tiny fix

examples/extras/fsdp_qlora/single_node.sh

@@ -1,10 +1,6 @@
 #!/bin/bash
 # DO NOT use GPTQ/AWQ model in FSDP+QLoRA
 
-pip install "transformers>=4.39.1"
-pip install "accelerate>=0.28.0"
-pip install "bitsandbytes>=0.43.0"
-
 CUDA_VISIBLE_DEVICES=0,1 accelerate launch \
     --config_file examples/accelerate/fsdp_config.yaml \
     src/train.py examples/extras/fsdp_qlora/llama3_lora_sft.yaml

scripts/llama_pro.py

@@ -104,10 +104,10 @@ def block_expansion(
         print("Model weights saved in {}".format(output_dir))
 
     print("Fine-tune this model with:")
-    print("  --model_name_or_path {} \\".format(output_dir))
-    print("  --finetuning_type freeze \\")
-    print("  --freeze_trainable_layers {} \\".format(num_expand))
-    print("  --use_llama_pro")
+    print("model_name_or_path: {}".format(output_dir))
+    print("finetuning_type: freeze")
+    print("freeze_trainable_layers: {}".format(num_expand))
+    print("use_llama_pro: true")
 
 
 if __name__ == "__main__":

src/llamafactory/data/loader.py

@@ -179,7 +179,7 @@ def get_dataset(
             if training_args.should_save:
                 dataset.save_to_disk(data_args.tokenized_path)
                 logger.info("Tokenized dataset saved at {}.".format(data_args.tokenized_path))
-                logger.info("Please restart the training with `--tokenized_path {}`.".format(data_args.tokenized_path))
+                logger.info("Please restart the training with `tokenized_path: {}`.".format(data_args.tokenized_path))
 
             sys.exit(0)