docs(nnx): add native LoRA Gemma4, Qwen3, and Llama3 tutorial notebooks

Author: RexBearIU

docs/guides/run_python_notebook.md

@@ -194,6 +194,11 @@ jupyter lab --ip=0.0.0.0 --port=8888 --no-browser --allow-root
 
 - **`rl_llama3_demo.ipynb`** → GRPO/GSPO training on [OpenAI's GSM8K dataset](https://huggingface.co/datasets/openai/gsm8k). We recommend running this on a v5p-8 TPU VM using [Method 2](#method-2-visual-studio-code-with-tpu-recommended) or [Method 3](#method-3-local-jupyter-lab-with-tpu-recommended).
 
+### Parameter-Efficient Fine-Tuning (PEFT/LoRA)
+
+- **`qwen3_native_lora_demo.ipynb`** → Qwen3-0.6B PEFT training and evaluation with native LoRA and QLoRA. Includes both SFT training on [OpenAI's GSM8K dataset](https://huggingface.co/datasets/openai/gsm8k) and pre-training. Runs successfully on free-tier Google Colab TPUs.
+- **`gemma4_native_lora_demo.ipynb`** → Gemma4-e2b PEFT training and evaluation with native LoRA and QLoRA, demonstrating multi-query attention (MQA) support under Flax NNX. We recommend running this on a v5p-8 TPU VM using [Method 2](#method-2-visual-studio-code-with-tpu-recommended) or [Method 3](#method-3-local-jupyter-lab-with-tpu-recommended).
+
 ## Common Pitfalls & Debugging
 
 | Issue                          | Solution                                                     |

docs/tutorials/post_training_index.md

@@ -27,6 +27,7 @@ MaxText was co-designed with key Google led innovations to provide a unified pos
   - [SFT on Single-Host TPUs](./posttraining/sft.md)
   - [SFT on Multi-Host TPUs](./posttraining/sft_on_multi_host.md)
 - **LoRA (Low-Rank Adaptation)**
+  - [Native LoRA/QLoRA on Single-Host TPUs](./posttraining/native_lora.md)
   - [LoRA on Single-Host TPUs](./posttraining/lora.md)
   - [LoRA on Multi-Host TPUs](./posttraining/lora_on_multi_host.md)
 - **DPO (Direct Preference Optimization) and ORPO (Odds-Ratio Policy Optimization)**
@@ -79,6 +80,7 @@ posttraining/rl_qwen3_30b.md
 posttraining/rl_gptoss_20b.md
 posttraining/knowledge_distillation.md
 posttraining/lora.md
+posttraining/native_lora.md
 posttraining/lora_on_multi_host.md
 posttraining/multimodal.md
 posttraining/full_finetuning.md

