cogvlm2-llama3-chat-19B
Maintainer: THUDM
153
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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 cogvlm2-llama3-chat-19B model is part of the CogVLM2 series of open-source models developed by THUDM. It is based on the Meta-Llama-3-8B-Instruct model, with significant improvements in benchmarks such as TextVQA and DocVQA. The model supports up to 8K content length and 1344x1344 image resolution, and provides both English and Chinese language support.
The cogvlm2-llama3-chinese-chat-19B model is a similar Chinese-English bilingual version of the same architecture. Both models are 19B in size and designed for image understanding and dialogue tasks.
Model inputs and outputs
Inputs
- Text: The models can take text-based inputs, such as questions, instructions, or prompts.
- Images: The models can also accept image inputs up to 1344x1344 resolution.
Outputs
- Text: The models generate text-based responses, such as answers, descriptions, or generated text.
Capabilities
The CogVLM2 models have achieved strong performance on a variety of benchmarks, competing with or surpassing larger non-open-source models. For example, the cogvlm2-llama3-chat-19B model scored 84.2 on TextVQA and 92.3 on DocVQA, while the cogvlm2-llama3-chinese-chat-19B model scored 85.0 on TextVQA and 780 on OCRbench.
What can I use it for?
The CogVLM2 models are well-suited for a variety of applications that involve image understanding and language generation, such as:
- Visual question answering: Use the models to answer questions about images, diagrams, or other visual content.
- Image captioning: Generate descriptive captions for images.
- Multimodal dialogue: Engage in contextual conversations that reference images or other visual information.
- Document understanding: Extract information and answer questions about complex documents, reports, or technical manuals.
Things to try
One interesting aspect of the CogVLM2 models is their ability to handle both Chinese and English inputs and outputs. This makes them useful for applications that require language understanding and generation in multiple languages, such as multilingual customer service chatbots or translation tools.
Another intriguing feature is the models' high-resolution image support, which enables them to work with detailed visual content like engineering diagrams, architectural plans, or medical scans. Developers could explore using the CogVLM2 models for tasks like visual-based technical support, design review, or medical image analysis.
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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