> ## Documentation Index > Fetch the complete documentation index at: https://internal.nolano.ai/llms.txt > Use this file to discover all available pages before exploring further. # Introduction > Automated Platform for Building Large Foundation Models Nolano.AI Logo

# Nolano AI: An Automated Platform for Developing Large-Scale Foundation Models **For AI/ML Researchers** at the frontier of foundation modeling research. The library seeks to abstract away the complexity of HPC, distributed training, tooling, systems and non-AI related engineering overhead involved in getting the foundation model research going.\ We allow AI researchers to focus on what matters most: **breakthrough innovations in foundation models**, by getting their experiment going in minutes, not months. ## What We Abstract Away Our platform eliminates the boilerplate parts of foundation model research so you can focus on pushing the frontier: Months of engineering setup for distributed training across clusters - we handle the infrastructure complexity so you can focus on model innovation. Deep expertise in HPC, CUDA, parallelization strategies, and hardware optimization - no need to become a systems expert to train foundation models. Redundant implementation of data loaders, training loops, and evaluation pipelines - our battle-tested pipelines handle petabyte-scale datasets efficiently. Complex configuration management across different experiments and modalities - simplified configuration with intelligent defaults and validation. ## Supported Modalities Build state-of-the-art language and code models with support for all major architectures. See tutorial Create powerful forecasting models for both univariate and multivariate time series data. See tutorial ### Key Features * Custom tokenizers for text & time series * BPE, WordPiece, SentencePiece implementations * Supports major dense (Encoder/Decoder) and Mixture-of-Experts architectures * Qwen, DeepSeek, Chronos, and TimeMoE * Multi-node, multi-GPU training out of the box * Data, model, and pipeline parallelism * Gradient accumulation and mixed precision training * Automatic sharding and load balancing * High-performance data loading and preprocessing * Memory-efficient streaming for large datasets * Automatic data shuffling and batching * Adaptive learning rate scheduling * Gradient clipping and stability monitoring * Memory optimization techniques * Dynamic loss scaling for mixed precision * Custom evaluation metrics support * Real-time training metrics and visualization * Supports Weights & Biases integration * Automatic checkpointing and versioning * Integration with Huggingface * Dynamic scaling based on workload * Optimized for cloud and on-premise deployments ## What You Get Start your first experiment immediately with our streamlined workflow Data preparation, training, evaluation and inference with simple CLI commands Scalable distributed training, hyperparameter transfer and automated optimization Models ready for deployment without additional engineering overhead Ready to get started? Check out our [Quickstart Guide](/quickstart) to begin building your first foundation model. *** *Nolano AI: Democratizing Foundation Model Research*