diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
index 81ffc4e..6a6eb20 100644
--- a/.github/workflows/ci.yaml
+++ b/.github/workflows/ci.yaml
@@ -20,7 +20,7 @@ jobs:
       - precommit
     strategy:
       matrix:
-        python-version: [ 3.11, 3.12 ]
+        python-version: [ 3.11 ]
     steps:
       - uses: actions/checkout@v3
       - name: Set up Python ${{ matrix.python-version }}
diff --git a/README.md b/README.md
index 2a908ef..83badd3 100644
--- a/README.md
+++ b/README.md
@@ -8,12 +8,19 @@
 ## About
 
 `Blaxbird` [blækbɜːd] is a high-level API to easily build NNX models and train them on CPU or GPU.
+
 Using `blaxbird` one can
 - concisely define models and loss functions without the usual JAX/Flax verbosity,
 - easily define checkpointers that save the best and most current network weights,
 - distribute data and model weights over multiple processes or GPUs,
 - define hooks that are periodically called during training.
 
+In addition, `blaxbird` offers high-quality implementation of common neural network modules and algorithms, such as:
+
+- MLP, Diffusion Transformer,
+- Flow Matching and Denoising Score Matching (EDM schedules) with Euler and Heun samplers,
+- Consistency Distillation/Matching.
+
 ## Example
 
 To use `blaxbird`, one only needs to define a model, a loss function, and train and validation step functions:
@@ -58,7 +65,7 @@ train = train_fn(
 train(jr.key(2), model, optimizer, train_itr, val_itr)
 ```
 
-See a self-contained example in [examples/mnist_classification](examples/mnist_classification).
+See the entire self-contained example in [examples/mnist_classification](examples/mnist_classification).
 
 ## Usage
 
@@ -158,7 +165,7 @@ metrics = nnx.MultiMetric(
   accuracy=nnx.metrics.Accuracy(),
   loss=nnx.metrics.Average("loss"),
 )
-hook =  hook_fn(metrics, val_iter, eval_every_n_steps)
+hook = hook_fn(metrics, val_iter, hook_every_n_steps)
 ```
 
 This creates a hook function `hook` that after `eval_every_n_steps` steps iterates over the validation set
@@ -194,7 +201,7 @@ For instance, you would construct the checkpointing function then like this:
 from blaxbird import get_default_checkpointer
 
 hook_save, *_ = get_default_checkpointer(
-  os.path.join(outfolder, "checkpoints"), save_every_n_steps=100
+  "checkpoints", save_every_n_steps=100
 )
 ```
 
@@ -208,7 +215,7 @@ checkpoints:
 from blaxbird import get_default_checkpointer
 
 save, restore_best, restore_last = get_default_checkpointer(
-  os.path.join(outfolder, "checkpoints"), save_every_n_steps=100
+  "checkpoints", save_every_n_steps=100
 )
 ```
 
@@ -239,8 +246,8 @@ train = train_fn(
 train(jr.key(1), model, optimizer, train_itr, val_itr)
 ```
 
-An self-contained example that also explains how the data loaders should look like can be found
-in [examples/mnist_classification](examples/mnist_classification).
+Self-contained examples that also explain how the data loaders should look like can be found
+in [examples](examples).
 
 ## Installation
 
diff --git a/blaxbird/__init__.py b/blaxbird/__init__.py
index 1fbf835..7095d02 100644
--- a/blaxbird/__init__.py
+++ b/blaxbird/__init__.py
@@ -1,6 +1,6 @@
 """blaxbird: A high-level API for building and training Flax NNX models."""
