Nemo Mbridge Mlm Bridge Training

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/pretrain_gpt.py. 4. Launch wrapper for both: uv run python -m torch.distributed.run. 5. Fresh-run cleanup: rm -rf nemo_experiments before the Bridge run.

Also state that MLM needs PYTHONPATH=3rdparty/Megatron-LM:$PYTHONPATH, matched Bridge and MLM losses should agree within BF16 rounding, and files under 3rdparty/Megatron-LM/ should not be modified from this repo.

Correlation Testing

Use vanilla_gpt_pretrain_config for loss-correlation testing. This recipe uses bare GPTModelProvider defaults (LayerNorm, GeLU, learned_absolute position embeddings, vocab_size inherited from tokenizer) — matching MLM pretrain_gpt.py defaults with no args.

MLM Correlation Run (2L/256H, 1 GPU)

PYTHONPATH=3rdparty/Megatron-LM:$PYTHONPATH \
uv run python -m torch.distributed.run --nproc_per_node=1 \
  3rdparty/Megatron-LM/pretrain_gpt.py \
  --num-layers 2 --hidden-size 256 --num-attention-heads 4 \
  --ffn-hidden-size 1024 --seq-length 512 --max-position-embeddings 512 \
  --micro-batch-size 4 --global-batch-size 32 \
  --train-iters 10 --eval-iters 2 --eval-interval 10 \
  --mock-data --bf16 --use-mcore-models \
  --tokenizer-type NullTokenizer --vocab-size 32000 \
  --lr 3e-4 --min-lr 3e-5 --seed 1234 --log-interval 1

Bridge Correlation Run (same config, 1 GPU)

rm -rf nemo_experiments && \
uv run python -m torch.distributed.run --nproc_per_node=1 \
  scripts/training/run_recipe.py \
  --recipe vanilla_gpt_pretrain_config \
  model.num_layers=2 model.hidden_size=256 \
  model.num_attention_heads=4 model.ffn_hidden_size=1024 \
  model.seq_length=512 dataset.sequence_length=512 \
  train.train_iters=10 train.global_batch_size=32 train.micro_batch_size=4 \
  validation.eval_interval=10 validation.eval_iters=2 \
  optimizer.lr=3e-4 optimizer.min_lr=3e-5 \
  scheduler.lr_warmup_iters=1 scheduler.lr_decay_iters=10 \
  rng.seed=1234 logger.log_interval=1

Verification

With matched parameters the LM losses should be nearly identical at each iteration. Compare lm loss values from both logs — they should agree to within BF16 rounding.

Multi-GPU Examples

MLM 2-GPU with TP=2

PYTHONPATH=3rdparty/Megatron-LM:$PYTHONPATH \
uv run python -m torch.distributed.run --nproc_per_node=2 \
  3rdparty/Megatron-LM/pretrain_gpt.py \
  --tensor-model-parallel-size 2 --sequence-parallel \
  --num-layers 4 --hidden-size 256 --num-attention-heads 4 \
  --seq-length 1024 --max-position-embeddings 1024 \
  --micro-batch-size 2 --global-batch-size 16 \
  --train-iters 10 --eval-iters 2 --eval-interval 10 \
  --mock-data --bf16 --use-mcore-models \
  --tokenizer-type NullTokenizer --vocab-size 1024 \
  --lr 1e-4 --log-interval 1

Bridge 2-GPU with TP=2

rm -rf nemo_experiments && \
uv run python -m torch.distributed.run --nproc_per_node=2 \
  scripts/training/run_recipe.py \
  --recipe vanilla_gpt_pretrain_config \
  model.tensor_model_parallel_size=2 model.sequence_parallel=true \
  model.num_layers=4 model.hidden_size=256 \
  model.num_attention_heads=4 model.ffn_hidden_size=1024 \
  model.seq_length=1024 dataset.sequence_length=1024 \
  train.train_iters=10 train.global_batch_size=16 train.micro_batch_size=2 \
  validation.eval_interval=10 validation.eval_iters=2 \
  scheduler.lr_warmup_iters=2 scheduler.lr_decay_iters=10 \
  logger.log_interval=1

Available Recipes

Common recipes (use with --recipe):

• vanilla_gpt_pretrain_config — Minimal GPT (bare GPTModelProvider defaults,

ideal for correlation testing and custom configs)

• llama32_1b_pretrain_config — Llama 3.2 1B (16L, 2048H, GBS=512, seq=8192)
• llama3_8b_pretrain_config — Llama 3 8B
• qwen3_8b_pretrain_config — Qwen3 8B
• deepseek_v2_lite_pretrain_config — DeepSeek-V2-Lite 16B MoE

SFT/PEFT variants use _sft_config / _peft_config suffix.

Megatron-Core Submodule

For what the submodule is and why two versions exist, see @docs/megatron-lm-to-megatron-bridge.md.

Check current version

./scripts/switch_mcore.sh status

Switch to dev for testing newer MCore features

./scripts/switch_mcore.sh dev

# uv sync (without --locked) since lockfile is for main
uv sync

Switch back to main

./scripts/switch_mcore.sh main

After pulling latest main

When you pull the latest Bridge main branch, the submodule pointer may have been updated. Re-sync the submodule:

git submodule update --init 3rdparty/Megatron-LM

Pitfalls

1. Always `rm -rf nemo_experiments` before a fresh correlation run. Bridge auto-resumes from stale checkpoints silently.

2. `uv run` required: Always use uv run python -m torch.distributed.run (not bare torchrun or python).

3. MLM PYTHONPATH: Must include 3rdparty/Megatron-LM so gpt_builders.py is importable.

4. Scheduler overrides: When overriding train.train_iters to a small value, also set scheduler.lr_warmup_iters and scheduler.lr_decay_iters or you get an assertion error.

5. Use `dataset.sequence_length` in CLI overrides, not dataset.seq_length.

6. MoE OOM: Large MoE models require full activation recomputation and typically multi-node EP. TP does NOT reduce per-GPU expert memory.

7. `uv sync --locked` fails after switching to dev: The lockfile is generated against the main MCore commit. Use uv sync (without --locked) when on dev.

Evaluation Report

Evaluation of the nemo-mbridge-mlm-bridge-training skill before publication through NVSkills-Eval.

This benchmark summarizes 3-Tier Evaluation from NVSkills-Eval results for the skill. The goal is to document whether the skill is safe, discoverable, effective, and useful for agents before it is published for broader workflow use.

Evaluation Summary

• Skill: nemo-mbridge-mlm-bridge-training
• Evaluation date: 2026-06-02
• NVSkills-Eval profile: external
• Environment: local
• Dataset: 1 evaluation tasks
• Attempts per task: 2
• Pass threshold: 50%
• Overall verdict: PASS

Agents Used

• claude-code
• codex

Metrics Used

Reported benchmark dimensions:

• Security: checks whether skill-assisted execution avoids unsafe behavior such as secret leakage, destructive commands, or unauthorized access.
• Correctness: checks whether the agent follows the expected workflow and produces the correct final output.
• Discoverability: checks whether the agent loads the skill when relevant and avoids using it when irrelevant.
• Effectiveness: checks whether the agent performs measurably better with the skill than without it.
• Efficiency: checks whether the agent uses fewer tokens and avoids redundant work.

Underlying evaluation signals used in this run:

• security (Security): checks for unsafe operations, secret leakage, and unauthorized access.
• skill_execution (Skill Execution): verifies that the agent loaded the expected skill and workflow.
• skill_efficiency (Efficiency): checks routing quality, decoy avoidance, and redundant tool usage.
• accuracy (Accuracy): grades final-answer correctness against the reference answer.
• goal_accuracy (Goal Accuracy): checks whether the overall user task completed successfully.
• behavior_check (Behavior Check): verifies expected behavior steps, including safety expectations.
• token_efficiency (Token Efficiency): compares token usage with and without the skill.

Test Tasks

The benchmark dataset contained 1 evaluation tasks:

• Positive tasks: 1 tasks where the skill was expected to activate.
• Negative tasks: 0 tasks where no skill was expected.
• Unlabeled tasks: 0 tasks where positive/negative intent could not be inferred.

Task composition is derived from the evaluation dataset when possible. Entries with expected_skill set are treated as positive skill-activation cases, while entries with expected_skill: null are treated as negative activation cases.

Results

Score values show skill-assisted performance. Values in parentheses show uplift versus the no-skill baseline when baseline data is available.

Tier 1: Static Validation Summary

Tier 1 validation passed with observations. NVSkills-Eval ran 9 checks and found 12 total findings.

Top findings:

• MEDIUM QUALITY/quality_correctness: SKILL_SPEC recommended field missing: 'metadata.author' (skills/nemo-mbridge-mlm-bridge-training/SKILL.md)
• MEDIUM QUALITY/quality_correctness: SKILL_SPEC recommended field missing: 'metadata.tags' (skills/nemo-mbridge-mlm-bridge-training/SKILL.md)
• MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (skills/nemo-mbridge-mlm-bridge-training/SKILL.md)
• MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (skills/nemo-mbridge-mlm-bridge-training/SKILL.md)
• MEDIUM SCHEMA/author_missing: Author not specified in metadata (skills/nemo-mbridge-mlm-bridge-training/SKILL.md)

Tier 2: Deduplication Summary

Tier 2 validation passed. NVSkills-Eval ran 2 checks and found 0 total findings.

Notable observations:

• Context Deduplication: Collected 1 file(s)
• Inter-Skill Deduplication: Parsed skill 'nemo-mbridge-mlm-bridge-training': 145 char description

Publication Recommendation

The skill is suitable to proceed toward NVSkills-Eval publication based on this benchmark. Skill owners should keep this file with the skill and refresh it when the evaluation dataset, skill behavior, or target agents materially change.

Description: <br>

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

This skill is ready for commercial/non-commercial use. <br>

Owner

NVIDIA <br>

License/Terms of Use: <br>

Apache 2.0 <br>

Use Case: <br>

Developers and engineers running Megatron-LM or Megatron Bridge training, comparing MLM vs Bridge loss curves, translating MLM CLI args to Bridge config, or debugging correlation divergences. <br>

Deployment Geography for Use: <br>

Global <br>

Known Risks and Mitigations: <br>

Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills. <br> Mitigation: Review and scan skill before deployment. <br>

Reference(s): <br>

• Megatron-LM to Megatron Bridge Guide <br>
• Megatron Bridge Documentation <br>

Skill Output: <br>

Output Type(s): [Shell commands, Configuration instructions, Analysis] <br> Output Format: [Markdown with inline bash code blocks] <br> Output Parameters: [1D] <br> Other Properties Related to Output: [None] <br>

Evaluation Agents Used: <br>

• Claude Code (claude-code) <br>
• Codex (codex) <br>

Evaluation Tasks: <br>

Evaluated against 1 evaluation task (positive skill-activation) with 2 attempts per task via NVSkills-Eval external profile. <br>

Evaluation Metrics Used: <br>

Reported benchmark dimensions: <br>

• Security: Checks whether skill-assisted execution avoids unsafe behavior such as secret leakage, destructive commands, or unauthorized access. <br>
• Correctness: Checks whether the agent follows the expected workflow and produces the correct final output. <br>
• Discoverability: Checks whether the agent loads the skill when relevant and avoids using it when irrelevant. <br>
• Effectiveness: Checks whether the agent performs measurably better with the skill than without it. <br>
• Efficiency: Checks whether the agent uses fewer tokens and avoids redundant work. <br>

Underlying evaluation signals used in this run: <br>

• security: Checks for unsafe operations, secret leakage, and unauthorized access. <br>
• skill_execution: Verifies that the agent loaded the expected skill and workflow. <br>
• skill_efficiency: Checks routing quality, decoy avoidance, and redundant tool usage. <br>
• accuracy: Grades final-answer correctness against the reference answer. <br>
• goal_accuracy: Checks whether the overall user task completed successfully. <br>
• behavior_check: Verifies expected behavior steps, including safety expectations. <br>
• token_efficiency: Compares token usage with and without the skill. <br>

Evaluation Results: <br>

Skill Version(s): <br>

b0f64d72 (source: git SHA, committed 2026-06-02) <br>

Ethical Considerations: <br>

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse. <br>

(For Release on NVIDIA Platforms Only) <br> Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here. <br>