Supervised Fine-Tuning (SFT)

We provide example SFT experiments using various datasets including SQuAD, OpenAI format datasets (with tool calling support), and custom JSONL datasets. For detailed documentation on supported datasets and configurations, see the SFT documentation.

SFT Single Node

The default SFT configuration is set to run on a single GPU. To start the experiment:

uv run python examples/run_sft.py

This fine-tunes the Llama3.2-1B model on the SQuAD dataset using a 1 GPU.

To use multiple GPUs on a single node, you can modify the cluster configuration. This adjustment will also let you potentially increase the model and batch size:

uv run python examples/run_sft.py \
  policy.model_name="meta-llama/Meta-Llama-3-8B" \
  policy.train_global_batch_size=128 \
  sft.val_global_batch_size=128 \
  cluster.gpus_per_node=8

Refer to examples/configs/sft.yaml for a full list of parameters that can be overridden.

SFT Multi-node

# Run from the root of NeMo RL repo
NUM_ACTOR_NODES=2

COMMAND="uv run ./examples/run_sft.py --config examples/configs/sft.yaml cluster.num_nodes=2 cluster.gpus_per_node=8 checkpointing.checkpoint_dir='results/sft_llama8b_2nodes' logger.wandb_enabled=True logger.wandb.name='sft-llama8b'" \
CONTAINER=YOUR_CONTAINER \
MOUNTS="$PWD:$PWD" \
sbatch \
    --nodes=${NUM_ACTOR_NODES} \
    --account=YOUR_ACCOUNT \
    --job-name=YOUR_JOBNAME \
    --partition=YOUR_PARTITION \
    --time=4:0:0 \
    --gres=gpu:8 \
    ray.sub