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A newer version of the Gradio SDK is available:
4.42.0
Fine-tuning FastChat-T5
You can use the following command to train FastChat-T5 with 4 x A100 (40GB).
torchrun --nproc_per_node=4 --master_port=9778 fastchat/train/train_flant5.py \
--model_name_or_path google/flan-t5-xl \
--data_path /data/dummy.json \
--bf16 True \
--output_dir ./checkpoints_flant5_3b \
--num_train_epochs 3 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 4 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 300 \
--save_total_limit 1 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--fsdp "full_shard auto_wrap" \
--fsdp_transformer_layer_cls_to_wrap T5Block \
--tf32 True \
--model_max_length 2048 \
--preprocessed_path ./preprocessed_data/processed.json \
--gradient_checkpointing True
After training, please use our post-processing function to update the saved model weight. Additional discussions can be found here.
Fine-tuning using (Q)LoRA
You can use the following command to train Vicuna-7B using QLoRA using ZeRO2. Note that ZeRO3 is not currently supported with QLoRA but ZeRO3 does support LoRA, which has a reference configuraiton under playground/deepspeed_config_s3.json
.
deepspeed train_lora.py \
--model_name_or_path ~/model_weights/llama-7b \
--lora_r 8 \
--lora_alpha 16 \
--lora_dropout 0.05 \
--data_path <path-to-data> \
--bf16 True \
--output_dir ./checkpoints \
--num_train_epochs 3 \
--per_device_train_batch_size 4 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 1 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 1200 \
--save_total_limit 100 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--tf32 True \
--model_max_length 2048 \
--q_lora True \
--deepspeed playground/deepspeed_config_s2.json \