LWM-Chat-1M-Jax
Maintainer: LargeWorldModel
124
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| Property | Value |
|---|---|
| Run this model | Run 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 LWM-1M-Jax is an open-source auto-regressive vision-language model developed by LargeWorldModel. It is based on the transformer architecture and trained on a diverse dataset including the Books3 dataset, Laion-2B-en, COYO-700M, WebVid10M, InternVid10M, Valley-Instruct-73K, and Video-ChatGPT.
The LWM-1M-Jax can be compared to similar models like the LWM-Text-1M which is a language-only model, and the LLaVA which is a multimodal chatbot. However, the LWM-1M-Jax integrates both text and visual data, providing unique capabilities compared to language-only models.
Model inputs and outputs
Inputs
- Text: The model can take in natural language text as input.
- Images: The model can also accept image data as input, with a focus on high-resolution images of at least 256 pixels.
- Videos: In addition to text and images, the model can process video data from sources like WebVid10M and InternVid10M.
Outputs
- Text: The primary output of the
LWM-1M-Jaxis generated text, allowing it to engage in tasks like language generation, question answering, and dialogue. - Visual understanding: Given image or video inputs, the model can also provide insights and analysis related to the visual content.
Capabilities
The LWM-1M-Jax model excels at tasks that require understanding and reasoning across both text and visual data. For example, it could be used to generate captions for images, answer questions about the contents of a video, or engage in multimodal dialogue that seamlessly integrates text and visual elements.
What can I use it for?
The LWM-1M-Jax model could be useful for a variety of applications that involve multimodal information processing, such as:
- Content creation: Generating descriptive text to accompany images or videos
- Intelligent assistants: Building AI assistants that can understand and respond to queries involving both text and visual data
- Multimedia analysis: Extracting insights and information from collections of text, images, and videos
Things to try
One interesting aspect of the LWM-1M-Jax model is its ability to integrate knowledge from diverse data sources, including textual, image, and video content. This could allow for unique applications, such as generating fictional stories that combine details and events from multiple media types, or providing detailed analyses of complex topics that draw upon a rich, multimodal knowledge base.
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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