diff --git a/README_en.md b/README_en.md
index 21438d7..4ceb8d5 100644
--- a/README_en.md
+++ b/README_en.md
@@ -635,7 +635,7 @@ This project is developed by the following institutions:
 
 👏 Welcome to explore other multimodal projects of our team:
 
-[VisCPM](https://github.com/OpenBMB/VisCPM/tree/main) | [RLHF-V](https://github.com/RLHF-V/RLHF-V) | [LLaVA-UHD](https://github.com/thunlp/LLaVA-UHD)
+[VisCPM](https://github.com/OpenBMB/VisCPM/tree/main) | [RLHF-V](https://github.com/RLHF-V/RLHF-V) | [LLaVA-UHD](https://github.com/thunlp/LLaVA-UHD) | [RLAIF-V](https://github.com/RLHF-V/RLAIF-V)
 
 ## Citation
 
diff --git a/finetune/finetune.py b/finetune/finetune.py
index 808700a..708be3d 100644
--- a/finetune/finetune.py
+++ b/finetune/finetune.py
@@ -46,6 +46,7 @@ class TrainingArguments(transformers.TrainingArguments):
     )
     tune_vision: Optional[bool] = field(default=True)
     tune_llm: Optional[bool] = field(default=True)
+    llm_type: str = field(default="minicpm")
 
 
 def rank0_print(*args):
@@ -166,10 +167,11 @@ def train():
         model.llm.requires_grad_(False)
     rank0_print(get_parameter_number(model))
 
-    llm_type = "minicpm"
-    if "llama3" in model.name_or_path.lower():
+    llm_type = training_args.llm_type    
+    if llm_type == "llama3":
         tokenizer.chat_template = "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}"
-        llm_type = "llama3"
+    
+    rank0_print(f'llm_type={llm_type}')
 
     # Load data
     if hasattr(model.config, "slice_config"):
diff --git a/finetune/finetune_ds.sh b/finetune/finetune_ds.sh
index 32f7f80..a904ce2 100644
--- a/finetune/finetune_ds.sh
+++ b/finetune/finetune_ds.sh
@@ -6,11 +6,12 @@ NODE_RANK=0
 MASTER_ADDR=localhost
 MASTER_PORT=6001
 
-MODEL="path/to/minicpmv2" 
+MODEL="openbmb/MiniCPM-Llama3-V-2_5" # or openbmb/MiniCPM-V-2
 # ATTENTION: specify the path to your training data, which should be a json file consisting of a list of conversations.
 # See the section for finetuning in README for more information.
 DATA="path/to/trainging_data"
 EVAL_DATA="path/to/test_data"
+LLM_TYPE="llama3" # if use openbmb/MiniCPM-V-2, please set LLM_TYPE=minicpm
 
 DISTRIBUTED_ARGS="
     --nproc_per_node $GPUS_PER_NODE \
@@ -21,6 +22,7 @@ DISTRIBUTED_ARGS="
 "
 torchrun $DISTRIBUTED_ARGS finetune.py \
     --model_name_or_path $MODEL \
+    --llm_type $LLM_TYPE \
     --data_path $DATA \
     --eval_data_path $EVAL_DATA \
     --remove_unused_columns false \
diff --git a/finetune/readme.md b/finetune/readme.md
index 1dd1414..8eb1de7 100644
--- a/finetune/readme.md
+++ b/finetune/readme.md
@@ -52,12 +52,13 @@ For the vision-language example with image, you are required to provide **\<imag
 
 ### Full-parameter finetuning
 
-Full-parameter parameter finetuning requires updating all parameters of LLM in the whole training process. Please specify the correct MODEL path and DATA path in the shell scripts.
+Full-parameter parameter finetuning requires updating all parameters of LLM in the whole training process. Please specify the correct MODEL path, DATA path and LLM_TYPE in the shell scripts.
 
 ```shell
 MODEL="openbmb/MiniCPM-Llama3-V-2_5" # or openbmb/MiniCPM-V-2
 DATA="path/to/trainging_data" # json file
 EVAL_DATA="path/to/test_data" # json file
+LLM_TYPE="llama3" # if use openbmb/MiniCPM-V-2, please set LLM_TYPE=minicpm
 ```
 
 To launch your training, run the following script: