Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/NVIDIA-NeMo/Megatron-Bridgenpx agentmods add skills/nvidia-nemo/megatron-bridge/nemo-mbridge-mlm-bridge-trainingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-mlm-bridge-training)<a href="https://agentmods.dev/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-mlm-bridge-training"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-mlm-bridge-training/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-mlm-bridge-training"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-mlm-bridge-training.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00044 | $0.01781 |
| Opus 5 | $0.00022 | $0.00890 |
| Sonnet 5 | $0.00009 | $0.00356 |
| Haiku 4.5 | $0.00004 | $0.00178 |
Grade C, and why
nemo-mbridge-mlm-bridge-training scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
5. Fresh-run cleanup: `rm -rf nemo_experiments` before the Bridge run. This is a copy
100% identical to nemo-mbridge-mlm-bridge-training — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MLM vs Bridge Training
For how they differ, the arg mapping tables, gotchas, and translation script, see:
- @docs/megatron-lm-to-megatron-bridge.md
First Answer Checklist
For MLM-vs-Bridge correlation questions, always name these items up front:
- Bridge recipe:
vanilla_gpt_pretrain_config. - Bridge entry point:
scripts/training/run_recipe.py. - MLM entry point:
3rdparty/Megatron-LM/pretrain_gpt.py. - Launch wrapper for both:
uv run python -m torch.distributed.run. - Fresh-run cleanup:
rm -rf nemo_experimentsbefore the Bridge run.
Also state that MLM needs
PYTHONPATH=3rdparty/Megatron-LM:$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.seq_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
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 179 lines · 44 tokens per session scan C 7eae976eb49a
nemo-mbridge-mlm-bridge-training is a skill published in the GitHub repository NVIDIA-NeMo/Megatron-Bridge (906 stars, last pushed today), licensed Apache-2.0. It adds 44 tokens to every session and 1,781 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). It is 100% identical to nemo-mbridge-mlm-bridge-training, differing in 0 lines, and is treated as a copy.
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