diff --git a/CHANGELOG.md b/CHANGELOG.md
index 7f6aeccf92..4fa3328e4a 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -103,7 +103,7 @@ vNext
 to keyword arguments. See more details in [#3389](https://github.com/google/flax/discussions/3389).
 - Use new typed PRNG keys throughout flax: this essentially involved changing
   uses of `jax.random.PRNGKey` to `jax.random.key`.
-  (See [JEP 9263](https://github.com/google/jax/pull/17297) for details).
+  (See [JEP 9263](https://github.com/jax-ml/jax/pull/17297) for details).
   If you notice dispatch performance regressions after this change, be sure
   you update `jax` to version 0.4.16 or newer.
 - Added `has_improved` field to EarlyStopping and changed the return signature of
diff --git a/docs/contributing.md b/docs/contributing.md
index 72c48ae1af..27b1043322 100644
--- a/docs/contributing.md
+++ b/docs/contributing.md
@@ -240,7 +240,7 @@ section above to keep the contents of both Markdown and Jupyter Notebook files i
 Some of the notebooks are built automatically as part of the pre-submit checks and
 as part of the [Read the Docs](https://flax.readthedocs.io/en/latest) build.
 The build will fail if cells raise errors. If the errors are intentional, you can either catch them,
-or tag the cell with `raises-exceptions` metadata ([example PR](https://github.com/google/jax/pull/2402/files)).
+or tag the cell with `raises-exceptions` metadata ([example PR](https://github.com/jax-ml/jax/pull/2402/files)).
 You have to add this metadata by hand in the `.ipynb` file. It will be preserved when somebody else
 re-saves the notebook.
 
diff --git a/docs/flip/1777-default-dtype.md b/docs/flip/1777-default-dtype.md
index 6344b6bb0e..6d1bca9d01 100644
--- a/docs/flip/1777-default-dtype.md
+++ b/docs/flip/1777-default-dtype.md
@@ -21,7 +21,7 @@ The current behavior is problematic and results in silent bugs, especially for d
 
 ### Dtypes in JAX
 
-JAX uses a NumPy-inspired [dtype promotion](https://github.com/google/jax/blob/main/jax/_src/dtypes.py) mechanism as explained [here](https://jax.readthedocs.io/en/latest/type_promotion.html?highlight=lattice#type-promotion-semantics). The type promotion rules are summarized by the following type lattice:
+JAX uses a NumPy-inspired [dtype promotion](https://github.com/jax-ml/jax/blob/main/jax/_src/dtypes.py) mechanism as explained [here](https://jax.readthedocs.io/en/latest/type_promotion.html?highlight=lattice#type-promotion-semantics). The type promotion rules are summarized by the following type lattice:
 
 ![JAX type promotion lattice](https://jax.readthedocs.io/en/latest/_images/type_lattice.svg)
 
diff --git a/docs/guides/converting_and_upgrading/convert_pytorch_to_flax.rst b/docs/guides/converting_and_upgrading/convert_pytorch_to_flax.rst
index 0ca3edf69b..2b0d0ed5c4 100644
--- a/docs/guides/converting_and_upgrading/convert_pytorch_to_flax.rst
+++ b/docs/guides/converting_and_upgrading/convert_pytorch_to_flax.rst
@@ -300,7 +300,7 @@ To load ``torch.nn.ConvTranspose2d`` parameters into Flax, we need to use the ``
   np.testing.assert_almost_equal(j_out, t_out, decimal=6)
 
 
-.. _`pull request`: https://github.com/google/jax/pull/5772
+.. _`pull request`: https://github.com/jax-ml/jax/pull/5772
 
