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### Fine-tuning FastChat-T5
You can use the following command to train FastChat-T5 with 4 x A100 (40GB).
```bash
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_conversation.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](https://github.com/lm-sys/FastChat/blob/55051ad0f23fef5eeecbda14a2e3e128ffcb2a98/fastchat/utils.py#L166-L185) to update the saved model weight. Additional discussions can be found [here](https://github.com/lm-sys/FastChat/issues/643).
### 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. To use QLoRA, you must have bitsandbytes>=0.39.0 and transformers>=4.30.0 installed.
```bash
deepspeed fastchat/train/train_lora.py \
--model_name_or_path ~/model_weights/llama-7b \
--lora_r 8 \
--lora_alpha 16 \
--lora_dropout 0.05 \
--data_path ./data/dummy_conversation.json \
--bf16 True \
--output_dir ./checkpoints \
--num_train_epochs 3 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--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 \
```
For T5-XL or XXL
```bash
deepspeed fastchat/train/train_lora_t5.py \
--model_name_or_path google/flan-t5-xl \
--data_path ./data/dummy_conversation.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 \
--model_max_length 2048 \
--preprocessed_path ./preprocessed_data/processed.json \
--gradient_checkpointing True \
--q_lora True \
--deepspeed playground/deepspeed_config_s2.json
```
### Fine-tuning Vicuna-7B with Local NPUs
You can use the following command to train Vicuna-7B with 8 x 910B (60GB). Use `--nproc_per_node` to specify the number of NPUs.
```bash
torchrun --nproc_per_node=8 --master_port=20001 fastchat/train/train.py \
--model_name_or_path ~/vicuna-7b-v1.5-16k \
--data_path data/dummy_conversation.json \
--fp16 True \
--output_dir output_vicuna \
--num_train_epochs 3 \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 1 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 1200 \
--save_total_limit 10 \
--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 'LlamaDecoderLayer' \
--model_max_length 2048 \
--gradient_checkpointing True \
--lazy_preprocess True
```
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