Training

This page covers the current supervised training path for graph retrievers.

What This Step Does

gfmrag.workflow.sft_training trains or evaluates a retriever on graph-index datasets that already have stage1 files and processed QA data. It can also load an existing checkpoint and emit retrieval predictions for downstream QA.

When You Need It

Use this page when you want to:

• fine-tune on your own datasets
• run retrieval evaluation from a pre-trained checkpoint
• generate predictions_<data_name>.json files for later QA

If you have not prepared the data yet, start with Data Format and Index.

Inputs

• A dataset root under datasets.cfgs.root
• One or more indexed datasets with processed/stage1/
• Training names in datasets.train_names
• Validation names in datasets.valid_names
• A training config, usually gfmrag/workflow/config/gfm_rag/sft_training.yaml

Outputs

Hydra writes runs under outputs/qa_finetune/<date>/<time>/.

Common outputs include:

• checkpoints managed by the trainer
• pretrained/ when save_pretrained=true
• predictions_<data_name>.json when trainer.args.do_predict=true

Minimal Example

Single-node fine-tuning:

Bash

python -m gfmrag.workflow.sft_training \
  datasets.cfgs.root=./data \
  datasets.train_names=[hotpotqa_train_example] \
  datasets.valid_names=[hotpotqa_test]

Multi-GPU fine-tuning:

Bash

torchrun --nproc_per_node=4 -m gfmrag.workflow.sft_training \
  datasets.cfgs.root=./data \
  datasets.train_names=[hotpotqa_train0,hotpotqa_train1] \
  datasets.valid_names=[hotpotqa_test,musique_test,2wikimultihopqa_test]

Retrieval evaluation from a pre-trained checkpoint:

Bash

torchrun --nproc_per_node=4 -m gfmrag.workflow.sft_training \
  load_model_from_pretrained=rmanluo/GFM-RAG-8M \
  datasets.cfgs.root=./data \
  datasets.train_names=[] \
  trainer.args.do_train=false \
  trainer.args.do_eval=true \
  trainer.args.do_predict=true \
  +trainer.args.eval_batch_size=1

Key Configs

• gfmrag/workflow/config/gfm_rag/sft_training.yaml
• GFM-RAG Fine-tuning Config
• Text Embedding Config
• Document Ranker Config
• Wandb Config

Common Pitfalls

• datasets.init_datasets=false requires datasets.feat_dim to be set.
• load_model_from_pretrained overwrites the model configuration with the checkpoint config.
• Prediction files are only written when trainer.args.do_predict=true.
• The downstream QA script expects both the retrieval output file and the dataset nodes.csv.

Related Legacy Workflows

The repository still contains older documentation pages discussing legacy stage-named training modules. Those are no longer the primary training path and are intentionally not used as the main tutorial flow.