 .. |nn.ConvTranspose| replace:: ``nn.ConvTranspose``
 .. _nn.ConvTranspose: https://flax.readthedocs.io/en/latest/api_reference/flax.linen/layers.html#flax.linen.ConvTranspose
diff --git a/docs/guides/flax_fundamentals/flax_basics.ipynb b/docs/guides/flax_fundamentals/flax_basics.ipynb
index e8e43f21c1..c2b16e3dd6 100644
--- a/docs/guides/flax_fundamentals/flax_basics.ipynb
+++ b/docs/guides/flax_fundamentals/flax_basics.ipynb
@@ -951,7 +951,7 @@
    "source": [
     "### Exporting to Tensorflow's SavedModel with jax2tf\n",
     "\n",
-    "JAX released an experimental converter called [jax2tf](https://github.com/google/jax/tree/main/jax/jax2tf), which allows converting trained Flax models into Tensorflow's SavedModel format (so it can be used for [TF Hub](https://www.tensorflow.org/hub), [TF.lite](https://www.tensorflow.org/lite), [TF.js](https://www.tensorflow.org/js), or other downstream applications). The repository contains more documentation and has various examples for Flax."
+    "JAX released an experimental converter called [jax2tf](https://github.com/jax-ml/jax/tree/main/jax/jax2tf), which allows converting trained Flax models into Tensorflow's SavedModel format (so it can be used for [TF Hub](https://www.tensorflow.org/hub), [TF.lite](https://www.tensorflow.org/lite), [TF.js](https://www.tensorflow.org/js), or other downstream applications). The repository contains more documentation and has various examples for Flax."
    ]
   }
  ],
diff --git a/docs/guides/flax_fundamentals/flax_basics.md b/docs/guides/flax_fundamentals/flax_basics.md
index 52755e9b5c..5b7e0657ff 100644
--- a/docs/guides/flax_fundamentals/flax_basics.md
+++ b/docs/guides/flax_fundamentals/flax_basics.md
@@ -469,4 +469,4 @@ Flax provides a handy wrapper - `TrainState` - that simplifies the above code. C
 
 ### Exporting to Tensorflow's SavedModel with jax2tf
 
-JAX released an experimental converter called [jax2tf](https://github.com/google/jax/tree/main/jax/jax2tf), which allows converting trained Flax models into Tensorflow's SavedModel format (so it can be used for [TF Hub](https://www.tensorflow.org/hub), [TF.lite](https://www.tensorflow.org/lite), [TF.js](https://www.tensorflow.org/js), or other downstream applications). The repository contains more documentation and has various examples for Flax.
+JAX released an experimental converter called [jax2tf](https://github.com/jax-ml/jax/tree/main/jax/jax2tf), which allows converting trained Flax models into Tensorflow's SavedModel format (so it can be used for [TF Hub](https://www.tensorflow.org/hub), [TF.lite](https://www.tensorflow.org/lite), [TF.js](https://www.tensorflow.org/js), or other downstream applications). The repository contains more documentation and has various examples for Flax.
diff --git a/docs/guides/flax_fundamentals/rng_guide.ipynb b/docs/guides/flax_fundamentals/rng_guide.ipynb
index b2677f1a7f..708b750121 100644
--- a/docs/guides/flax_fundamentals/rng_guide.ipynb
+++ b/docs/guides/flax_fundamentals/rng_guide.ipynb
@@ -105,7 +105,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Set the JAX config variable `jax_threefry_partitionable` to `True`. This will be the default value in the future and makes the PRNG more efficiently auto-parallelizable under `jax.jit`. Refer to [JAX discussion](https://github.com/google/jax/discussions/18480) for more details."
+    "Set the JAX config variable `jax_threefry_partitionable` to `True`. This will be the default value in the future and makes the PRNG more efficiently auto-parallelizable under `jax.jit`. Refer to [JAX discussion](https://github.com/jax-ml/jax/discussions/18480) for more details."
    ]
   },
   {
@@ -1467,7 +1467,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[Flax lifted transforms](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/transformations.html) allow you to use [JAX transforms](https://github.com/google/jax#transformations) with `Module` arguments. This section will show you how to control how PRNG keys are split in Flax lifted transforms.\n",
+    "[Flax lifted transforms](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/transformations.html) allow you to use [JAX transforms](https://github.com/jax-ml/jax#transformations) with `Module` arguments. This section will show you how to control how PRNG keys are split in Flax lifted transforms.\n",
     "\n",
     "Refer to [Lifted transformations](https://flax.readthedocs.io/en/latest/developer_notes/lift.html) for more detail."
    ]
diff --git a/docs/guides/flax_fundamentals/rng_guide.md b/docs/guides/flax_fundamentals/rng_guide.md
index efb5432a17..838feff1ad 100644
--- a/docs/guides/flax_fundamentals/rng_guide.md
+++ b/docs/guides/flax_fundamentals/rng_guide.md
@@ -55,7 +55,7 @@ import hashlib
 jax.devices()
 ```
 
-Set the JAX config variable `jax_threefry_partitionable` to `True`. This will be the default value in the future and makes the PRNG more efficiently auto-parallelizable under `jax.jit`. Refer to [JAX discussion](https://github.com/google/jax/discussions/18480) for more details.
+Set the JAX config variable `jax_threefry_partitionable` to `True`. This will be the default value in the future and makes the PRNG more efficiently auto-parallelizable under `jax.jit`. Refer to [JAX discussion](https://github.com/jax-ml/jax/discussions/18480) for more details.
 
