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Get Started Free →nvidia/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.
Passed 20 of 22 test cases with the skill, and 7 of 22 without it.
and 22 more in the full report
14 fixed6 passed either way1 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 14 test cases the skill fixed. Same model, same prompt, with and without the skill.
What script and subcommand check the current active version/branch of the integrated Megatron-Core submodule within this repository?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies ./scripts/switch_mcore.sh status | Missed | Passed |
The instructions your model reads when the skill loads.
For how they differ, the arg mapping tables, gotchas, and translation script, see:
For MLM-vs-Bridge correlation questions, always name these items up front:
vanilla_gpt_pretrain_config.scripts/training/run_recipe.py.3rdparty/Megatron-LM/pretrain_gpt.py.uv run python -m torch.distributed.run.rm -rf nemo_experiments before the Bridge run.Also state that MLM needs
PYTHONPATH=3rdparty/Megatron-LM:$PYTHONPATH, matched Bridge and MLM losses
should agree within BF16 rounding, and files under 3rdparty/Megatron-LM/
should not be modified from this repo.
Use vanilla_gpt_pretrain_config for loss-correlation testing. This recipe uses
bare GPTModelProvider defaults (LayerNorm, GeLU, learned_absolute position
embeddings, vocab_size inherited from tokenizer) — matching MLM
pretrain_gpt.py defaults with no args.
PYTHONPATH=3rdparty/Megatron-LM:$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 \
test cases passed, vs 7 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.