Selecting an appropriate development framework is essential for working with MLLMs. It’s important to choose a framework that not only supports the specific modalities relevant to respective projects but also fits well with the existing technology stack and development practices.
NVIDIA NeMo™ is an end-to-end platform for developing custom generative AI. The NeMo framework provides a comprehensive library designed to facilitate the creation and fine-tuning of MLLMs across various data modalities.
- NeVA (LLaVA): Provides training, fine-tuning, and inference capabilities.
- VideoNeVA (LLaVA): Provides training and inference capabilities for video modality.
The effectiveness of an MLLM heavily depends on the quality and alignment of the multimodal data it’s trained on. This involves collecting datasets that include aligned pairs or groups of different modalities, such as text-image pairs or video with captions. Proper preprocessing and normalization of this data is crucial to ensure that it can be effectively used to train the model.
Leveraging a pretrained model can significantly reduce the need for extensive computational resources and provide a shortcut to achieving effective results. Fine-tuning this model on a specific dataset allows it to adapt to the particular characteristics and requirements of an application, enhancing its performance and relevance.
Once the model is set up, it’s important to test it extensively with real-world data and scenarios. This testing phase is critical to understanding how well the model performs and identifying any areas where it may need further refinement. Continuous iteration based on performance feedback is key to developing a robust MLLM that reliably meets your objectives.
Deploying an MLLM involves integrating it into a suitable operational environment where it can receive inputs and generate outputs as required. Post-deployment, it’s important to monitor the model’s performance continuously and adjust its configuration as needed to maintain its effectiveness and efficiency.