Fix docs

configs/llava/README.md

@@ -21,7 +21,7 @@ Instruction tuning large language models (LLMs) using machine-generated instruct
 According to the license of LLaMA, we cannot provide the merged checkpoint directly. Please use the below
 script to download and get the merged the checkpoint.
 
-```baseh
+```shell
 python tools/model_converters/llava-delta2mmpre.py huggyllama/llama-7b liuhaotian/LLaVA-Lightning-7B-delta-v1-1 ./LLaVA-Lightning-7B-delta-v1-1.pth
 ```
 

docs/en/notes/changelog.md

@@ -7,7 +7,7 @@
 - Support inference of more **multi-modal** algorithms, such as **LLaVA**, **MiniGPT-4**, **Otter**, etc.
 - Support around **10 multi-modal datasets**!
 - Add **iTPN**, **SparK** self-supervised learning algorithms.
-- Provide examples of [New Config](./mmpretrain/configs/) and [DeepSpeed/FSDP](./configs/mae/benchmarks/).
+- Provide examples of [New Config](https://github.com/open-mmlab/mmpretrain/tree/main/mmpretrain/configs/) and [DeepSpeed/FSDP](https://github.com/open-mmlab/mmpretrain/tree/main/configs/mae/benchmarks/).
 
 ### New Features
 

docs/en/useful_tools/shape_bias.md

@@ -1,10 +1,10 @@
-## Shape Bias Tool Usage
+# Shape Bias Tool Usage
 
 Shape bias measures how a model relies the shapes, compared to texture, to sense the semantics in images. For more details,
 we recommend interested readers to this [paper](https://arxiv.org/abs/2106.07411). MMPretrain provide an off-the-shelf toolbox to
 obtain the shape bias of a classification model. You can following these steps below:
 
-### Prepare the dataset
+## Prepare the dataset
 
 First you should download the [cue-conflict](https://github.com/bethgelab/model-vs-human/releases/download/v0.1/cue-conflict.tar.gz) to `data` folder,
 and then unzip this dataset. After that, you `data` folder should have the following structure:
@@ -18,7 +18,7 @@ data
 |      |── truck
 ```
 
-### Modify the config for classification
+## Modify the config for classification
 
 We run the shape-bias tool on a ViT-base model with masked autoencoder pretraining. Its config file is `configs/mae/benchmarks/vit-base-p16_8xb128-coslr-100e_in1k.py`, and its checkpoint is downloaded from [this link](https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-base-p16_8xb512-fp16-coslr-1600e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k_20220825-cf70aa21.pth). Replace the original test_pipeline, test_dataloader and test_evaluation with the following configurations:
 
@@ -55,7 +55,7 @@ test_evaluator = dict(
 
 Please note you should make custom modifications to the `csv_dir` and `model_name` above. I renamed my modified sample config file as `vit-base-p16_8xb128-coslr-100e_in1k_shape-bias.py` in the folder `configs/mae/benchmarks/`.
 
-### Inference your model with above modified config file
+## Inference your model with above modified config file
 
 Then you should inferece your model on the `cue-conflict` dataset with the your modified config file.
 
@@ -77,7 +77,7 @@ bash tools/dist_test.sh configs/mae/benchmarks/vit-base-p16_8xb128-coslr-100e_in
 After that, you should obtain a csv file in `csv_dir` folder, named `cue-conflict_model-name_session-1.csv`. Besides this file, you should also download these [csv files](https://github.com/bethgelab/model-vs-human/tree/master/raw-data/cue-conflict) to the
 `csv_dir`.
 
-### Plot shape bias
+## Plot shape bias
 
 Then we can start to plot the shape bias:
 

mmpretrain/models/multimodal/flamingo/flamingo.py

@@ -23,7 +23,7 @@ class Flamingo(BaseModel):
         zeroshot_prompt (str): Prompt used for zero-shot inference.
             Defaults to '<image>Output:'.
         shot_prompt_tmpl (str): Prompt used for few-shot inference.
-            Defaults to '<image>Output:{caption}<|endofchunk|>'.
+            Defaults to ``<image>Output:{caption}<|endofchunk|>``.
         final_prompt_tmpl (str): Final part of prompt used for inference.
             Defaults to '<image>Output:'.
         generation_cfg (dict): The extra generation config, accept the keyword

mmpretrain/models/multimodal/minigpt4/minigpt4.py

@@ -36,7 +36,7 @@ class MiniGPT4(BaseModel):
         raw_prompts (list): Prompts for training. Defaults to None.
         max_txt_len (int): Max token length while doing tokenization. Defaults
             to 32.
-        end_sym (str): Ended symbol of the sequence. Defaults to '\n'.
+        end_sym (str): Ended symbol of the sequence. Defaults to '\\n'.
         generation_cfg (dict): The config of text generation. Defaults to
             dict().
         data_preprocessor (:obj:`BaseDataPreprocessor`): Used for

mmpretrain/models/multimodal/otter/otter.py

@@ -20,8 +20,8 @@ class Otter(Flamingo):
         zeroshot_prompt (str): Prompt used for zero-shot inference.
             Defaults to an.
         shot_prompt_tmpl (str): Prompt used for few-shot inference.
-            Defaults to '<image>User:Please describe the image.
-            GPT:<answer>{caption}<|endofchunk|>'.
+            Defaults to ``<image>User:Please describe the image.
+            GPT:<answer>{caption}<|endofchunk|>``.
         final_prompt_tmpl (str): Final part of prompt used for inference.
             Defaults to '<image>User:Please describe the image. GPT:<answer>'.
         generation_cfg (dict): The extra generation config, accept the keyword