diff --git a/docs/en/advanced_guides/multimodal_eval.md b/docs/en/advanced_guides/multimodal_eval.md
new file mode 100644
index 00000000..967d1537
--- /dev/null
+++ b/docs/en/advanced_guides/multimodal_eval.md
@@ -0,0 +1,108 @@
+# Multi-modality Evaluation
+
+We support several multi-modality datasets, such as [MMBench](https://opencompass.org.cn/MMBench), [SEED-Bench](https://github.com/AILab-CVC/SEED-Bench) to evaluate multi-modality models. Before starting, please make sure you have downloaded the evaluation datasets following the official instruction.
+
+## Start Evaluation
+
+Before evaluation, you could modify `tasks.py` or create a new file like `tasks.py` to evaluate your own model.
+
+Generally to run the evaluation, we use command below.
+
+### Slurm
+
+```sh
+cd $root
+python run.py configs/multimodal/tasks.py --mm-eval --slurm -p $PARTITION
+```
+
+### PyTorch
+
+```sh
+cd $root
+python run.py configs/multimodal/tasks.py --mm-eval
+```
+
+## Configuration File
+
+We adapt the new config format of [MMEngine](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta).
+
+### Task File
+
+Here is the example config of `configs/multimodal/tasks.py`.
+
+```python
+from mmengine.config import read_base
+
+with read_base():
+    from .minigpt_4.minigpt_4_7b_mmbench import (minigpt_4_mmbench_dataloader,
+                                                 minigpt_4_mmbench_evaluator,
+                                                 minigpt_4_mmbench_load_from,
+                                                 minigpt_4_mmbench_model)
+
+models = [minigpt_4_mmbench_model]
+datasets = [minigpt_4_mmbench_dataloader]
+evaluators = [minigpt_4_mmbench_evaluator]
+load_froms = [minigpt_4_mmbench_load_from]
+
+# set the platform and resources
+num_gpus = 8
+num_procs = 8
+launcher = 'pytorch'
+```
+
+### Details of Task
+
+Here is an example of MiniGPT-4 with MMBench and we provide some comments for
+users to understand the meaning of the keys in config.
+
+```python
+from opencompass.multimodal.models.minigpt_4 import (
+    MiniGPT4MMBenchPromptConstructor, MiniGPT4MMBenchPostProcessor)
+
+# dataloader settings
+# Here we use Transforms in MMPreTrain to process images
+val_pipeline = [
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(type='mmpretrain.torchvision/Normalize',
+         mean=(0.48145466, 0.4578275, 0.40821073),
+         std=(0.26862954, 0.26130258, 0.27577711)),
+    dict(type='mmpretrain.PackInputs',
+         algorithm_keys=[
+             'question', 'category', 'l2-category', 'context', 'index',
+             'options_dict', 'options', 'split'
+         ])
+]
+
+# The defined MMBench datasets to load evaluation data
+dataset = dict(type='opencompass.MMBenchDataset',
+               data_file='data/mmbench/mmbench_test_20230712.tsv',
+               pipeline=val_pipeline)
+
+minigpt_4_mmbench_dataloader = dict(batch_size=1,
+                                    num_workers=4,
+                                    dataset=dataset,
+                                    collate_fn=dict(type='pseudo_collate'),
+                                    sampler=dict(type='DefaultSampler',
+                                                 shuffle=False))
+
+# model settings
+minigpt_4_mmbench_model = dict(
+    type='minigpt-4',  # the test multomodal algorithm, the type can be found in `opencompass/multimodal/models/minigpt_4.py`, `@MM_MODELS.register_module('minigpt-4')`
+    low_resource=False,
+    llama_model='/path/to/vicuna-7b/',  # the model path of LLM
+    prompt_constructor=dict(type=MiniGPT4MMBenchPromptConstructor,  # the PromptConstructor to construct the prompt
+                            image_prompt='###Human: <Img><ImageHere></Img>',
+                            reply_prompt='###Assistant:'),
+    post_processor=dict(type=MiniGPT4MMBenchPostProcessor))  # the PostProcessor to deal with the output, process it into the required format
+
+# evaluation settings
+minigpt_4_mmbench_evaluator = [
+    dict(type='opencompass.DumpResults',  # the evaluator will dump results to save_path, code can be found in `opencompass/metrics/dump_results.py`
+         save_path='work_dirs/minigpt-4-7b-mmbench.xlsx')
+]
+
+minigpt_4_mmbench_load_from = '/path/to/prerained_minigpt4_7b.pth'  # the model path of linear layer between Q-Former and LLM in MiniGPT-4
+```
diff --git a/docs/en/index.rst b/docs/en/index.rst
index 5aed9209..b92fd18e 100644
--- a/docs/en/index.rst
+++ b/docs/en/index.rst
@@ -60,6 +60,7 @@ We always welcome *PRs* and *Issues* for the betterment of OpenCompass.
