Merge pull request #580 from google:mohit/fix_gemma_links

PiperOrigin-RevId: 623256002

README.md

@@ -45,7 +45,7 @@ For your first time running MaxText, we provide specific [instructions](getting_
 MaxText supports training and inference of various open models. Follow user guides in the [getting started](getting_started) folder to know more.
 
 Some extra helpful guides:
-* [Gemma](https://ai.google.dev/gemma): a family of open-weights Large Language Model (LLM) by [Google DeepMind](https://deepmind.google/), based on Gemini research and technology. You can run decode and finetuning using [these instructions](getting_started/Run_Gemma.md).
+* [Gemma](https://ai.google.dev/gemma): a family of open-weights Large Language Model (LLM) by [Google DeepMind](https://deepmind.google/), based on Gemini research and technology. You can run decode and finetuning using [these instructions](end_to_end/gemma/Run_Gemma.md).
 * [Llama2](https://llama.meta.com/llama2/): a family of open-weights Large Language Model (LLM) by Meta. You can run decode and finetuning using [these instructions](getting_started/Run_Llama2.md).
 
 In addition to the getting started guides, there are always other MaxText capabilities that are being constantly being added! The full suite of end-to-end tests is in [end_to_end](end_to_end). We run them with a nightly cadence. They can be a good source for understanding MaxText Alternatively you can see the continuous [unit tests](.github/workflows/UnitTests.yml) which are run almost continuously.

end_to_end/gemma/Run_Gemma.md

@@ -19,7 +19,7 @@
 
 Following the instructions at [kaggle](https://www.kaggle.com/models/google/gemma/frameworks/maxText) will let you download Gemma model weights. You will have to consent to license for Gemma using your kaggle account's [API credentials](https://github.com/Kaggle/kaggle-api?tab=readme-ov-file#api-credentials).
 
-After downloading the weights run [test_convert_chkpt.sh](https://github.com/google/maxtext/blob/main/end_to_end/gemma/test_convert_chkpt.sh), which converts the checkpoint to be compatible with MaxText and uploads them to a GCS bucket. You can run decode and finetuning using instructions mentioned in the test scripts at [end_to_end/gemma](https://github.com/google/maxtext/blob/main/end_to_end/gemma).
+After downloading the weights run [convert_gemma_chkpt.py](../../MaxText/convert_gemma_chkpt.py), which converts the checkpoint to be compatible with MaxText and uploads them to a GCS bucket. You can run decode and finetuning using instructions mentioned in the test scripts at [end_to_end/gemma](../../end_to_end/gemma).
 
 ## MaxText supports pretraining and finetuning with high performance