nemo-mbridge-perf-sequence-packing
Validate and use packed sequences and long-context training in Megatron-Bridge, distinguishing offline packed SFT for LLMs from in-batch packing for VLMs, and applying the right CP constraints.
npx skills add NVIDIA/skills --skill nemo-mbridge-perf-sequence-packing --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Sequence Packing Skill For stable background and recommendation level, see: - @docs/training/packed-sequences.md - @skills/nemo-mbridge-perf-sequence-packing/card.yaml ## Enablement Offline packed SFT for LLM finetuning: ```python from megatron.bridge.data.datasets.packed_sequence import PackedSequenceSpecs cfg.train.micro_batch_size = 1 cfg.dataset.seq_length = 4096 cfg.model.seq_length = 4096
What does the nemo-mbridge-perf-sequence-packing skill do?
Validate and use packed sequences and long-context training in Megatron-Bridge, distinguishing offline packed SFT for LLMs from in-batch packing for VLMs, and applying the right CP constraints.
How do I install it?
Run `npx skills add NVIDIA/skills --skill nemo-mbridge-perf-sequence-packing --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From NVIDIA/skills, a repository with 2,789 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.