 ```{code-cell}
 jax.config.update('jax_threefry_partitionable', True)
@@ -647,7 +647,7 @@ jax.debug.visualize_array_sharding(out)
 
 +++
 
-[Flax lifted transforms](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/transformations.html) allow you to use [JAX transforms](https://github.com/google/jax#transformations) with `Module` arguments. This section will show you how to control how PRNG keys are split in Flax lifted transforms.
+[Flax lifted transforms](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/transformations.html) allow you to use [JAX transforms](https://github.com/jax-ml/jax#transformations) with `Module` arguments. This section will show you how to control how PRNG keys are split in Flax lifted transforms.
 
 Refer to [Lifted transformations](https://flax.readthedocs.io/en/latest/developer_notes/lift.html) for more detail.
 
diff --git a/docs/guides/flax_sharp_bits.ipynb b/docs/guides/flax_sharp_bits.ipynb
index 22ccae6124..8055e829ae 100644
--- a/docs/guides/flax_sharp_bits.ipynb
+++ b/docs/guides/flax_sharp_bits.ipynb
@@ -49,7 +49,7 @@
     "\n",
     "### Background \n",
     "\n",
-    "The [dropout](https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf) stochastic regularization technique randomly removes hidden and visible units in a network. Dropout is a random operation, requiring a PRNG state, and Flax (like JAX) uses [Threefry](https://github.com/google/jax/blob/main/docs/jep/263-prng.md) PRNG that is splittable. \n",
+    "The [dropout](https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf) stochastic regularization technique randomly removes hidden and visible units in a network. Dropout is a random operation, requiring a PRNG state, and Flax (like JAX) uses [Threefry](https://github.com/jax-ml/jax/blob/main/docs/jep/263-prng.md) PRNG that is splittable. \n",
     "\n",
     "> Note: Recall that JAX has an explicit way of giving you PRNG keys: you can fork the main PRNG state (such as `key = jax.random.key(seed=0)`) into multiple new PRNG keys with `key, subkey = jax.random.split(key)`. Refresh your memory in [🔪 JAX - The Sharp Bits 🔪 Randomness and PRNG keys](https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#random-numbers).\n",
     "\n",
diff --git a/docs/guides/flax_sharp_bits.md b/docs/guides/flax_sharp_bits.md
index 617571144a..a01fc3232c 100644
--- a/docs/guides/flax_sharp_bits.md
+++ b/docs/guides/flax_sharp_bits.md
@@ -41,7 +41,7 @@ Check out a full example below.
 
 ### Background 
 
-The [dropout](https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf) stochastic regularization technique randomly removes hidden and visible units in a network. Dropout is a random operation, requiring a PRNG state, and Flax (like JAX) uses [Threefry](https://github.com/google/jax/blob/main/docs/jep/263-prng.md) PRNG that is splittable. 
+The [dropout](https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf) stochastic regularization technique randomly removes hidden and visible units in a network. Dropout is a random operation, requiring a PRNG state, and Flax (like JAX) uses [Threefry](https://github.com/jax-ml/jax/blob/main/docs/jep/263-prng.md) PRNG that is splittable. 
 
