Comparison of Technical Advantages
Compared with mainstream multimodal models such as GPT-4V, InternLM-XComposer has the following significant advantages:
1. Parametric efficiency
Achieve performance comparable to GPT-4V on multiple tasks using only 7B parameters, with lower compute resource consumption.
2. Context processing capability
Supports 96K ultra-long context processing, much higher than the context window of most mainstream models.
3. Open source features
- Fully open source, including model weights and training code
- Support for local deployment and secondary development
- No need to pay for API calls
4. Multimodal integration capability
It performs particularly well in video understanding, supporting fine-grained video frame analysis and long streaming times.
5. Hardware adaptability
A 4-bit quantized version is available that can run on resource-limited devices and is more flexible than closed-source models.
Taken together, InternLM-XComposer offers better accessibility, more flexible deployment options and more efficient resource utilization while maintaining high performance.
This answer comes from the articleInternLM-XComposer: a multimodal macromodel for outputting very long text and image-video comprehensionThe
