Agent skill · Data & Analytics

nemo-mbridge-mlm-bridge-training

Run Megatron-LM (MLM) and Megatron Bridge training with mock or real data. Covers correlation testing, available recipes, and multi-GPU examples.

NVIDIAgithub.com/NVIDIAGitHub ↗
claude-codecodexApache-2.0
Install
npx skills add NVIDIA/skills --skill nemo-mbridge-mlm-bridge-training --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 6
SKILL.md size: 6 KB
Bundled scripts: none
Path: skills/nemo-mbridge-mlm-bridge-training/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,789
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# MLM vs Bridge Training For how they differ, the arg mapping tables, gotchas, and translation script, see: - @docs/megatron-lm-to-megatron-bridge.md ## First Answer Checklist For MLM-vs-Bridge correlation questions, always name these items up front: 1. Bridge recipe: `vanilla_gpt_pretrain_config`. 2. Bridge entry point: `scripts/training/run_recipe.py`. 3. MLM entry point: `3rdparty/Megatron-LM/p

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About this skill
What does the nemo-mbridge-mlm-bridge-training skill do?

Run Megatron-LM (MLM) and Megatron Bridge training with mock or real data. Covers correlation testing, available recipes, and multi-GPU examples.

How do I install it?

Run `npx skills add NVIDIA/skills --skill nemo-mbridge-mlm-bridge-training --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.

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