| # Stanford Alpaca | |
| This is a replica of Alpaca by Stanford' tatsu | |
| Trained using the original instructions with a minor modification in FSDP mode | |
| ## Compute Used | |
| Trained on 4xA100s for 6H | |
| Donated by redmond.ai | |
| NO LORA HAS BEEN USED, this is a natively-finetuned model, hence "alpaca-native" | |
| If you are interested on more llama-based models, you can check out my profile or search for other models at https://huggingface.co/models?other=llama | |
| This (MIGHT) be a quantized version of this model, but be careful: https://boards.4channel.org/g/thread/92173062#p92182396 | |
| CONFIGURATION (default except fsdp): | |
| ```shell | |
| torchrun --nproc_per_node=4 --master_port=3045 train.py \ | |
| --model_name_or_path /workspace/llama-7b-hf \ | |
| --data_path ./alpaca_data.json \ | |
| --bf16 True \ | |
| --output_dir /workspace/output \ | |
| --num_train_epochs 3 \ | |
| --per_device_train_batch_size 4 \ | |
| --per_device_eval_batch_size 4 \ | |
| --gradient_accumulation_steps 8 \ | |
| --evaluation_strategy "no" \ | |
| --save_strategy "steps" \ | |
| --save_steps 200 \ | |
| --save_total_limit 1 \ | |
| --learning_rate 2e-5 \ | |
| --weight_decay 0. \ | |
| --warmup_ratio 0.03 \ | |
| --lr_scheduler_type "cosine" \ | |
| --logging_steps 1 \ | |
| --fsdp "shard_grad_op auto_wrap" \ | |
| --fsdp_transformer_layer_cls_to_wrap 'LLaMADecoderLayer' \ | |
| --tf32 True --report_to="wandb" | |
| ``` |