ShareGPT4V-7B
Maintainer: Lin-Chen
75
⚙️
| 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 ShareGPT4V-7B model is an open-source chatbot trained by fine-tuning the CLP vision tower and LLaMA/Vicuna language model on the ShareGPT4V dataset and LLaVA instruction-tuning data. It was developed by the maintainer Lin-Chen and is similar to other large multimodal language models like LLaVA-13b-delta-v0, llava-v1.6-mistral-7b, and llava-llama-3-8b-v1_1.
Model inputs and outputs
The ShareGPT4V-7B model is a large language model trained to generate human-like text in response to prompts. It can accept a variety of inputs, including natural language instructions, questions, and conversations. The model's outputs are generated text that aims to be relevant, coherent, and human-like.
Inputs
- Natural language prompts, questions, or instructions
- Images (the model can generate text descriptions and captions for images)
Outputs
- Generated text responses to prompts, questions, or instructions
- Image captions and descriptions
Capabilities
The ShareGPT4V-7B model is capable of engaging in open-ended conversation, answering questions, generating creative writing, and providing detailed descriptions of images. It demonstrates strong language understanding and generation abilities, as well as the ability to reason about and describe visual information.
What can I use it for?
The ShareGPT4V-7B model is well-suited for research on large multimodal language models and chatbots. It could be used to develop interactive AI assistants, creative writing tools, image captioning systems, and other applications that require natural language generation and multimodal understanding.
Things to try
One interesting thing to try with the ShareGPT4V-7B model is to provide it with a sequence of images and ask it to generate a coherent, flowing narrative based on the visual information. The model's ability to understand and reason about visual content, combined with its language generation capabilities, could result in compelling and creative storytelling.
Another thing to explore is the model's performance on specialized tasks or datasets, such as scientific question answering or visual reasoning benchmarks. Comparing the ShareGPT4V-7B model's results to other large language models could yield valuable insights about its strengths, weaknesses, and overall capabilities.
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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