 
-__version__ = "0.0.1"
+__version__ = "0.0.2"
 
 from blaxbird._src.checkpointer import get_default_checkpointer
 from blaxbird._src.trainer import train_fn
diff --git a/blaxbird/_src/checkpointer.py b/blaxbird/_src/checkpointer.py
index 8314089..64e9dd5 100644
--- a/blaxbird/_src/checkpointer.py
+++ b/blaxbird/_src/checkpointer.py
@@ -65,7 +65,7 @@ def save_fn(
       )
       checkpoint_manager.wait_until_finished()
     except Exception as e:
-      logging.error(f"could not save checkpoint because of: {e}")
+      logging.error(f"could not save better checkpoint because of: {e}")
       logging.error("resuming nonetheless")
     try:
       logging.info("saving last checkpoint")
@@ -87,15 +87,19 @@ def restore_best_fn(
     """
     graph_def, state = nnx.split(model)
     opt_def, opt_state = nnx.split(optimizer.opt_state)
-    restored = checkpoint_manager.restore(
-      checkpoint_manager.best_step(),
-      args=ocp.args.Composite(
-        state=ocp.args.StandardRestore(nnx.eval_shape(lambda: state)),
-        opt_state=ocp.args.StandardRestore(nnx.eval_shape(lambda: opt_state)),
-      ),
-    )
-    model = nnx.merge(graph_def, restored["state"])
-    optimizer.opt_state = nnx.merge(opt_def, restored["opt_state"])
+    try:
+      logging.info("trying to restore best checkpoint")
+      restored = checkpoint_manager.restore(
+        checkpoint_manager.best_step(),
+        args=ocp.args.Composite(
+          state=ocp.args.StandardRestore(nnx.eval_shape(lambda: state)),
+          opt_state=ocp.args.StandardRestore(nnx.eval_shape(lambda: opt_state)),
+        ),
+      )
+      model = nnx.merge(graph_def, restored["state"])
+      optimizer.opt_state = nnx.merge(opt_def, restored["opt_state"])
+    except FileNotFoundError:
+      logging.warning("could not find checkpoint. resuming with blank state")
     return model, optimizer
 
   def restore_last_fn(
@@ -119,9 +123,7 @@ def restore_last_fn(
       model = nnx.merge(graphdef, restored[0])
       optimizer.opt_state = nnx.merge(optdef, restored[1])
     except FileNotFoundError:
-      logging.warning(
-        "could not find last checkpoint. resuming with blank state"
-      )
+      logging.warning("could not find checkpoint. resuming with blank state")
     return model, optimizer
 
   return save_fn, restore_best_fn, restore_last_fn
diff --git a/blaxbird/_src/experimental/__init__.py b/blaxbird/_src/experimental/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/blaxbird/_src/experimental/edm.py b/blaxbird/_src/experimental/edm.py
new file mode 100644
index 0000000..5068980
--- /dev/null
+++ b/blaxbird/_src/experimental/edm.py
@@ -0,0 +1,130 @@
+import dataclasses
+
+import numpy as np
+from flax import nnx
+from jax import numpy as jnp
+from jax import random as jr
+
+from blaxbird._src.experimental import samplers
+
+
+@dataclasses.dataclass
+class EDMParameterization:
+  n_sampling_steps: int = 25
+  sigma_min: float = 0.002
+  sigma_max: float = 80.0
+  rho: float = 7.0
+  sigma_data: float = 0.5
+  P_mean: float = -1.2
+  P_std: float = 1.2
+  S_churn: float = 40
+  S_min: float = 0.05
+  S_max: float = 50
+  S_noise: float = 1.003
+
+  def sigma(self, eps):
+    return jnp.exp(eps * self.P_std + self.P_mean)
+
+  def loss_weight(self, sigma):
+    return (jnp.square(sigma) + jnp.square(self.sigma_data)) / jnp.square(
+      sigma * self.sigma_data
+    )
+
+  def skip_scaling(self, sigma):
+    return self.sigma_data**2 / (sigma**2 + self.sigma_data**2)
+
+  def out_scaling(self, sigma):
+    return sigma * self.sigma_data / (sigma**2 + self.sigma_data**2) ** 0.5
+
+  def in_scaling(self, sigma):
+    return 1 / (sigma**2 + self.sigma_data**2) ** 0.5
+
+  def noise_conditioning(self, sigma):
+    return 0.25 * jnp.log(sigma)
+
+  def sampling_sigmas(self, num_steps):
+    rho_inv = 1 / self.rho
+    step_idxs = jnp.arange(num_steps, dtype=jnp.float32)
+    sigmas = (
+      self.sigma_max**rho_inv
+      + step_idxs
+      / (num_steps - 1)
+      * (self.sigma_min**rho_inv - self.sigma_max**rho_inv)
+    ) ** self.rho
+    return jnp.concatenate([sigmas, jnp.zeros_like(sigmas[:1])])
+
+  def sigma_hat(self, sigma, num_steps):
+    gamma = (
+      jnp.minimum(self.S_churn / num_steps, 2**0.5 - 1)
+      if self.S_min <= sigma <= self.S_max
+      else 0
+    )
+    return sigma + gamma * sigma
+
+
+@dataclasses.dataclass
+class EDMConfig:
+  n_sampling_steps: int = 25
+  sampler: str = "heun"
+  parameterization: EDMParameterization = dataclasses.field(
+    default_factory=EDMParameterization
+  )
+
+
+def edm(config: EDMConfig):
+  """Construct denoising score-matching functions.
+
+  Uses the EDM parameterization.
+
+  Args:
+    config: a EDMConfig object
+
+  Returns:
+    returns a tuple consisting of train_step, val_step and sampling functions
+  """
+  parameterization = config.parameterization
+
+  def denoise(model, rng_key, inputs, sigma, context):
+    new_shape = (-1,) + tuple(np.ones(inputs.ndim - 1, dtype=np.int32).tolist())
+    inputs_t = inputs * parameterization.in_scaling(sigma).reshape(new_shape)
+    noise_cond = parameterization.noise_conditioning(sigma)
+    outputs = model(
+      inputs=inputs_t,
+      context=context,
+      times=noise_cond,
+    )
+    skip = inputs * parameterization.skip_scaling(sigma).reshape(new_shape)
+    outputs = outputs * parameterization.out_scaling(sigma).reshape(new_shape)
+    outputs = skip + outputs
+    return outputs
+
+  def loss_fn(model, rng_key, batch):
+    inputs = batch["inputs"]
+    new_shape = (-1,) + tuple(np.ones(inputs.ndim - 1, dtype=np.int32).tolist())
+
+    epsilon_key, noise_key, rng_key = jr.split(rng_key, 3)
+    epsilon = jr.normal(epsilon_key, (inputs.shape[0],))
+    sigma = parameterization.sigma(epsilon)
+
+    noise = jr.normal(noise_key, inputs.shape) * sigma.reshape(new_shape)
+    denoise_key, rng_key = jr.split(rng_key)
+    target_hat = denoise(
+      model,
+      denoise_key,
+      inputs=inputs + noise,
+      sigma=sigma,
+      context=batch.get("context"),
+    )
+
+    loss = jnp.square(inputs - target_hat)
+    loss = parameterization.loss_weight(sigma).reshape(new_shape) * loss
+    return loss.mean()
+
+  def train_step(model, rng_key, batch, **kwargs):
+    return nnx.value_and_grad(loss_fn)(model, rng_key, batch)
+
+  def val_step(model, rng_key, batch, **kwargs):
+    return loss_fn(model, rng_key, batch)
+
+  sampler = getattr(samplers, config.sampler + "sample_fn")(config)
+  return train_step, val_step, sampler
diff --git a/blaxbird/_src/experimental/nn/__init__.py b/blaxbird/_src/experimental/nn/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/blaxbird/_src/experimental/nn/dit.py b/blaxbird/_src/experimental/nn/dit.py
new file mode 100644
index 0000000..01c1824
--- /dev/null
+++ b/blaxbird/_src/experimental/nn/dit.py
@@ -0,0 +1,289 @@
+import jax
+from einops import rearrange
+from flax import nnx
+from jax import numpy as jnp
+
+from blaxbird._src.experimental.nn.embedding import timestep_embedding
+from blaxbird._src.experimental.nn.mlp import MLP
+
+
+def _modulate(inputs, shift, scale):  # noqa: ANN001, ANN202
+  return inputs * (1.0 + scale[:, None]) + shift[:, None]
+
+
+def get_sinusoidal_embedding_1d(length, embedding_dim):  # noqa: ANN001, ANN202
+  emb = timestep_embedding(length.reshape(-1), embedding_dim)
+  return emb
+
+
+def sinusoidal_init(shape, dtype):  # noqa: ANN001, ANN202
+  def get_sinusoidal_embedding_2d(grid, embedding_dim):  # noqa: ANN001, ANN202
+    emb_h = get_sinusoidal_embedding_1d(grid[0], embedding_dim // 2)
+    emb_w = get_sinusoidal_embedding_1d(grid[1], embedding_dim // 2)
+    emb = jnp.concatenate([emb_h, emb_w], axis=1)
+    return emb
+
+  _, n_h_patches, n_w_patches, embedding_dim = shape
+  grid_h = jnp.arange(n_h_patches, dtype=jnp.float32)
+  grid_w = jnp.arange(n_w_patches, dtype=jnp.float32)
+  grid = jnp.meshgrid(grid_w, grid_h)
+
+  grid = jnp.stack(grid, axis=0)
+  grid = grid.reshape([2, 1, n_w_patches, n_h_patches])
+  pos_embed = get_sinusoidal_embedding_2d(grid, embedding_dim)
+
+  return jnp.expand_dims(pos_embed, 0)  # (1, H*W, D)
+
+
+class OutProjection(nnx.Module):
+  def __init__(  # noqa: PLR0913
+    self, hidden_size, n_embedding_features, patch_size, out_channels, *, rngs
+  ):
+    super().__init__()
+    self.ada = nnx.Sequential(
+      nnx.silu, nnx.Linear(n_embedding_features, 2 * hidden_size, rngs=rngs)
+    )
+    self.norm = nnx.LayerNorm(hidden_size, rngs=rngs)
+    self.out = nnx.Linear(
+      hidden_size, patch_size * patch_size * out_channels, rngs=rngs
+    )
+
+  def __call__(self, inputs, context):
+    shift, scale = jnp.split(self.ada(context), 2, -1)
+    outs = self.out(_modulate(self.norm(inputs), shift, scale))
+    return outs
+
+
+class DiTBlock(nnx.Module):
+  def __init__(  # noqa: PLR0913
+    self,
+    hidden_size: int,
+    n_embedding_features: int,
+    *,
+    n_heads: int,
+    dropout_rate: float = 0.1,
+    rngs: nnx.rnglib.Rngs,
+  ):
+    """Diffusion-Transformer block.
+
+    Args:
+      hidden_size: number of features of the hidden layers
+      n_embedding_features: number o features of time embedding
+      n_heads: number of transformer heads
+      dropout_rate: float
+      rngs: random keys
+    """
+    super().__init__()
+    self.ada = nnx.Sequential(
+      nnx.silu, nnx.Linear(n_embedding_features, hidden_size * 6, rngs=rngs)
+    )
+
+    self.layer_norm1 = nnx.LayerNorm(
+      hidden_size, use_scale=False, use_bias=False, rngs=rngs
+    )
+    self.self_attn = nnx.MultiHeadAttention(
+      num_heads=n_heads, in_features=hidden_size, rngs=rngs, decode=False
+    )
+    self.layer_norm2 = nnx.LayerNorm(
+      hidden_size, use_scale=False, use_bias=False, rngs=rngs
+    )
+    self.mlp = MLP(
+      hidden_size,
+      (hidden_size * 4, hidden_size),
+      dropout_rate=dropout_rate,
+      rngs=rngs,
+    )
+
+  def __call__(self, inputs: jax.Array, context: jax.Array) -> jax.Array:
+    """Transform inputs through the DiT block.
+
+    Args:
+      inputs: input array
+      context: values to condition on
+
+    Returns:
+      returns a jax.Array
+    """
+    hidden = inputs
+    adaln_norm = self.ada(context)
+    attn, gate = jnp.split(adaln_norm, 2, axis=-1)
+
+    pre_shift, pre_scale, post_scale = jnp.split(attn, 3, -1)
+    intermediate = _modulate(self.layer_norm1(hidden), pre_shift, pre_scale)
+    intermediate = self.self_attn(intermediate)
+    hidden = hidden + post_scale[:, None] * intermediate
+
+    pre_shift, pre_scale, post_scale = jnp.split(gate, 3, -1)
+    intermediate = _modulate(self.layer_norm2(hidden), pre_shift, pre_scale)
+    intermediate = self.mlp(intermediate)
+    outputs = hidden + post_scale[:, None] * intermediate
+
+    return outputs
+
+
+class DiT(nnx.Module):
+  def __init__(  # noqa: PLR0913
+    self,
+    image_size: int,
+    n_hidden_channels: int,
+    patch_size: int,
+    n_layers: int,
+    n_heads: int,
+    n_embedding_features=256,
+    dropout_rate=0.0,
+    *,
+    rngs: nnx.rnglib.Rngs,
+  ):
+    """Diffusion-Transformer.
