Llava-hf
Models by this creator
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llava-v1.6-mistral-7b-hf
132
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.
Updated 5/28/2024
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llava-1.5-7b-hf
119
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.
Updated 5/28/2024
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llava-v1.6-34b-hf
50
The llava-v1.6-34b-hf model is the latest version of the LLaVA chatbot, developed by the llava-hf team. It leverages the NousResearch/Nous-Hermes-2-Yi-34B large language model as its base, and has been further trained on a diverse dataset of image-text pairs and multimodal instruction-following data. Compared to the previous LLaVA-1.5 model, this version improves upon the OCR capabilities and common sense reasoning by increasing the input image resolution and using a more comprehensive training dataset. Similar models in the LLaVA family include the llava-v1.6-mistral-7b-hf which uses the mistralai/Mistral-7B-Instruct-v0.2 model as its base, and the llava-v1.5-7b-hf which is a smaller 7B version of the original LLaVA-1.5 model. Model inputs and outputs The llava-v1.6-34b-hf model is a multimodal language model, capable of processing both text and image inputs. It can be used for a variety of tasks, including image captioning, visual question answering, and multimodal chatbot interactions. Inputs Text Prompt**: The text input that provides context and instructions for the model. This can include questions, commands, or conversational prompts. Image**: One or more images that the model should analyze and incorporate into its response. Outputs Generated Text**: The model's response, which can range from a single sentence to multiple paragraphs, depending on the input prompt and the task at hand. Capabilities The llava-v1.6-34b-hf model excels at tasks that require understanding and reasoning about both visual and textual information. For example, it can be used to answer questions about the contents of an image, generate captions for images, or engage in multimodal conversations where the user provides both text and images. The model's improved OCR and common sense reasoning capabilities, as compared to the previous LLaVA-1.5 version, make it well-suited for tasks that involve processing real-world visual information, such as interpreting diagrams, charts, or other complex images. What can I use it for? The llava-v1.6-34b-hf model is primarily intended for research purposes, as it can be used to explore the potential of large-scale multimodal language models. Potential applications include: Chatbots and virtual assistants**: The model can be used to build chatbots and virtual assistants that can engage in natural, multimodal conversations with users. Automated image captioning and visual question answering**: The model can be used to generate captions for images and answer questions about their contents. Multimodal content generation**: The model can be used to generate text that is conditioned on both textual and visual inputs, such as generating product descriptions or creative writing prompts based on images. See the model hub to explore other versions of the LLaVA model that may be better suited for your specific use case. Things to try One interesting aspect of the llava-v1.6-34b-hf model is its ability to handle multiple images within a single prompt. This allows you to experiment with complex multimodal reasoning tasks, where the model needs to synthesize information from several visual inputs to generate a coherent response. Another area to explore is the model's performance on specialized tasks or datasets. While the model was trained on a broad range of multimodal data, it may excel at certain types of visual-linguistic tasks more than others. Trying the model on benchmarks or custom datasets related to your area of interest can help you understand its strengths and limitations. Finally, the model supports various optimization techniques, such as 4-bit quantization and the use of Flash-Attention 2, which can significantly improve the inference speed and memory efficiency of the model. Experimenting with these optimizations can help you deploy the model in more resource-constrained environments, such as mobile devices or edge computing systems.
Updated 7/2/2024