Update README.md

README.md

@@ -45,7 +45,7 @@ generate_ids = model.generate(**inputs, max_new_tokens=256, streamer=streamer)
 You could also alternatively launch a CLI demo by using the script in https://github.com/hiyouga/LLaMA-Efficient-Tuning
 
 ```bash
-python src/cli_demo.py --model_name_or_path hiyouga/baichuan-13b-sft
+python src/cli_demo.py --template default --model_name_or_path hiyouga/baichuan-13b-sft
 ```
 
 ---
@@ -54,6 +54,38 @@ You can reproduce our results by visiting the following step-by-step (Chinese) g
 
 https://zhuanlan.zhihu.com/p/645010851
 
+or using the following scripts in [LLaMA-Efficient-Tuning](https://github.com/hiyouga/LLaMA-Efficient-Tuning):
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+    --stage sft \
+    --model_name_or_path baichuan-inc/Baichuan-13B-Base \
+    --do_train \
+    --dataset alpaca_gpt4_en,alpaca_gpt4_zh,self_cognition,oaast_sft,sharegpt_zh,refgpt_zh_p1,lima,self_cognition \
+    --template default \
+    --finetuning_type lora \
+    --lora_rank 32 \
+    --lora_target W_pack,o_proj,down_proj,up_proj,gate_proj \
+    --output_dir baichuan_13b_lora \
+    --per_device_train_batch_size 4 \
+    --gradient_accumulation_steps 8 \
+    --preprocessing_num_workers 16 \
+    --max_source_length 1024 \
+    --max_target_length 512 \
+    --optim paged_adamw_32bit \
+    --lr_scheduler_type cosine \
+    --logging_steps 10 \
+    --save_steps 100 \
+    --eval_steps 100 \
+    --warmup_steps 100 \
+    --learning_rate 3e-5 \
+    --max_grad_norm 0.5 \
+    --num_train_epochs 2.0 \
+    --quantization_bit 4 \
+    --plot_loss \
+    --fp16
+```
+
 Loss curve:
 
 ![loss](loss.svg)