diff --git a/scripts/vllm_infer.py b/scripts/vllm_infer.py
new file mode 100644
index 00000000..0d498959
--- /dev/null
+++ b/scripts/vllm_infer.py
@@ -0,0 +1,139 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import json
+import os
+import sys
+from typing import List
+
+import fire
+from vllm import LLM, SamplingParams
+from vllm.lora.request import LoRARequest
+
+from llamafactory.data import get_dataset, get_template_and_fix_tokenizer
+from llamafactory.extras.constants import IGNORE_INDEX
+from llamafactory.hparams import get_train_args
+from llamafactory.model import load_tokenizer
+
+
+max_tokens = 2048
+
+
+def vllm_infer(
+    model_name_or_path: str = None,
+    adapter_name_or_path: str = None,
+    dataset_dir: str = "data",
+    eval_dataset: str = None,
+    template: str = "default",
+    max_sample: int = None,
+    preprocessing_num_workers: int = 16,
+    predict_with_generate: bool = True,
+    do_predict: bool = True,
+    temperature: float = 0.7,
+    top_p: float = 0.7,
+    top_k: float = 50,
+    output_dir: str = "output",
+):
+
+    if len(sys.argv) == 1:
+        model_args, data_args, training_args, finetuning_args, generating_args = (
+            get_train_args(
+                dict(
+                    model_name_or_path=model_name_or_path,
+                    adapter_name_or_path=adapter_name_or_path,
+                    dataset_dir=dataset_dir,
+                    eval_dataset=eval_dataset,
+                    template=template,
+                    max_sample=max_sample,
+                    preprocessing_num_workers=preprocessing_num_workers,
+                    predict_with_generate=predict_with_generate,
+                    do_predict=do_predict,
+                    temperature=temperature,
+                    top_p=top_p,
+                    top_k=top_k,
+                    output_dir=output_dir,
+                )
+            )
+        )
+    else:
+        model_args, data_args, training_args, finetuning_args, generating_args = (
+            get_train_args()
+        )
+
+    tokenizer = load_tokenizer(model_args)["tokenizer"]
+    template = get_template_and_fix_tokenizer(tokenizer, data_args)
+
+    eval_dataset = get_dataset(
+        template, model_args, data_args, training_args, finetuning_args.stage, tokenizer
+    )["eval_dataset"]
+
+    prompts = [item["input_ids"] for item in eval_dataset]
+    prompts = tokenizer.batch_decode(prompts, skip_special_tokens=False)
+
+    labels = [
+        list(filter(lambda x: x != IGNORE_INDEX, item["labels"]))
+        for item in eval_dataset
+    ]
+    labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
+
+    sampling_params = SamplingParams(
+        temperature=generating_args.temperature,
+        top_k=generating_args.top_k,
+        top_p=generating_args.top_p,
+        max_tokens=max_tokens,
+    )
+
+    if model_args.adapter_name_or_path:
+        if isinstance(model_args.adapter_name_or_path, list):
+            lora_path = model_args.adapter_name_or_path[0]
+        else:
+            lora_path = model_args.adapter_name_or_path
+
+        lora_requests = LoRARequest("lora_adapter_0", 0, lora_path=lora_path)
+        enable_lora = True
+    else:
+        lora_requests = None
+        enable_lora = False
+
+    llm = LLM(
+        model=model_args.model_name_or_path,
+        trust_remote_code=True,
+        tokenizer=model_args.model_name_or_path,
+        enable_lora=enable_lora,
+    )
+
+    outputs = llm.generate(prompts, sampling_params, lora_request=lora_requests)
+
+    if not os.path.exists(training_args.output_dir):
+        os.makedirs(training_args.output_dir, exist_ok=True)
+
+    output_prediction_file = os.path.join(
+        training_args.output_dir, "generated_predictions.jsonl"
+    )
+
+    with open(output_prediction_file, "w", encoding="utf-8") as writer:
+        res: List[str] = []
+        for text, pred, label in zip(prompts, outputs, labels):
+            res.append(
+                json.dumps(
+                    {"prompt": text, "predict": pred.outputs[0].text, "label": label},
+                    ensure_ascii=False,
+                )
+            )
+        writer.write("\n".join(res))
+
+
+if __name__ == "__main__":
+    fire.Fire(vllm_infer)