 > Note: Recall that JAX has an explicit way of giving you PRNG keys: you can fork the main PRNG state (such as `key = jax.random.key(seed=0)`) into multiple new PRNG keys with `key, subkey = jax.random.split(key)`. Refresh your memory in [🔪 JAX - The Sharp Bits 🔪 Randomness and PRNG keys](https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#random-numbers).
 
diff --git a/docs/guides/parallel_training/flax_on_pjit.ipynb b/docs/guides/parallel_training/flax_on_pjit.ipynb
index 2eddfb04a8..19861778b9 100644
--- a/docs/guides/parallel_training/flax_on_pjit.ipynb
+++ b/docs/guides/parallel_training/flax_on_pjit.ipynb
@@ -220,7 +220,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that device axis names like `'data'`, `'model'` or `None` are passed into both [`flax.linen.with_partitioning`](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/spmd.html#flax.linen.with_partitioning) and [`jax.lax.with_sharding_constraint`](https://github.com/google/jax/blob/main/jax/_src/pjit.py#L1516) API calls. This refers to how each dimension of this data should be sharded — either across one of the device mesh dimensions, or not sharded at all.\n",
+    "Note that device axis names like `'data'`, `'model'` or `None` are passed into both [`flax.linen.with_partitioning`](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/spmd.html#flax.linen.with_partitioning) and [`jax.lax.with_sharding_constraint`](https://github.com/jax-ml/jax/blob/main/jax/_src/pjit.py#L1516) API calls. This refers to how each dimension of this data should be sharded — either across one of the device mesh dimensions, or not sharded at all.\n",
     "\n",
     "For example:\n",
     "\n",
diff --git a/docs/guides/parallel_training/flax_on_pjit.md b/docs/guides/parallel_training/flax_on_pjit.md
index 98ccdfb248..a20c296627 100644
--- a/docs/guides/parallel_training/flax_on_pjit.md
+++ b/docs/guides/parallel_training/flax_on_pjit.md
@@ -135,7 +135,7 @@ class DotReluDot(nn.Module):
     return z, None
 ```
 
-Note that device axis names like `'data'`, `'model'` or `None` are passed into both [`flax.linen.with_partitioning`](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/spmd.html#flax.linen.with_partitioning) and [`jax.lax.with_sharding_constraint`](https://github.com/google/jax/blob/main/jax/_src/pjit.py#L1516) API calls. This refers to how each dimension of this data should be sharded — either across one of the device mesh dimensions, or not sharded at all.
+Note that device axis names like `'data'`, `'model'` or `None` are passed into both [`flax.linen.with_partitioning`](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/spmd.html#flax.linen.with_partitioning) and [`jax.lax.with_sharding_constraint`](https://github.com/jax-ml/jax/blob/main/jax/_src/pjit.py#L1516) API calls. This refers to how each dimension of this data should be sharded — either across one of the device mesh dimensions, or not sharded at all.
 
 For example:
 
diff --git a/docs/guides/training_techniques/transfer_learning.ipynb b/docs/guides/training_techniques/transfer_learning.ipynb
index ae5400c6f2..909a11715a 100644
--- a/docs/guides/training_techniques/transfer_learning.ipynb
+++ b/docs/guides/training_techniques/transfer_learning.ipynb
@@ -48,7 +48,7 @@
     "# Note that the Transformers library doesn't use the latest Flax version.\n",
     "! pip install -q \"transformers[flax]\"\n",
     "# Install/upgrade Flax and JAX. For JAX installation with GPU/TPU support,\n",
-    "# visit https://github.com/google/jax#installation.\n",
+    "# visit https://github.com/jax-ml/jax#installation.\n",
     "! pip install -U -q flax jax jaxlib"
    ]
   },
diff --git a/docs/guides/training_techniques/transfer_learning.md b/docs/guides/training_techniques/transfer_learning.md
index e596ec6c75..acef5b72f6 100644
--- a/docs/guides/training_techniques/transfer_learning.md
+++ b/docs/guides/training_techniques/transfer_learning.md
@@ -38,7 +38,7 @@ Depending on your task, some of the content in this guide may be suboptimal. For
 # Note that the Transformers library doesn't use the latest Flax version.
 ! pip install -q "transformers[flax]"
 # Install/upgrade Flax and JAX. For JAX installation with GPU/TPU support,
-# visit https://github.com/google/jax#installation.
+# visit https://github.com/jax-ml/jax#installation.
 ! pip install -U -q flax jax jaxlib
 ```
 
