Microsoft Magma is a research codebase for a single large multimodal vision-language-action (VLA) foundation model, as opposed to a general-purpose application. The core is an 8B-parameter model built on LLaMA-3-8B-Instruct with a ConvNeXt vision encoder, capable of understanding images/video and producing both textual responses and discretized robot/UI actions. The repository contains the full training stack: model definitions, data preprocessing, dataset-specific constructors, mixed RLDS/PyTorch data loaders, action tokenization, DeepSpeed training configs, and evaluation hooks for robotics benchmarks like LIBERO and SimplerEnv. It also ships FastAPI servers and demo agents for UI automation and robot trajectory planning, but these are secondary to the model training and data pipeline core, making this a hybrid repo with a dominant data-research character. The data lineage is central to the project. Raw heterogeneous datasets—Open X-Embodiment, Ego4D, EpicKitchen, SeeClick, LLaVA, and the in-house Magma-820K—are normalized into a unified multimodal conversation format via per-dataset Constructor subclasses. Visual point tracks from CoTracker2 are clustered, quantized, and rendered as 'Trace-of-Mark' overlays; bounding boxes and labels are rendered as 'Set-of-Mark' overlays. Continuous robot actions are discretized into token IDs aligned with the LLM vocabulary, allowing a standard causal LM objective to train action prediction alongside visual understanding. The resulting model can be deployed via the included FastAPI server for interactive inference. Reproducing the full pipeline requires access to gated or proprietary datasets, CoTracker2 checkpoints, a custom transformers fork, and significant GPU resources, so end-to-end reproduction is not turnkey, though inference on the released HuggingFace checkpoint is more accessible.