docs: add LoRA fine-tuning tutorial

docs/tutorials/posttraining/lora.md

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+
+# LoRA Fine-tuning on single-host TPUs
+
+**Low-Rank Adaptation (LoRA)** is a Parameter-Efficient Fine-Tuning (PEFT) technique designed to optimize large language models while minimizing resource consumption.
+
+Unlike traditional full-parameter fine-tuning, LoRA:
+* **Freezes the pre-trained model weights**, preserving the original knowledge.
+* **Injects trainable rank decomposition matrices** into the Transformer layers.
+
+This approach **greatly reduces the number of trainable parameters** required for downstream tasks, making the process faster and more memory-efficient.
+
+This tutorial provides step-by-step instructions for setting up the environment and performing LoRA fine-tuning on a Hugging Face dataset using MaxText. 
+
+We use [Tunix](https://github.com/google/tunix), a JAX-based library, to power these post-training tasks.
+
+In this tutorial we use a single host TPU VM such as `v6e-8/v5p-8`. Let's get started!
+
+## Install MaxText and Post-Training dependencies
+
+For instructions on installing MaxText with post-training dependencies on your VM, please refer to the [official documentation](https://maxtext.readthedocs.io/en/latest/install_maxtext.html) and use the `maxtext[tpu-post-train]` installation path to include all necessary post-training dependencies.
+
+## Setup environment variables
+
+Set the following environment variables before running LoRA Fine-tuning.
+
+```sh
+# -- Model configuration --
+export MODEL_NAME=<MaxText Model> # e.g., 'llama3.1-8b-Instruct'
+
+# -- MaxText configuration --
+export BASE_OUTPUT_DIRECTORY=<output directory to store run logs> # e.g., gs://my-bucket/my-output-directory
+export RUN_NAME=<name for this run> # e.g., $(date +%Y-%m-%d-%H-%M-%S)
+export STEPS=<number of fine-tuning steps to run> # e.g., 1000
+export PER_DEVICE_BATCH_SIZE=<batch size per device> # e.g., 1
+export HF_TOKEN=<Hugging Face Access Token>
+export LORA_RANK=<dimension of the low-rank update matrices> # e.g., 16
+export LORA_ALPHA=<scaling factor for LoRA weights> # e.g., 32.0
+export LEARNING_RATE=<step size for the optimizer> # e.g., 3e-6
+export MAX_TARGET_LENGTH=<maximum sequence length for input and output tokens> # e.g., 1024
+export WEIGHT_DTYPE=<data type for storing model weights> # e.g., bfloat16
+export DTYPE=<data type for numerical computations> # e.g., bfloat16
+
+# -- Dataset configuration --
+export DATASET_NAME=<Hugging Face dataset name> # e.g., openai/gsm8k
+export TRAIN_SPLIT=<data split for train> # e.g., train
+export HF_DATA_DIR=<The directory or sub-config within the Hugging Face dataset> # e.g., main
+export TRAIN_DATA_COLUMNS=<data columns to train on> # e.g., ['question','answer']
+```
+
+## Get your model checkpoint
+
+This section explains how to prepare your model checkpoint for use with MaxText. You have two options: using an existing MaxText checkpoint or converting a Hugging Face checkpoint.
+
+### Option 1: Using an existing MaxText checkpoint
+
+If you already have a MaxText-compatible model checkpoint, simply set the following environment variable and move on to the next section.
+
+```sh
+export MAXTEXT_CKPT_PATH=<gcs path for MaxText checkpoint> # e.g., gs://my-bucket/my-model-checkpoint/0/items
+```
+
+### Option 2: Converting a Hugging Face checkpoint
+
+Refer the steps in [Hugging Face to MaxText](https://maxtext.readthedocs.io/en/maxtext-v0.2.1/guides/checkpointing_solutions/convert_checkpoint.html#hugging-face-to-maxtext) to convert a hugging face checkpoint to MaxText. Make sure you have correct checkpoint files converted and saved. Similar as Option 1, you can set the following environment and move on.
+
+```sh
+export MAXTEXT_CKPT_PATH=<gcs path for MaxText checkpoint> # e.g., gs://my-bucket/my-model-checkpoint/0/items
+```
+## (Optional) Resume from a previous LoRA checkpoint
+
+If you want to resume training from a previous run or further fine-tune an existing LoRA adapter, you can specify the LoRA checkpoint path.
+- **load_parameters_path**: Points to the frozen base model weights (the original model).
+- **lora_restore_path**: Points to the previous LoRA adapter weights you wish to load.
+
+```sh
+# If starting fresh, you can leave this empty or skip this variable
+export LORA_RESTORE_PATH=<gcs path to your previous LoRA checkpoint> # e.g., gs://my-bucket/run-1/checkpoints/0/items
+```
+## Run LoRA Fine-Tuning on Hugging Face Dataset
+
+Once your environment variables and checkpoints are ready, you can start the LoRA fine-tuning process.
+
+Execute the following command to begin training:
+
+```sh
+python3 -m maxtext.trainers.post_train.sft.train_sft \
+    maxtext/configs/post_train/sft.yml \
+    run_name="${RUN_NAME}" \
+    base_output_directory="${BASE_OUTPUT_DIRECTORY}" \
+    model_name="${MODEL_NAME}" \
+    load_parameters_path="${MAXTEXT_CKPT_PATH}" \
+    lora_restore_path="${LORA_RESTORE_PATH}" \
+    hf_access_token="${HF_TOKEN}" \
+    hf_path="${DATASET_NAME}" \
+    train_split="${TRAIN_SPLIT}" \
+    hf_data_dir="${HF_DATA_DIR}" \
+    train_data_columns="${TRAIN_DATA_COLUMNS}" \
+    steps="${STEPS}" \
+    per_device_batch_size="${PER_DEVICE_BATCH_SIZE}" \
+    max_target_length="${MAX_TARGET_LENGTH}" \
+    learning_rate="${LEARNING_RATE}" \
+    weight_dtype="${WEIGHT_DTYPE}" \
+    dtype="${DTYPE}" \
+    profiler=xplane \
+    enable_nnx=True \
+    pure_nnx_decoder=True \
+    enable_lora=True \
+    lora_rank="${LORA_RANK}" \
+    lora_alpha="${LORA_ALPHA}" \
+    scan_layers=True
+```
+
+Your fine-tuned model checkpoints will be saved here: `$BASE_OUTPUT_DIRECTORY/$RUN_NAME/checkpoints`.