diff --git a/docs/guides/training_techniques/use_checkpointing.ipynb b/docs/guides/training_techniques/use_checkpointing.ipynb
index aa13a126b9..38e7ac9e86 100644
--- a/docs/guides/training_techniques/use_checkpointing.ipynb
+++ b/docs/guides/training_techniques/use_checkpointing.ipynb
@@ -48,7 +48,7 @@
    "source": [
     "## Setup\n",
     "\n",
-    "Install/upgrade Flax and [Orbax](https://github.com/google/orbax). For JAX installation with GPU/TPU support, visit [this section on GitHub](https://github.com/google/jax#installation)."
+    "Install/upgrade Flax and [Orbax](https://github.com/google/orbax). For JAX installation with GPU/TPU support, visit [this section on GitHub](https://github.com/jax-ml/jax#installation)."
    ]
   },
   {
diff --git a/docs/guides/training_techniques/use_checkpointing.md b/docs/guides/training_techniques/use_checkpointing.md
index 1be8c5e0e0..9f01b5526e 100644
--- a/docs/guides/training_techniques/use_checkpointing.md
+++ b/docs/guides/training_techniques/use_checkpointing.md
@@ -48,7 +48,7 @@ If you need to learn more about `orbax.checkpoint`, refer to the [Orbax docs](ht
 
 ## Setup
 
-Install/upgrade Flax and [Orbax](https://github.com/google/orbax). For JAX installation with GPU/TPU support, visit [this section on GitHub](https://github.com/google/jax#installation).
+Install/upgrade Flax and [Orbax](https://github.com/google/orbax). For JAX installation with GPU/TPU support, visit [this section on GitHub](https://github.com/jax-ml/jax#installation).
 
 
 Note: Before running `import jax`, create eight fake devices to mimic a [multi-host environment](https://jax.readthedocs.io/en/latest/jax-101/06-parallelism.html?#aside-hosts-and-devices-in-jax) in this notebook. Note that the order of imports is important here. The `os.environ["XLA_FLAGS"] = '--xla_force_host_platform_device_count=8'` command works only with the CPU backend, which means it won't work with GPU/TPU acceleration on if you're running this notebook in Google Colab. If you are already running the code on multiple devices (for example, in a 4x2 TPU environment), you can skip running the next cell.
diff --git a/docs/index.rst b/docs/index.rst
index c286d4f0a0..f4c13ba9d8 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -102,7 +102,7 @@ Installation
    # or to install the latest version of Flax:
    pip install --upgrade git+https://github.com/google/flax.git
 
-Flax installs the vanilla CPU version of JAX, if you need a custom version please check out `JAX's installation page <https://github.com/google/jax#installation>`__.
+Flax installs the vanilla CPU version of JAX, if you need a custom version please check out `JAX's installation page <https://github.com/jax-ml/jax#installation>`__.
 
 Basic usage
 ^^^^^^^^^^^^
diff --git a/docs/quick_start.ipynb b/docs/quick_start.ipynb
index 32530b9bed..ee1224d601 100644
--- a/docs/quick_start.ipynb
+++ b/docs/quick_start.ipynb
@@ -12,7 +12,7 @@
     "\n",
     "Welcome to Flax!\n",
     "\n",
-    "Flax is an open source Python neural network library built on top of [JAX](https://github.com/google/jax). This tutorial demonstrates how to construct a simple convolutional neural\n",
+    "Flax is an open source Python neural network library built on top of [JAX](https://github.com/jax-ml/jax). This tutorial demonstrates how to construct a simple convolutional neural\n",
     "network (CNN) using the [Flax](https://flax.readthedocs.io) Linen API and train\n",
     "the network for image classification on the MNIST dataset."
    ]
diff --git a/docs/quick_start.md b/docs/quick_start.md
index ac8a9fb860..1bd713d56d 100644
--- a/docs/quick_start.md
+++ b/docs/quick_start.md
@@ -16,7 +16,7 @@ jupytext:
 
 Welcome to Flax!
 
