Efficient miniaturized model architecture
InternLM-XComposer achieves an energy efficiency ratio comparable to that of the GPT-4V using only 7B parameters through innovative modeling, an achievement that is a landmark in the multimodal field.
Technical Principles: The model adopts the attention mechanism optimization and parameter sharing strategy, which significantly improves the efficiency of parameter usage. In particular, the computational efficiency is maintained by sparse attention pattern when dealing with very long text.
performance: On the standard evaluation dataset, the model is within 10% of GPT-4V in tasks such as image understanding and text generation, while the model volume is only about 1/20 of GPT-4V.
- Hardware advantage: 24GB GPU can run smoothly
- Optimized solution: 4-bit quantized version available to accommodate lower-end devices
- Ease of deployment: open source features support rapid localized deployment
This breakthrough allows high-quality multimodal AI technology to be more widely applied to all types of devices and scenarios.
This answer comes from the articleInternLM-XComposer: a multimodal macromodel for outputting very long text and image-video comprehensionThe
