diff --git a/commands/run_sft.sh b/commands/run_sft.sh
index ab67222618..a418bb058c 100644
--- a/commands/run_sft.sh
+++ b/commands/run_sft.sh
@@ -41,7 +41,7 @@ accelerate launch $EXTRA_ACCELERATE_ARGS \
     --dataset_name $DATASET_NAME \
     --output_dir $OUTPUT_DIR \
     --max_steps $MAX_STEPS \
-    --batch_size $BATCH_SIZE \
+    --per_device_train_batch_size $BATCH_SIZE \
     --seq_length $SEQ_LEN \
     $EXTRA_TRAINING_ARGS
 """
diff --git a/docs/source/lora_tuning_peft.mdx b/docs/source/lora_tuning_peft.mdx
index 4b4345bc5f..582f1ac9d8 100644
--- a/docs/source/lora_tuning_peft.mdx
+++ b/docs/source/lora_tuning_peft.mdx
@@ -71,7 +71,7 @@ The `trl` library is powered by `accelerate`. As such it is best to configure an
 
 ```bash
 accelerate config # will prompt you to define the training configuration
-accelerate launch scripts/gpt2-sentiment_peft.py # launches training
+accelerate launch examples/scripts/ppo.py --use_peft # launch`es training
 ```
 
 ## Using `trl` + `peft` and Data Parallelism
@@ -140,5 +140,5 @@ python PATH_TO_SCRIPT
 You can easily fine-tune Llama2 model using `SFTTrainer` and the official script! For example to fine-tune llama2-7b on the Guanaco dataset, run (tested on a single NVIDIA T4-16GB):
 
 ```bash
-python examples/scripts/sft.py --model_name meta-llama/Llama-2-7b-hf --dataset_name timdettmers/openassistant-guanaco --load_in_4bit --use_peft --batch_size 4 --gradient_accumulation_steps 2
+python examples/scripts/sft.py --output_dir sft_openassistant-guanaco  --model_name meta-llama/Llama-2-7b-hf --dataset_name timdettmers/openassistant-guanaco --load_in_4bit --use_peft --per_device_train_batch_size 4 --gradient_accumulation_steps 2
 ```
diff --git a/examples/scripts/sft.py b/examples/scripts/sft.py
index d60af083e1..5072920cb4 100644
--- a/examples/scripts/sft.py
+++ b/examples/scripts/sft.py
@@ -88,6 +88,7 @@ class ScriptArguments:
         quantization_config=quantization_config,
     )
     tokenizer = AutoTokenizer.from_pretrained(model_config.model_name_or_path, use_fast=True)
+    tokenizer.pad_token = tokenizer.eos_token
 
     ################
     # Dataset