68 lines
2.0 KiB
Bash
Executable File
68 lines
2.0 KiB
Bash
Executable File
# Sample script to finetune RAG using Ray for distributed retrieval.
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# Add parent directory to python path to access lightning_base.py
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export PYTHONPATH="../":"${PYTHONPATH}"
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#creates the custom knowlegebase
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python use_own_knowledge_dataset.py \
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--csv_path /DIR/SQUAD-KB/squad-kb.csv \
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--output_dir /DIR/SQUAD-KB
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# Start a single-node Ray cluster.
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ray start --head
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# A sample finetuning run, you need to specify data_dir, output_dir and model_name_or_path
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# run ./examples/rag/finetune_rag_ray.sh --help to see all the possible options
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python finetune_rag.py \
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--data_dir /DIR/squad-training-data \
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--output_dir /DIR/model_checkpoints \
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--model_name_or_path facebook/rag-token-base \
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--model_type rag_token \
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--fp16 \
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--gpus 2 \
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--profile \
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--do_train \
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--end2end \
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--do_predict \
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--n_val -1 \
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--train_batch_size 4 \
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--eval_batch_size 1 \
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--max_source_length 128 \
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--max_target_length 25 \
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--val_max_target_length 25 \
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--test_max_target_length 25 \
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--label_smoothing 0.1 \
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--dropout 0.1 \
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--attention_dropout 0.1 \
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--weight_decay 0.001 \
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--adam_epsilon 1e-08 \
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--max_grad_norm 0.1 \
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--lr_scheduler polynomial \
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--learning_rate 3e-05 \
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--num_train_epochs 10 \
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--warmup_steps 500 \
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--gradient_accumulation_steps 8 \
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--distributed_retriever ray \
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--num_retrieval_workers 4 \
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--passages_path /DIR/SQUAD-KB/my_knowledge_dataset \
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--index_path /DIR/SQUAD-KB/my_knowledge_dataset_hnsw_index.faiss \
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--index_name custom \
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--context_encoder_name facebook/dpr-ctx_encoder-multiset-base \
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--csv_path /DIR/SQUAD-KB/squad-kb.csv \
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--index_gpus 1 \
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--gpu_order [5,6,7,8,9,0,1,2,3,4] \
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--shard_dir ./test_dir/kb-shards \
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--indexing_freq 500
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# Stop the Ray cluster.
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ray stop
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#this script was used to test the SQuAD data.
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#change the dir paramater acording to your prefernece.
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#please use the same device ordere when running CUDA_VISIBLE_DEVICES=5,6,7,8,9,0,1,2,3,4 sh finetune_rag_ray_end2end.sh |