Choose a PEFT Backend#

Parameter-efficient fine tuning (PEFT) in Nemotron is implemented as dedicated steps that emit adapter checkpoints. Pick the backend that matches your base checkpoint format and your data path.

Options#

Step id

Best when

Primary inputs

Primary output artifact

peft/automodel

You have a Hugging Face base, chat-formatted JSON Lines (JSONL), and a small GPU count

training_jsonl

checkpoint_lora

peft/megatron_bridge

You have a Megatron base checkpoint and packed Apache Parquet at scale

packed_parquet, checkpoint_megatron

checkpoint_lora

Decision Flow#

  1. If you have one to four graphics processing units (GPUs) and JSON Lines (JSONL) chat data, use peft/automodel.

  2. If you have eight or more GPUs, you already run Megatron packing, and you train adapters on a Megatron base, use peft/megatron_bridge.

  3. If deployment requires a merged Hugging Face model, plan convert/merge_lora after training. Add any Megatron to Hugging Face conversion step that your pipeline needs before merge. Adapter evaluation scores are not identical to merged model scores.

Sample Commands#

$ uv run nemotron steps run peft/automodel -c tiny
$ uv run nemotron steps run peft/megatron_bridge -c tiny

The Megatron Bridge path needs compatible packed Parquet and a base checkpoint_megatron path that you set in training configuration.

Success Criteria#

  • You version adapter artifacts with base model id, data blend, rank, alpha, and target module set so you can reproduce runs.

  • You re-evaluate after merge when production uses merged weights.