    advanced_guides/new_model.md
    advanced_guides/evaluation_turbomind.md
    advanced_guides/code_eval_service.md
+   advanced_guides/multimodal_eval.md
    advanced_guides/prompt_attack.md
    advanced_guides/longeval.md
 
diff --git a/docs/zh_cn/advanced_guides/multimodal_eval.md b/docs/zh_cn/advanced_guides/multimodal_eval.md
new file mode 100644
index 00000000..619875f6
--- /dev/null
+++ b/docs/zh_cn/advanced_guides/multimodal_eval.md
@@ -0,0 +1,107 @@
+# 多模态评测
+
+我们支持了多个多模态数据集，例如 [MMBench](https://opencompass.org.cn/MMBench)，[SEED-Bench](https://github.com/AILab-CVC/SEED-Bench)，来对多模态模型进行评测。在开始评测之前，请确保您已经按照官方教程下载了评测数据集。
+
+## 开始评测
+
+在评测前，您需要先修改 `tasks.py` 或者创建一个类似的新文件 `tasks_your_model.py` 来对您的模型进行评测。
+
+一般来说我们使用下列命令启动评测。
+
+### Slurm
+
+```sh
+cd $root
+python run.py configs/multimodal/tasks.py --mm-eval --slurm -p $PARTITION
+```
+
+### PyTorch
+
+```sh
+cd $root
+python run.py configs/multimodal/tasks.py --mm-eval
+```
+
+## 配置文件
+
+We adapt the new config format of [MMEngine](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta).
+
+### 任务文件
+
+这是 `configs/multimodal/tasks.py` 的示例。
+
+```python
+from mmengine.config import read_base
+
+with read_base():
+    from .minigpt_4.minigpt_4_7b_mmbench import (minigpt_4_mmbench_dataloader,
+                                                 minigpt_4_mmbench_evaluator,
+                                                 minigpt_4_mmbench_load_from,
+                                                 minigpt_4_mmbench_model)
+
+models = [minigpt_4_mmbench_model]
+datasets = [minigpt_4_mmbench_dataloader]
+evaluators = [minigpt_4_mmbench_evaluator]
+load_froms = [minigpt_4_mmbench_load_from]
+
+# set the platform and resources
+num_gpus = 8
+num_procs = 8
+launcher = 'pytorch'
+```
+
+### 细节配置
+
+这是使用 MMBench 对 MiniGPT-4 进行评测的示例，我们提供了部分注释方便用户理解配置文件的含义。
+
+```python
+from opencompass.multimodal.models.minigpt_4 import (
+    MiniGPT4MMBenchPromptConstructor, MiniGPT4MMBenchPostProcessor)
+
+# dataloader settings
+# 我们使用 MMPreTrain 中的 transforms 对图像数据进行处理
+val_pipeline = [
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(type='mmpretrain.torchvision/Normalize',
+         mean=(0.48145466, 0.4578275, 0.40821073),
+         std=(0.26862954, 0.26130258, 0.27577711)),
+    dict(type='mmpretrain.PackInputs',
+         algorithm_keys=[
+             'question', 'category', 'l2-category', 'context', 'index',
+             'options_dict', 'options', 'split'
+         ])
+]
+
+# 定义 MMBench dataset 来读取对应的数据
+dataset = dict(type='opencompass.MMBenchDataset',
+               data_file='data/mmbench/mmbench_test_20230712.tsv',
+               pipeline=val_pipeline)
+
+minigpt_4_mmbench_dataloader = dict(batch_size=1,
+                                    num_workers=4,
+                                    dataset=dataset,
+                                    collate_fn=dict(type='pseudo_collate'),
+                                    sampler=dict(type='DefaultSampler',
+                                                 shuffle=False))
+
+# model settings
+minigpt_4_mmbench_model = dict(
+    type='minigpt-4',  # 被测试的多模模型，type 在 `opencompass/multimodal/models/minigpt_4.py` 的 `@MM_MODELS.register_module('minigpt-4')` 中有定义
+    low_resource=False,
+    llama_model='/path/to/vicuna-7b/',  # LLM 的模型路径
+    prompt_constructor=dict(type=MiniGPT4MMBenchPromptConstructor,  # 使用 PromptConstructor 来构建 LLM 的输入 prompt
+                            image_prompt='###Human: <Img><ImageHere></Img>',
+                            reply_prompt='###Assistant:'),
+    post_processor=dict(type=MiniGPT4MMBenchPostProcessor))  # 使用 PostProcessor 来处理模型输出，使其符合输出格式的要求
+
+# evaluation settings
+minigpt_4_mmbench_evaluator = [
+    dict(type='opencompass.DumpResults',  # evaluator 将结果保存在 save_path，代码在 `opencompass/metrics/dump_results.py`
+         save_path='work_dirs/minigpt-4-7b-mmbench.xlsx')
+]
+
+minigpt_4_mmbench_load_from = '/path/to/prerained_minigpt4_7b.pth'  # 线性层的模型路径（MiniGPT-4 中 Q-Former 和 LLM 之间的线性投影层）
+```
diff --git a/docs/zh_cn/index.rst b/docs/zh_cn/index.rst
index c7344c19..bde7e1d3 100644
--- a/docs/zh_cn/index.rst
+++ b/docs/zh_cn/index.rst
@@ -60,6 +60,7 @@ OpenCompass 上手路线
    advanced_guides/new_model.md
    advanced_guides/evaluation_turbomind.md
    advanced_guides/code_eval_service.md
+   advanced_guides/multimodal_eval.md
    advanced_guides/prompt_attack.md
    advanced_guides/longeval.md