docs: add links to Checkpoint Conversion Guide in getting started tutorials

Author: shralex

docs/run_maxtext/run_maxtext_localhost.md

@@ -63,7 +63,7 @@ python3 -m maxtext.inference.decode \
   per_device_batch_size=1
 ```
 
-**Note:** Because the model hasn't been properly trained, the output text will be random. To generate meaningful output, you need to load a trained checkpoint using the `load_parameters_path` argument.
+**Note:** Because the model hasn't been properly trained, the output text will be random. To generate meaningful output, you need to load a trained checkpoint using the `load_parameters_path` argument. For instructions on how to convert pre-trained Hugging Face model checkpoints (like Llama or Gemma) to MaxText's Orbax format, please refer to the [Checkpoint Conversion Guide](../guides/checkpointing_solutions/convert_checkpoint.md).
 
 ### Running models using provided configs
 

docs/tutorials/first_run.md

@@ -58,15 +58,15 @@ python3 -m maxtext.inference.decode \
   per_device_batch_size=1
 ```
 
-This command uses a model with randomly initialized weights, so the outputs are also random. To get high quality output you need pass in a checkpoint, typically via the `load_parameters_path` argument.
+This command uses a model with randomly initialized weights, so the outputs are also random. To get high quality output you need pass in a checkpoint, typically via the `load_parameters_path` argument. For instructions on how to convert pre-trained Hugging Face model checkpoints (like Llama or Gemma) to MaxText's Orbax format, please refer to the [Checkpoint Conversion Guide](../guides/checkpointing_solutions/convert_checkpoint.md).
 
 ### Run MaxText via notebook
 
 In the same TPU VM where you just installed all the dependencies of MaxText, You can also run training and decoding in MaxText via Notebook (for e.g., via Jupyter or Colab).
 
 #### Decoding in MaxText via notebook
 
-You can use [demo_decoding.ipynb](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/examples/demo_decoding.ipynb) to try out decoding on MaxText's `Llama3.1-8b` model implementation. In this notebook, we give `"I love to"` as the prompt, and the greedily sampled first output token is `" cook"`. Please remember to provide the path to your `Llama3.1-8b` checkpoint for the `load_parameters_path` argument in the config inside the notebook. You can use [to_maxtext.py](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/checkpoint_conversion/to_maxtext.py) to create a MaxText/Orbax checkpoint from a Huggingface checkpoint.
+You can use [demo_decoding.ipynb](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/examples/demo_decoding.ipynb) to try out decoding on MaxText's `Llama3.1-8b` model implementation. In this notebook, we give `"I love to"` as the prompt, and the greedily sampled first output token is `" cook"`. Please remember to provide the path to your `Llama3.1-8b` checkpoint for the `load_parameters_path` argument in the config inside the notebook. You can use the [to_maxtext.py](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/checkpoint_conversion/to_maxtext.py) script or follow the [Checkpoint Conversion Guide](../guides/checkpointing_solutions/convert_checkpoint.md) to create a MaxText/Orbax checkpoint from a Hugging Face checkpoint.
 
 ### Run MaxText on NVIDIA GPUs