About this Skill
Field-tested methodology and concrete recipes for training and operating large-scale LLM/VLM/multi-modal models end to end - choosing and benchmarking accelerators, storage and network; SLURM/Kubernetes orchestration; maximizing training throughput and fitting models in memory; diagnosing and surviving training instabilities, NaN/Inf, and hardware/job failures; checkpointing and fault tolerance; inference…
Why we picked it
A task from ML Engineering Skills. Retain the upstream supporting files and follow the repository’s setup instructions.
Before you use it
- The relevant ML stack and cluster access. Cluster evaluation runs hardware/software checks and needs the upstream test dependencies.
Sources & references
Original sources are linked below. Practical starting points are editorial suggestions from SkillHey.