-Flax is an open source Python neural network library built on top of [JAX](https://github.com/google/jax). This tutorial demonstrates how to construct a simple convolutional neural
+Flax is an open source Python neural network library built on top of [JAX](https://github.com/jax-ml/jax). This tutorial demonstrates how to construct a simple convolutional neural
 network (CNN) using the [Flax](https://flax.readthedocs.io) Linen API and train
 the network for image classification on the MNIST dataset.
 
diff --git a/docs_nnx/contributing.md b/docs_nnx/contributing.md
index 72c48ae1af..27b1043322 100644
--- a/docs_nnx/contributing.md
+++ b/docs_nnx/contributing.md
@@ -240,7 +240,7 @@ section above to keep the contents of both Markdown and Jupyter Notebook files i
 Some of the notebooks are built automatically as part of the pre-submit checks and
 as part of the [Read the Docs](https://flax.readthedocs.io/en/latest) build.
 The build will fail if cells raise errors. If the errors are intentional, you can either catch them,
-or tag the cell with `raises-exceptions` metadata ([example PR](https://github.com/google/jax/pull/2402/files)).
+or tag the cell with `raises-exceptions` metadata ([example PR](https://github.com/jax-ml/jax/pull/2402/files)).
 You have to add this metadata by hand in the `.ipynb` file. It will be preserved when somebody else
 re-saves the notebook.
 
diff --git a/docs_nnx/flip/1777-default-dtype.md b/docs_nnx/flip/1777-default-dtype.md
index 6344b6bb0e..6d1bca9d01 100644
--- a/docs_nnx/flip/1777-default-dtype.md
+++ b/docs_nnx/flip/1777-default-dtype.md
@@ -21,7 +21,7 @@ The current behavior is problematic and results in silent bugs, especially for d
 
 ### Dtypes in JAX
 
-JAX uses a NumPy-inspired [dtype promotion](https://github.com/google/jax/blob/main/jax/_src/dtypes.py) mechanism as explained [here](https://jax.readthedocs.io/en/latest/type_promotion.html?highlight=lattice#type-promotion-semantics). The type promotion rules are summarized by the following type lattice:
+JAX uses a NumPy-inspired [dtype promotion](https://github.com/jax-ml/jax/blob/main/jax/_src/dtypes.py) mechanism as explained [here](https://jax.readthedocs.io/en/latest/type_promotion.html?highlight=lattice#type-promotion-semantics). The type promotion rules are summarized by the following type lattice:
 
 ![JAX type promotion lattice](https://jax.readthedocs.io/en/latest/_images/type_lattice.svg)
 
diff --git a/docs_nnx/mnist_tutorial.ipynb b/docs_nnx/mnist_tutorial.ipynb
index 147ed07b06..a1aa4eae89 100644
--- a/docs_nnx/mnist_tutorial.ipynb
+++ b/docs_nnx/mnist_tutorial.ipynb
@@ -12,7 +12,7 @@
     "\n",
     "Welcome to Flax NNX! In this tutorial you will learn how to build and train a simple convolutional neural network (CNN) to classify handwritten digits on the MNIST dataset using the Flax NNX API.\n",
     "\n",
-    "Flax NNX is a Python neural network library built upon [JAX](https://github.com/google/jax). If you have used the Flax Linen API before, check out [Why Flax NNX](https://flax.readthedocs.io/en/latest/why.html). You should have some knowledge of the main concepts of deep learning.\n",
+    "Flax NNX is a Python neural network library built upon [JAX](https://github.com/jax-ml/jax). If you have used the Flax Linen API before, check out [Why Flax NNX](https://flax.readthedocs.io/en/latest/why.html). You should have some knowledge of the main concepts of deep learning.\n",
     "\n",
     "Let’s get started!"
    ]
diff --git a/docs_nnx/mnist_tutorial.md b/docs_nnx/mnist_tutorial.md
index 74be0ec5b4..a4a05cf4ba 100644
--- a/docs_nnx/mnist_tutorial.md
+++ b/docs_nnx/mnist_tutorial.md
@@ -16,7 +16,7 @@ jupytext:
 
 Welcome to Flax NNX! In this tutorial you will learn how to build and train a simple convolutional neural network (CNN) to classify handwritten digits on the MNIST dataset using the Flax NNX API.
 
-Flax NNX is a Python neural network library built upon [JAX](https://github.com/google/jax). If you have used the Flax Linen API before, check out [Why Flax NNX](https://flax.readthedocs.io/en/latest/why.html). You should have some knowledge of the main concepts of deep learning.
+Flax NNX is a Python neural network library built upon [JAX](https://github.com/jax-ml/jax). If you have used the Flax Linen API before, check out [Why Flax NNX](https://flax.readthedocs.io/en/latest/why.html). You should have some knowledge of the main concepts of deep learning.
 
 Let’s get started!