diff --git a/docs/communityExamples.md b/docs/communityExamples.md
index 4effc93..6252ae2 100644
--- a/docs/communityExamples.md
+++ b/docs/communityExamples.md
@@ -9,4 +9,5 @@ If you find flowMC useful, please consider contributing your example to this pag
 
 - [jim - A JAX-based gravitational-wave inference toolkit](https://github.com/kazewong/jim)
 - [Bayeux - Stitching together models and samplers](https://github.com/jax-ml/bayeux)
-  - [Colab example](https://colab.research.google.com/drive/1-PhneVVik5GUq6w2HlKOsqvus13ZLaBH?usp=sharing)
\ No newline at end of file
+  - [Colab example](https://colab.research.google.com/drive/1-PhneVVik5GUq6w2HlKOsqvus13ZLaBH?usp=sharing)
+- [Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows](https://arxiv.org/pdf/2405.14392)
\ No newline at end of file
diff --git a/setup.cfg b/setup.cfg
index bd32912..a99d077 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -15,8 +15,8 @@ packages_dir=
     =src
 packages = find:
 install_requires =
-    jax>=0.4.12
-    jaxlib>=0.4.12
+    jax>=0.4.24
+    jaxlib>=0.4.24
     equinox>=0.10.6
     optax>=0.1.5
     evosax>=0.1.4
diff --git a/src/flowMC/nfmodel/base.py b/src/flowMC/nfmodel/base.py
index 57986d0..111862d 100644
--- a/src/flowMC/nfmodel/base.py
+++ b/src/flowMC/nfmodel/base.py
@@ -191,6 +191,28 @@ def train(
 
         return rng, best_model, best_state, loss_values
 
+    def to_precision(self, precision: str = "float32"):
+        """Convert all parameters to a given precision.
+
+        !!! warning
+            This function is **experimental** and may change in the future.
+
+        Args:
+            precision (str): Precision to convert to.
+
+        Returns:
+            eqx.Module: Model with parameters converted to the given precision.
+        """
+
+        precisions_dict  = {"float16": jnp.float16, "bfloat16": jnp.bfloat16, "float32": jnp.float32, "float64": jnp.float64}
+        try:
+            precision_format = precisions_dict[precision.lower()]
+        except KeyError:
+            raise ValueError(f"Precision {precision} not supported. Choose from {precisions_dict.keys()}")
+        dynamic_model, static_model = eqx.partition(self, eqx.is_array)
+        dynamic_model = jax.tree.map(lambda x: x.astype(precision_format), dynamic_model)
+        return eqx.combine(dynamic_model, static_model)
+
 
 class Bijection(eqx.Module):
     """