docs/tutorials/posttraining/native_lora.md

@@ -0,0 +1,166 @@
+<!--
+ Copyright 2023–2026 Google LLC
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+      https://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ -->
+
+# Native NNX LoRA on single-host TPUs
+
+**Native Low-Rank Adaptation (LoRA)** under pure Flax NNX in MaxText provides a highly optimized, state-of-the-art parameter-efficient fine-tuning (PEFT) framework.
+
+Unlike traditional Linen/Tunix adapter wrappers, Native NNX LoRA operates by directly wrapping NNX modules. This allows for:
+
+- **Zero adapter-wrapping overhead**: Cleaner model codebases and simplified parameter matching.
+- **Native Checkpoint Save and Restore**: Full out-of-the-box compatibility with Orbax checkpointers, allowing frozen base weights and active adapter parameters to be saved/loaded seamlessly.
+- **Int8 Weight Quantization (QLoRA)**: Full support for memory-efficient 8-bit quantization during fine-tuning.
+
+This tutorial provides step-by-step instructions for performing native LoRA/QLoRA fine-tuning and pre-training on single-host TPUs using pure Flax NNX.
+
+______________________________________________________________________
+
+## 🚀 Quick Experimentation with Notebooks
+
+For interactive playground setups on Google Colab or local JupyterLab, we provide fully detailed demo notebooks:
+
+- **Qwen3 Native LoRA Demo**: [qwen3_native_lora_demo.ipynb](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/examples/qwen3_native_lora_demo.ipynb)
+- **Gemma4 Native LoRA Demo**: [gemma4_native_lora_demo.ipynb](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/examples/gemma4_native_lora_demo.ipynb)
+
+______________________________________________________________________
+
+## Setup environment variables
+
+Log in to Hugging Face. Provide your access token when prompted:
+
+```bash
+hf auth login
+```
+
+Set the following environment variables before running LoRA Fine-tuning.
+
+```sh
+# -- Model configuration --
+export MODEL_NAME=<MODEL_NAME> # e.g., 'qwen3-0.6b' or 'gemma4-e2b'
+export TOKENIZER_PATH=<TOKENIZER_PATH> # e.g., 'Qwen/Qwen3-0.6B' or 'google/gemma-4-E2B-it'
+
+# -- MaxText configuration --
+export BASE_OUTPUT_DIRECTORY=<GCS_BUCKET> # e.g., gs://my-bucket/my-output-directory or /path/to/my-output-directory
+export RUN_NAME=<RUN_NAME> # e.g., $(date +%Y-%m-%d-%H-%M-%S)
+export STEPS=<STEPS> # e.g., 1000
+export PER_DEVICE_BATCH_SIZE=<BATCH_SIZE_PER_DEVICE> # e.g., 1
+export LORA_RANK=<LORA_RANK> # e.g., 16
+export LORA_ALPHA=<LORA_ALPHA> # e.g., 32.0
+export LEARNING_RATE=<LEARNING_RATE> # e.g., 3e-6
+export MAX_TARGET_LENGTH=<MAX_TARGET_LENGTH> # e.g., 1024
+
+# -- Dataset configuration --
+export DATASET_NAME=<DATASET_NAME> # e.g., openai/gsm8k
+export TRAIN_SPLIT=<TRAIN_SPLIT> # e.g., train
+export HF_DATA_DIR=<DATASET_PATH> # e.g., main
+export TRAIN_DATA_COLUMNS=<DATA_COLUMNS> # 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=<CKPT_PATH> # e.g., gs://my-bucket/my-model-checkpoint/0/items or /path/to/my-model-checkpoint/0/items
+```
+
+### Option 2: Converting a Hugging Face checkpoint
+
+Refer to the steps in [Hugging Face to MaxText](../../guides/checkpointing_solutions/convert_checkpoint.md#hugging-face-to-maxtext) to convert a Hugging Face checkpoint to MaxText. Similar to Option 1, you can set the following environment variable and move on.
+
+```sh
+export MAXTEXT_CKPT_PATH=<CKPT_PATH> # e.g., gs://my-bucket/my-model-checkpoint/0/items or /path/to/my-model-checkpoint/0/items
+```
+
+______________________________________________________________________
+
+## Run Native LoRA Fine-Tuning
+
+Execute the following command to begin LoRA fine-tuning on a Hugging Face dataset (e.g. GSM8K) using the native SFT entrypoint `train_sft_native.py`:
+
+```sh
+python3 -m maxtext.trainers.post_train.sft.train_sft_native \
+    src/maxtext/configs/post_train/sft.yml \
+    run_name="${RUN_NAME?}" \
+    base_output_directory="${BASE_OUTPUT_DIRECTORY?}" \
+    model_name="${MODEL_NAME?}" \
+    load_parameters_path="${MAXTEXT_CKPT_PATH?}" \
+    tokenizer_path="${TOKENIZER_PATH?}" \
+    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=bfloat16 \
+    dtype=bfloat16 \
+    formatting_func_path="maxtext.input_pipeline.instruction_data_processing.math_qa_formatting" \
+    formatting_func_kwargs="{'template_path': 'src/maxtext/examples/chat_templates/math_qa.json'}" \
+    lora.enable_lora=True \
+    lora.lora_rank="${LORA_RANK?}" \
+    lora.lora_alpha="${LORA_ALPHA?}"
+```
+
+______________________________________________________________________
+
+## Run Native Pre-training with QLoRA (8-bit Quantization)
+
+To run a standard native pre-training loop with memory-efficient 8-bit quantized weights, execute:
+
+```sh
+python3 -m maxtext.trainers.pre_train.train \
+    src/maxtext/configs/base.yml \
+    run_name="native_qlora_pretrain_demo" \
+    model_name="gemma4-e2b" \
+    scan_layers=False \
+    steps=10 \
+    dataset_type="synthetic" \
+    per_device_batch_size=1 \
+    max_target_length=32 \
+    enable_checkpointing=True \
+    checkpoint_period=5 \
+    base_output_directory="/tmp/native_qlora_pretrain_checkpoint" \
+    attention="dot_product" \
+    weight_dtype="bfloat16" \
+    dtype="bfloat16" \
+    lora.enable_lora=True \
+    lora.lora_weight_qtype="int8" \
+    lora.lora_tile_size=32 \
+    lora.lora_rank=4 \
+    lora.lora_alpha=8.0
+```
+
+______________________________________________________________________
+
+## ⚙️ LoRA/QLoRA Configuration Reference
+
+All low-rank adaptation properties are prefixed under the `lora.` namespace inside the configuration. The key arguments are:
+
+| Parameter                | Type    | Default | Description                                                 |
+| ------------------------ | ------- | ------- | ----------------------------------------------------------- |
+| `lora.enable_lora`       | `bool`  | `False` | Enables/Disables native LoRA wrapping.                      |
+| `lora.lora_rank`         | `int`   | `4`     | The low-rank dimension ($r$) of the adapters.               |
+| `lora.lora_alpha`        | `float` | `8.0`   | Scaling hyperparameter ($\alpha$) for the low-rank updates. |
+| `lora.lora_weight_qtype` | `str`   | `""`    | Set to `"int8"` to enable 8-bit quantized weights (QLoRA).  |
+| `lora.lora_tile_size`    | `int`   | `32`    | Tiling dimension for quantized linear layers.               |