Update README.md

README.md

@@ -19,7 +19,7 @@ inference: false
 A bilingual instruction-tuned LoRA model of https://huggingface.co/baichuan-inc/Baichuan-13B-Base
 
 - Instruction-following datasets used: alpaca-en, alpaca-zh, sharegpt, open assistant, lima, refgpt
-- Training framework: https://github.com/hiyouga/LLaMA-Efficient-Tuning
+- Training framework: https://github.com/hiyouga/LLaMA-Factory
 
 Usage:
 
@@ -42,7 +42,7 @@ inputs = inputs.to("cuda")
 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
+You could also alternatively launch a CLI demo by using the script in https://github.com/hiyouga/LLaMA-Factory
 
 ```bash
 python src/cli_demo.py --template default --model_name_or_path hiyouga/baichuan-13b-sft
@@ -54,7 +54,7 @@ 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):
+or using the following scripts in [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory):
 
 ```bash
 CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
@@ -65,13 +65,12 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --template default \
     --finetuning_type lora \
     --lora_rank 32 \
-    --lora_target W_pack,o_proj,down_proj,up_proj,gate_proj \
+    --lora_target all \
     --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 \
+    --cutoff_len 1024 \
     --optim paged_adamw_32bit \
     --lr_scheduler_type cosine \
     --logging_steps 10 \