+
+    Args:
+      image_size: size of the image, e.g., (32, 32, 3)
+      n_hidden_channels: number if hidden channels
+      patch_size: size of each path
+      n_layers: integer
+      n_heads: integer
+      n_embedding_features: integer
+      dropout_rate: float
+      rngs: random keys
+    """
+    self.image_size = image_size
+    self.n_in_channels = image_size[-1]
+    self.n_embedding_features = n_embedding_features
+    self.patch_size = patch_size
+    self.time_embedding = nnx.Sequential(
+      nnx.Linear(n_embedding_features, n_embedding_features, rngs=rngs),
+      nnx.swish,
+      nnx.Linear(n_embedding_features, n_embedding_features, rngs=rngs),
+      nnx.swish,
+    )
+    self.patchify = nnx.Conv(
+      self.n_in_channels,
+      n_hidden_channels,
+      (patch_size, patch_size),
+      (patch_size, patch_size),
+      padding="VALID",
+      kernel_init=nnx.initializers.xavier_uniform(),
+      rngs=rngs,
+    )
+    self.patch_embedding = nnx.Param(
+      sinusoidal_init(
+        (
+          1,
+          image_size[0] // patch_size,
+          image_size[1] // patch_size,
+          n_hidden_channels,
+        ),
+        None,
+      ),
+    )
+    self.dit_blocks = tuple(
+      [
+        DiTBlock(
+          n_hidden_channels,
+          n_embedding_features,
+          n_heads=n_heads,
+          dropout_rate=dropout_rate,
+          rngs=rngs,
+        )
+        for _ in range(n_layers)
+      ]
+    )
+    self.out_projection = OutProjection(
+      n_hidden_channels,
+      n_embedding_features,
+      patch_size,
+      self.n_in_channels,
+      rngs=rngs,
+    )
+
+  def _patchify(self, inputs):
+    n_h_patches = self.image_size[0] // self.patch_size
+    n_w_patches = self.image_size[1] // self.patch_size
+    hidden = self.patchify(inputs)
+    outputs = rearrange(
+      hidden, "b h w c -> b (h w) c", h=n_h_patches, w=n_w_patches
+    )
+    return outputs
+
+  def _unpatchify(self, inputs):
+    H = self.image_size[0] // self.patch_size
+    W = self.image_size[1] // self.patch_size
+    P = Q = self.patch_size
+    hidden = jnp.reshape(inputs, (-1, H, W, P, Q, self.n_in_channels))
+    outputs = rearrange(
+      hidden, "b h w p q c -> b (h p) (w q) c", h=H, w=W, p=P, q=Q
+    )
+    return outputs
+
+  def _embed(self, inputs):
+    return inputs + jax.lax.stop_gradient(self.patch_embedding.value)
+
+  def __call__(
+    self, inputs: jax.Array, times: jax.Array, context: jax.Array = None
+  ):
+    """Transform inputs through the DiT.
+
+    Args:
+      inputs: input in image form
+      times: one-dimensional array
+      context: conditioning variable in image form
+
+    Returns:
+      returns a jax
+    """
+    hidden = self._patchify(inputs)
+    hidden = self._embed(hidden)
+    times = self.time_embedding(
+      timestep_embedding(times, self.n_embedding_features)
+    )
+
+    for block in self.dit_blocks:
+      hidden = block(hidden, context=times)
+
+    hidden = self.out_projection(hidden, times)
+    outputs = self._unpatchify(hidden)
+    return outputs
+
+
+def SmallDiT(image_size, patch_size=2, **kwargs):
+  return DiT(
+    image_size,
+    n_hidden_channels=384,
+    patch_size=patch_size,
+    n_layers=12,
+    n_heads=6,
+    **kwargs,
+  )
+
+
+def BaseDiT(image_size, patch_size=2, **kwargs):
+  return DiT(
+    image_size,
+    n_hidden_channels=768,
+    patch_size=patch_size,
+    n_layers=12,
+    n_heads=12,
+    **kwargs,
+  )
+
+
+def LargeDiT(image_size, patch_size=2, **kwargs):
+  return DiT(
+    image_size,
+    n_hidden_channels=1024,
+    patch_size=patch_size,
+    n_layers=24,
+    n_heads=16,
+    **kwargs,
+  )
+
+
+def XtraLargeDiT(image_size, patch_size=2, **kwargs):
+  return DiT(
+    image_size,
+    n_hidden_channels=1152,
+    patch_size=patch_size,
+    n_layers=28,
+    n_heads=16,
+    **kwargs,
+  )
diff --git a/blaxbird/_src/experimental/nn/embedding.py b/blaxbird/_src/experimental/nn/embedding.py
new file mode 100644
index 0000000..c500b99
--- /dev/null
+++ b/blaxbird/_src/experimental/nn/embedding.py
@@ -0,0 +1,9 @@
+from jax import numpy as jnp
+
+
+def timestep_embedding(timesteps, embedding_dim: int, dtype=jnp.float32):
+  half = embedding_dim // 2
+  freqs = jnp.exp(-jnp.log(10_000) * jnp.arange(0, half) / half)
+  emb = timesteps.astype(dtype)[:, None] * freqs[None, ...]
