llava-1.5-7b-hf
Maintainer: llava-hf
119
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| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
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Model overview
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.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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