Guide for adding support for new LLM or VLM models in Megatron-Bridge. Covers bridge, provider, recipe, tests, docs, and examples. Use when the user asks to add, support, onboard, or integrate a new model, or when creating bridges, providers, or recipes for a new model family.
Convert single-node scripts to multi-node Slurm sbatch jobs and debug common multi-node failures. Covers srun-native vs uv run torch.distributed approaches, container setup, NCCL timeouts, OOM sizing for MoE models, and interactive allocation. Use when creating Slurm scripts, scaling to multi-node, or debugging multi-node job failures.
Developer environment setup, CI/CD workflows, and CI failure debugging for Megatron Bridge. Covers container-based development, uv package management, pre-commit hooks, running tests, CI failure investigation, and common pitfalls. Use when onboarding, setting up a dev environment, troubleshooting build issues, or investigating CI failures.
Code style and quality guidelines for Megatron Bridge. Covers naming, type hints, ruff enforcement, keyword-arg safety, copyright headers, logging, and common anti-patterns. Auto-invoked during code review and when writing new code.
Structured framework for verifying numerical parity of HF<->MCore weight conversions. References existing tools and the add-model-support skill. Use when debugging weight mismatches, verifying checkpoint round-trips, or choosing which verification tool to run.
Validate and use MoE expert-parallel communication overlap in Megatron-Bridge, including overlap_moe_expert_parallel_comm, delay_wgrad_compute, and flex dispatcher backends such as DeepEP and HybridEP.
Validate and use CUDA graph capture in Megatron Bridge, including local full-iteration graphs and Transformer Engine scoped graphs for attention, MLP, and MoE modules.
Run Megatron-LM (MLM) and Megatron Bridge training with mock or real data. Covers correlation testing, available recipes, and multi-GPU examples. Use when running training, comparing MLM vs Bridge, or translating configs.