[doc] migrate the markdown files (#2652)

docs/source/en/basics/model_checkpoint.md

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+# Model Checkpoint
+
+Author : Guangyang Lu
+
+**Prerequisite:**
+- [Launch Colossal-AI](./launch_colossalai.md)
+- [Initialize Colossal-AI](./initialize_features.md)
+
+**Example Code:**
+- [ColossalAI-Examples Model Checkpoint](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/utils/checkpoint)
+
+**This function is experiential.**
+
+## Introduction
+
+In this tutorial, you will learn how to save and load model checkpoints.
+
+To leverage the power of parallel strategies in Colossal-AI, modifications to models and tensors are needed, for which you cannot directly use `torch.save` or `torch.load`  to save or load model checkpoints. Therefore, we have provided you with the API to achieve the same thing.
+
+Moreover, when loading, you are not demanded to use the same parallel strategy as saving.
+
+## How to use
+
+### Save
+
+There are two ways to train a model in Colossal-AI, by engine or by trainer.
+**Be aware that we only save the `state_dict`.** Therefore, when loading the checkpoints, you need to define the model first.
+
+#### Save when using engine
+
+```python
+from colossalai.utils import save_checkpoint
+model = ...
+engine, _, _, _ = colossalai.initialize(model=model, ...)
+for epoch in range(num_epochs):
+    ... # do some training
+    save_checkpoint('xxx.pt', epoch, model)
+```
+
+#### Save when using trainer
+```python
+from colossalai.trainer import Trainer, hooks
+model = ...
+engine, _, _, _ = colossalai.initialize(model=model, ...)
+trainer = Trainer(engine, ...)
+hook_list = [
+            hooks.SaveCheckpointHook(1, 'xxx.pt', model)
+            ...]
+
+trainer.fit(...
+            hook=hook_list)
+```
+
+### Load
+
+```python
+from colossalai.utils import load_checkpoint
+model = ...
+load_checkpoint('xxx.pt', model)
+... # train or test
+```