+  emb = jnp.concatenate([jnp.sin(emb), jnp.cos(emb)], axis=1)
+  return emb
diff --git a/blaxbird/_src/experimental/nn/mlp.py b/blaxbird/_src/experimental/nn/mlp.py
new file mode 100644
index 0000000..cd18af5
--- /dev/null
+++ b/blaxbird/_src/experimental/nn/mlp.py
@@ -0,0 +1,60 @@
+from collections.abc import Callable
+
+import jax
+from flax import nnx
+
+
+class MLP(nnx.Module):
+  # ruff: noqa: PLR0913, ANN204, ANN101
+  def __init__(
+    self,
+    in_features: int,
+    output_features: tuple[int, ...],
+    *,
+    kernel_init: nnx.initializers.Initializer = nnx.initializers.lecun_normal(),
+    bias_init: nnx.initializers.Initializer = nnx.initializers.zeros_init(),
+    use_bias: bool = True,
+    dropout_rate: float = None,
+    activation: Callable[[jax.Array], jax.Array] = jax.nn.silu,
+    activate_last: bool = False,
+    rngs: nnx.rnglib.Rngs,
+  ):
+    features = [in_features] + list(output_features)
+    layers = []
+    for index, (din, dout) in enumerate(zip(features[:-1], features[1:])):
+      layers.append(
+        nnx.Linear(
+          in_features=din,
+          out_features=dout,
+          kernel_init=kernel_init,
+          bias_init=bias_init,
+          use_bias=use_bias,
+          rngs=rngs,
+        )
+      )
+    self.layers = tuple(layers)
+    self.dropout_rate = dropout_rate
+    self.activate_last = activate_last
+    self.activation = activation
+    if dropout_rate is not None:
+      self.dropout_layer = nnx.Dropout(dropout_rate, rngs=rngs)
+
+  def __call__(self, inputs: jax.Array):
+    """Project inputs through the MLP.
+
+    Args:
+      inputs: jax.Array
+
+    Returns:
+      jax.Array
+    """
+    num_layers = len(self.layers)
+
+    out = inputs
+    for i, layer in enumerate(self.layers):
+      out = layer(out)
+      if i < num_layers - 1 or self.activate_last:
+        if self.dropout_rate is not None:
+          out = self.dropout_layer(out)
+        out = self.activation(out)
+    return out
diff --git a/blaxbird/_src/experimental/rfm.py b/blaxbird/_src/experimental/rfm.py
new file mode 100644
index 0000000..b7c5f18
--- /dev/null
+++ b/blaxbird/_src/experimental/rfm.py
@@ -0,0 +1,91 @@
+import dataclasses
+
+import numpy as np
+from flax import nnx
+from jax import numpy as jnp
+from jax import random as jr
+
+from blaxbird._src.experimental import samplers
+
+
+def _forward_process(inputs, times, noise):
+  new_shape = (-1,) + tuple(np.ones(inputs.ndim - 1, dtype=np.int32).tolist())
+  times = times.reshape(new_shape)
+  inputs_t = times * inputs + (1.0 - times) * noise
+  return inputs_t
+
+
+@dataclasses.dataclass
+class RFMParameterization:
+  t_eps: float = 1e-5
+  t_max: float = 1.0
+
+  def sigma(self, eps):
+    return self.t_eps + (self.t_max - self.t_eps)
+
+  def loss_weight(self, t):
+    return 1.0
+
+  def skip_scaling(self, t):
+    return 0.0
+
+  def out_scaling(self, t):
+    return 1.0
+
+  def in_scaling(self, t):
+    return 1.0
+
+  def noise_conditioning(self, t):
+    return t
+
+  def sampling_sigmas(self, num_steps):
+    return jnp.linspace(self.t_eps, self.t_max, num_steps)
+
+  def sigma_hat(self, t, num_steps):
+    return t
+
+
+@dataclasses.dataclass
+class RFMConfig:
+  n_sampling_steps: int = 25
+  sampler: str = "euler"
+  parameterization: RFMParameterization = dataclasses.field(
+    default_factory=RFMParameterization
+  )
+
+
+def rfm(config: RFMConfig = RFMConfig()):
+  """Construct rectified flow matching functions.
