🤖AI & LLM

An important obstacle in the development of AGI is the massive amount of processing power needed to train AI models. Neurolov has developed a novel program for renting GPUs to overcome this issue. Through an easy-to-use interface, Neurolov makes high-performance computing capability more accessible to a wider range of users. Even small-scale researchers and developers will have access to the computational resources required for training complex AI and ML models thanks to this GPU power sharing arrangement.

AI Modeling

8.1 Comprehensive AI Model Support

Neurolov offers extensive support for a wide range of AI models, including CNNs for image processing, RNNs and LSTMs for sequence data, Transformer models for NLP tasks, GANs for content creation, and Reinforcement Learning models for decision making tasks. Pre-trained versions of popular models such as BERT, GPT, and ResNet are available for quick deployment and fine-tuning.

8.2 Advanced LLM Fine-tuning

The platform provides advanced LLM fine-tuning capabilities, supporting prompt engineering, few-shot learning, and efficient fine-tuning techniques like LoRA. Users can optimize hyper parameters for enhanced model performance and leverage distributed training for large language models, enabling easy adaptation of state-of-the-art LLMs to specific use cases. 8.3 Intelligent Model Selection

Neurolov features an intelligent model selection algorithm that analyzes input data characteristics, task requirements, and available GPU resources to recommend the most suitable model architecture and size. This ensures efficient resource utilization and optimal task performance across various AI applications.

8.4 Federated Learning Implementation To facilitate collaborative model training while preserving data privacy, Neurolov implements federated learning. This includes secure aggregation of model updates from multiple participants, differential privacy techniques to prevent data leakage, adaptive federated optimization algorithms for faster convergence, and support for both cross-silo and cross-device federated learning scenarios.

Federated Learning Implementation