Checkpointing with Hugging Face Models

NeMo RL provides two checkpoint formats for Hugging Face models: Torch distributed and Hugging Face format. Torch distributed is used by default for efficiency, and Hugging Face format is provided for compatibility with Hugging Face’s AutoModel.from_pretrained API. Note that Hugging Face format checkpoints save only the model weights, ignoring the optimizer states. It is recommended to use Torch distributed format to save intermediate checkpoints and to save a Hugging Face checkpoint only at the end of training.

A checkpoint converter is provided to convert a Torch distributed checkpoint checkpoint to Hugging Face format after training:

uv run examples/convert_dcp_to_hf.py --config=<YAML CONFIG USED DURING TRAINING> <ANY CONFIG OVERRIDES USED DURING TRAINING> --dcp-ckpt-path=<PATH TO DIST CHECKPOINT TO CONVERT> --hf-ckpt-path=<WHERE TO SAVE HF CHECKPOINT>

Usually Hugging Face checkpoints keep the weights and tokenizer together (which we also recommend for provenance). You can copy it afterwards. Here’s an end-to-end example:

# Change to your appropriate checkpoint directory
CKPT_DIR=results/sft/step_10

uv run examples/convert_dcp_to_hf.py --config=$CKPT_DIR/config.yaml --dcp-ckpt-path=$CKPT_DIR/policy/weights --hf-ckpt-path=${CKPT_DIR}-hf
rsync -ahP $CKPT_DIR/policy/tokenizer ${CKPT_DIR}-hf/