diff --git a/docs/source/contrib.rst b/docs/source/contrib.rst
index 741417163..b2dca2ba1 100644
--- a/docs/source/contrib.rst
+++ b/docs/source/contrib.rst
@@ -9,3 +9,66 @@ Nested Sampling
     :undoc-members:
     :show-inheritance:
     :member-order: bysource
+
+
+Stein Variational Inference
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Stein Variational Inference (SteinVI) is a family of VI techniques for approximate Bayesian inference based on
+Stein’s method (see [1] for an overview). It is gaining popularity as it combines
+the scalability of traditional VI with the flexibility of non-parametric particle-based methods.
+
+Stein variational gradient descent (SVGD) [2] is a recent SteinVI technique which uses iteratively moves a set of
+particles :math:`\{z_i\}_{i=1}^N` to approximate a distribution p(z).
+SVGD is well suited for capturing correlations between latent variables as a particle-based method.
+The technique preserves the scalability of traditional VI approaches while offering the flexibility and modeling scope
+of methods such as Markov chain Monte Carlo (MCMC). SVGD is good at capturing multi-modality [3][4].
+
+``numpyro.contrib.einstein`` is a framework for particle-based inference using the ELBO-within-Stein algorithm.
+The framework works on Stein mixtures, a restricted mixture of guide programs parameterized by Stein particles.
+Similarly to how SVGD works, Stein mixtures can approximate model posteriors by moving the Stein particles according
+to the Stein forces. Because the Stein particles parameterize a guide, they capture a neighborhood rather than a
+single point. This property means Stein mixtures significantly reduce the number of particles needed to represent
+high dimensional models.
+
+``numpyro.contrib.einstein`` mimics the interface from ``numpyro.infer.svi``, so trying SteinVI requires minimal
+change to the code for existing models inferred with SVI. For primary usage, see the
+`Bayesian neural network example <https://num.pyro.ai/en/latest/examples/stein_bnn.html>`_.
+
+The framework currently supports several kernels, including:
+
+- `RBFKernel`
+- `LinearKernel`
+- `RandomFeatureKernel`
+- `MixtureKernel`
+- `PrecondMatrixKernel`
+- `HessianPrecondMatrix`
+- `GraphicalKernel`
+
+For example, usage see:
+
+- The `Bayesian neural network example <https://num.pyro.ai/en/latest/examples/stein_bnn.html>`_
+
+SteinVI Interface
+-----------------
+.. autoclass:: numpyro.contrib.einstein.steinvi.SteinVI
+
+SteinVI Kernels
+---------------
+.. autoclass:: numpyro.contrib.einstein.kernels.RBFKernel
+.. autoclass:: numpyro.contrib.einstein.kernels.LinearKernel
+.. autoclass:: numpyro.contrib.einstein.kernels.RandomFeatureKernel
+.. autoclass:: numpyro.contrib.einstein.kernels.MixtureKernel
+.. autoclass:: numpyro.contrib.einstein.kernels.PrecondMatrixKernel
+.. autoclass:: numpyro.contrib.einstein.kernels.GraphicalKernel
+
+References
+----------
+1. *Stein's Method Meets Statistics: A Review of Some Recent Developments* (2021)
+Andreas Anastasiou, Alessandro Barp, François-Xavier Briol, Bruno Ebner,
+Robert E. Gaunt, Fatemeh Ghaderinezhad, Jackson Gorham, Arthur Gretton,
+Christophe Ley, Qiang Liu, Lester Mackey, Chris. J. Oates, Gesine Reinert,
+Yvik Swan. https://arxiv.org/abs/2105.03481
+2. *Stein variational gradient descent: A general-purpose Bayesian inference algorithm* (2016)
+Qiang Liu, Dilin Wang. NeurIPS
+3. *Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models* (2019)
+Dilin Wang, Qiang Liu. PMLR
diff --git a/numpyro/contrib/einstein/kernels.py b/numpyro/contrib/einstein/kernels.py
index 900867ae9..76fc88c25 100644
--- a/numpyro/contrib/einstein/kernels.py
+++ b/numpyro/contrib/einstein/kernels.py
@@ -196,10 +196,7 @@ def kernel(x, y):
 
 class RandomFeatureKernel(SteinKernel):
     """
-    Calculates the random kernel
-    :math:`k(x,y)= 1/m\\sum_{l=1}^{m}\\phi(x,w_l)\\phi(y,w_l),
-    from [1].
-
+    Calculates the random kernel :math:`k(x,y)= 1/m\\sum_{l=1}^{m}\\phi(x,w_l)\\phi(y,w_l)` from [1].
 
     ** References: **
     1. *Stein Variational Gradient Descent as Moment Matching* by Liu and Wang
@@ -209,7 +206,6 @@ class RandomFeatureKernel(SteinKernel):
     :param random_indices: The set of indices which to do random feature expansion on.
                            (default None, meaning all indices)
     :param bandwidth_factor: A multiplier to the bandwidth based on data size n (default 1/log(n))
-
     """
 
     def __init__(
@@ -416,9 +412,7 @@ def kernel(x, y):
 
 class GraphicalKernel(SteinKernel):
     """
-    Calculates graphical kernel
-    :math: `k(x,y) = diag({K^(l)(x,y)}_l)
-    from [1].
+    Calculates graphical kernel :math: `k(x,y) = diag({K^(l)(x,y)}_l)` from [1].
 
     ** References: **
     1. *Stein Variational Message Passing for Continuous Graphical Models* by Wang, Zheng and Liu
diff --git a/numpyro/contrib/einstein/steinvi.py b/numpyro/contrib/einstein/steinvi.py
index 78f6fc7c5..cb5be0e2a 100644
--- a/numpyro/contrib/einstein/steinvi.py
+++ b/numpyro/contrib/einstein/steinvi.py
@@ -33,8 +33,7 @@ def _numel(shape):
 
 
 class SteinVI:
-    """
-    Stein Variational Gradient Descent for Non-parametric Inference.
+    """Stein variational inference for stein mixtures.
 
     :param model: Python callable with Pyro primitives for the model.
     :param guide: Python callable with Pyro primitives for the guide
diff --git a/setup.py b/setup.py
index 03f32abe6..9babf2d3c 100644
--- a/setup.py
+++ b/setup.py
@@ -9,7 +9,7 @@
 from setuptools import find_packages, setup
 
 PROJECT_PATH = os.path.dirname(os.path.abspath(__file__))
-_jax_version_constraints = ">=0.2.13"
+_jax_version_constraints = ">=0.2.13,<0.2.28"
 _jaxlib_version_constraints = ">=0.1.65"
 
 # Find version