Raumberg/myllm

Raumberg/myllm is a low-traction (17 stars, 1 fork) project that attempts to enter the highly competitive and technically demanding niche of distributed LLM training. Quantitatively, the project has zero velocity and has failed to gain any significant community interest over its 400+ day lifespan. From a competitive standpoint, it faces insurmountable moats from established industry-standard frameworks such as Microsoft's DeepSpeed, NVIDIA's Megatron-LM, and PyTorch's native Fully Sharded Data Parallel (FSDP). These alternatives are backed by massive engineering teams and validated on clusters with thousands of GPUs. Frontier labs like OpenAI and Anthropic treat distributed training infrastructure as a core proprietary advantage, while cloud providers (AWS SageMaker, Google Vertex AI) offer managed versions of these capabilities, leaving little room for a small-scale independent framework. The project likely serves as a personal learning exercise or a reference implementation for basic distributed patterns rather than a production-ready tool. It is effectively displaced by any standard PyTorch or MosaicML ecosystem tool.