf

llava-hf

Total Score

115

The llava-1.5-7b-hf model is an open-source chatbot trained by fine-tuning the LLaMA and Vicuna models on GPT-generated multimodal instruction-following data. It is an auto-regressive language model based on the transformer architecture, developed by llava-hf. Similar models include the llava-v1.6-mistral-7b-hf and nanoLLaVA models. The llava-v1.6-mistral-7b-hf model leverages the mistralai/Mistral-7B-Instruct-v0.2 language model and improves upon LLaVa-1.5 with increased input image resolution and an improved visual instruction tuning dataset. The nanoLLaVA model is a smaller 1B vision-language model designed to run efficiently on edge devices. Model inputs and outputs Inputs Text prompts**: The model can accept text prompts to generate responses. Images**: The model can also accept one or more images as part of the input prompt to generate captions, answer questions, or complete other multimodal tasks. Outputs Text responses**: The model generates text responses based on the input prompts and any provided images. Capabilities The llava-1.5-7b-hf model is capable of a variety of multimodal tasks, including image captioning, visual question answering, and multimodal chatbot use cases. It can generate coherent and relevant responses by combining its language understanding and visual perception capabilities. What can I use it for? You can use the llava-1.5-7b-hf model for a range of applications that require multimodal understanding and generation, such as: Intelligent assistants**: Integrate the model into a chatbot or virtual assistant to provide users with a more engaging and contextual experience by understanding and responding to both text and visual inputs. Content generation**: Use the model to generate image captions, visual descriptions, or other multimodal content to enhance your applications or services. Education and training**: Leverage the model's capabilities to develop interactive learning experiences that combine textual and visual information. Things to try One interesting aspect of the llava-1.5-7b-hf model is its ability to understand and reason about the relationship between text and images. Try providing the model with a prompt that includes both text and an image, and see how it can use the visual information to generate more informative and relevant responses.

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Updated 5/16/2024