Artifact Graph

For definitions of artifact, step, and how the CLI’s --produces and --consumes filters let you walk the artifact graph, see Nemotron Steps Basics. This page focuses on the artifact types and common chains specific to model training.

Common Training Paths

The supervised fine-tuning paths in the Nemotron pipeline follow one of the following two chains.

• The Hugging Face line used by sft/automodel: training_jsonl → sft/automodel → checkpoint_hf.

• The packed Megatron line used by sft/megatron_bridge: training_jsonl → packing prep → packed_parquet → sft/megatron_bridge → checkpoint_megatron.

A typical alignment path starts from a checkpoint_megatron policy, adds preference or reward-side data, runs one of the rl/nemo_rl/... steps, and produces a new checkpoint_megatron.

A typical compression path starts from checkpoint_hf, runs optimize/modelopt/quantize, and produces checkpoint_megatron. Add a conversion step after quantization when the next consumer needs a Hugging Face layout again.

Tokenizer and Chat Template Consistency

Matching artifact types is not enough for correctness. The tokenizer, the chat template, and the maximum sequence length must stay consistent across every step that tokenizes text or loads weights for the same model line. A mismatch often appears as a plausible training loss curve with poor downstream quality.

Related Reading

• Nemotron Steps Basics defines the concepts of step, configuration, environment profile, and artifact.

• Training Basics defines the training-specific terms, including tokenizers and chat templates.

• Data and Checkpoint Formats describes the on-disk layouts of each artifact type.

• Training Libraries describes the ecosystem of libraries that back the steps.