llava-v1.5-7b
Maintainer: liuhaotian
274
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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
llava-v1.5-7b is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model based on the transformer architecture. The model was created by liuhaotian, and similar models include llava-v1.5-7B-GGUF, LLaVA-13b-delta-v0, llava-v1.6-mistral-7b, and llava-1.5-7b-hf.
Model inputs and outputs
llava-v1.5-7b is a large language model that can take in textual prompts and generate relevant responses. The model is particularly designed for multimodal tasks, allowing it to process and generate text based on provided images.
Inputs
- Textual prompts in the format "USER: <prompt>\nASSISTANT:"
- Optional image data, indicated by the
<image>token in the prompt
Outputs
- Generated text responses relevant to the given prompt and image (if provided)
Capabilities
llava-v1.5-7b can perform a variety of tasks, including:
- Open-ended conversation
- Answering questions about images
- Generating captions for images
- Providing detailed descriptions of scenes and objects
- Assisting with creative writing and ideation
The model's multimodal capabilities allow it to understand and generate text based on both textual and visual inputs.
What can I use it for?
llava-v1.5-7b can be a powerful tool for researchers and hobbyists working on projects related to computer vision, natural language processing, and artificial intelligence. Some potential use cases include:
- Building interactive chatbots and virtual assistants
- Developing image captioning and visual question answering systems
- Enhancing text generation models with multimodal understanding
- Exploring the intersection of language and vision in AI
By leveraging the model's capabilities, you can create innovative applications that combine language and visual understanding.
Things to try
One interesting thing to try with llava-v1.5-7b is its ability to handle multi-image and multi-prompt generation. This means you can provide multiple images in a single prompt and the model will generate a response that considers all the visual inputs. This can be particularly useful for tasks like visual reasoning or complex scene descriptions.
Another intriguing aspect of the model is its potential for synergy with other large language models, such as GPT-4. As mentioned in the LLaVA-13b-delta-v0 model card, the combination of llava-v1.5-7b and GPT-4 set a new state-of-the-art on the ScienceQA dataset. Exploring these types of model combinations and their capabilities can lead to exciting advancements in the field of multimodal AI.
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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