RaiMind
How to train an Multimodal LLM tailored for a specific
industry or business?
Training large models for general or specialized services involves multiple steps Including demand identification, planning and design, data collection and cleansing, model training &/ fine tuning, validation, model deployment and inferencing. Continuously and iteratively improve Multimodal LLM capabilities through real-time monitoring, close-loop feedback and fine tuning.
Find real pain points instead of perceived ones in specific scenarios, strengthen gaps around specific pain points, prioritize solutions, and zoom-in an effective starting point.
Multi-modal mapping of data can create more pure, reliable and valuable data insights, which will greatly improve application effects and reduce usage costs.
Based on demand, we conduct research and development and sandbox simulation training in the digital twin representation of actual scenarios, and complete the deployment of digital/virtual and physical/embodied artificial intelligence teams at the same time.
Perceive multi-modal data of application scenarios, and provide auxiliary (or human-in-the-loop) decision-making to predict hidden dangers, ensure system stability, and optimize training through real-time monitoring and feedback.
