LLM Foundry is a production-grade framework for training, finetuning, evaluating, and serving large language models, with a focus on the MPT architecture and HuggingFace integration. It provides a full pipeline from data preparation (MDS streaming), to model definition (custom MPT, HF wrappers, embedding models), to distributed training (Composer, FSDP, TP), and to evaluation (ICL benchmarks, gauntlets). The architecture is modular, using YAML-driven configuration and a central registry pattern for extensibility. The codebase includes dozens of callbacks for monitoring and training stabilization, custom optimizers, and efficient data collation (bin packing). While tightly integrated with MosaicML's Composer and platform, the core registry and layer abstractions are reusable. The repository is actively maintained with comprehensive test suites, though some features remain experimental and several dependencies (flash attention, megablocks) require careful version management.