Merge pull request #2682 from AI-Hypercomputer:hengtaoguo-doc

PiperOrigin-RevId: 832063253

Author: Google-ML-Automation

README.md

@@ -18,6 +18,8 @@
 
 [![MaxText Package Tests](https://github.com/AI-Hypercomputer/maxtext/actions/workflows/RunTests.yml/badge.svg)](https://github.com/AI-Hypercomputer/maxtext/actions/workflows/build_and_test_maxtext.yml)
 
+> **_NOTE:_** We recommend running MaxText with Python 3.12, as it is our primary supported version. Other Python versions may encounter compatibility issues.
+
 MaxText is a high performance, highly scalable, open-source LLM library and reference implementation written in pure Python/[JAX](https://docs.jax.dev/en/latest/jax-101.html) and targeting Google Cloud TPUs and GPUs for training. 
 
 MaxText provides a library of high performance models to choose from, including Gemma, Llama, DeepSeek, Qwen, and Mistral. For each of these models, MaxText supports pre-training (up to tens of thousands of chips) and scalable post-training, with popular techniques like Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO, a type of Reinforcement Learning). 

docs/index.md

@@ -16,8 +16,6 @@
 
 # MaxText
 
-> **_NOTE:_** We recommend running MaxText with Python 3.12, as it is our primary supported version. Other Python versions may encounter compatibility issues.
-
 MaxText is a high performance, highly scalable, open-source LLM library and reference implementation written in pure Python/[JAX](https://docs.jax.dev/en/latest/jax-101.html) and targeting Google Cloud TPUs and GPUs for training.
 
 MaxText provides a library of high performance models to choose from, including Gemma, Llama, DeepSeek, Qwen, and Mistral. For each of these models, MaxText supports pre-training (up to tens of thousands of chips) and scalable post-training, with popular techniques like Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO, a type of Reinforcement Learning).