ChartMoE
| Property | Value |
|---|---|
| License | Apache-2.0 |
| Pipeline Tag | Visual Question Answering |
| Paper | Research Paper |
| Framework | PyTorch, Transformers |
What is ChartMoE?
ChartMoE is an advanced multimodal large language model that specializes in chart analysis and manipulation. Built upon the InternLM-XComposer2 architecture, it incorporates a Mixture-of-Expert connector to enable sophisticated chart processing capabilities. This model represents a significant advancement in visual-language processing, particularly focused on chart understanding and manipulation.
Implementation Details
The model is implemented using the Transformers library and PyTorch framework. It can be easily loaded using the HuggingFace Transformers library, requiring minimal setup code. The model operates in half-precision (FP16) for optimal performance and memory usage.
- Simple integration with HuggingFace Transformers
- Optimized for GPU execution with half-precision support
- Built on proven InternLM-XComposer2 architecture
Core Capabilities
- Chart Understanding: Deep comprehension of chart contents and relationships
- Chart Replotting: Ability to recreate charts with modifications
- Chart Editing: Interactive modification of chart elements
- Highlighting: Emphasis of specific chart components
- Chart Transformation: Converting between different chart types
Frequently Asked Questions
Q: What makes this model unique?
ChartMoE stands out for its specialized Mixture-of-Expert connector architecture and comprehensive chart manipulation capabilities, making it particularly effective for complex chart-related tasks that require both visual understanding and generation abilities.
Q: What are the recommended use cases?
The model is ideal for applications requiring sophisticated chart analysis, modification, and transformation. This includes financial analysis, data visualization tools, automated reporting systems, and educational platforms where chart understanding and manipulation are crucial.