+
+  Args:
+    config: a FlowMatchingConfig object
+
+  Returns:
+    returns a tuple consisting of train_step, val_step and sampling functions
+  """
+  parameterization = config.parameterization
+
+  def _loss_fn(model, rng_key, batch):
+    inputs = batch["inputs"]
+    time_key, rng_key = jr.split(rng_key)
+    times = jr.uniform(time_key, shape=(inputs.shape[0],))
+    times = (
+      times * (parameterization.t_max - parameterization.t_eps)
+      + parameterization.t_eps
+    )
+    noise_key, rng_key = jr.split(rng_key)
+    noise = jr.normal(noise_key, inputs.shape)
+    inputs_t = _forward_process(inputs, times, noise)
+    vt = model(inputs=inputs_t, times=times, context=batch.get("context"))
+    ut = inputs - noise
+    loss = jnp.mean(jnp.square(ut - vt))
+    return loss
+
+  def train_step(model, rng_key, batch, **kwargs):
+    return nnx.value_and_grad(_loss_fn)(model, rng_key, batch)
+
+  def val_step(model, rng_key, batch, **kwargs):
+    return _loss_fn(model, rng_key, batch)
+
+  sampler = getattr(samplers, config.sampler + "_sample_fn")(config)
+  return train_step, val_step, sampler
diff --git a/blaxbird/_src/experimental/samplers.py b/blaxbird/_src/experimental/samplers.py
new file mode 100644
index 0000000..61246d1
--- /dev/null
+++ b/blaxbird/_src/experimental/samplers.py
@@ -0,0 +1,140 @@
+import chex
+import jax
+import numpy as np
+from flax import nnx
+from jax import numpy as jnp
+from jax import random as jr
+
+from blaxbird._src.experimental.edm import EDMConfig
+from blaxbird._src.experimental.rfm import (
+  RFMConfig,
+)
+
+
+def euler_sample_fn(config: RFMConfig):
+  """Construct an Euler sampler for flow matching.
+
+  Args:
+    config: a FlowMatchingConfig object
+
+  Returns:
+    returns a callable that can be used to sample from a flow matching model
+  """
+
+  def sample_fn(
+    model: nnx.Module,
+    rng_key: jax.Array,
+    sample_shape: tuple = (),
+    *,
+    context: jax.Array = None,
+  ) -> jax.Array:
+    """Sample from a flow matching model.
+
+    Args:
+      model: a nnx.Module that is used as the learned vector field in flow
+       matching
+      rng_key: a jax.random.key object
+      sample_shape: the shape of the data to be generated, where the first axis
+        is the batch dimension and the other axes are the feature dimensions
+      context: a conditioning variable (if used)
+
+    Returns:
+      returns a sample from the model
+    """
+    if context is not None:
+      chex.assert_equal(sample_shape[0], len(context))
+    dt = 1.0 / config.n_sampling_steps
+    samples = jr.normal(rng_key, sample_shape)
+    time_steps = config.parameterization.sampling_sigmas(
+      config.n_sampling_steps
+    )
+    for times in time_steps:
+      times = jnp.repeat(times, samples.shape[0])  # noqa: PLW2901
+      vt = model(inputs=samples, times=times, context=context)
+      samples = samples + vt * dt
+    return samples
+
+  return sample_fn
+
+
+def heun_sampler_fn(config: EDMConfig):
+  """Construct a Heun sampler for denoising score matching.
+
+  Args:
+    config: a EDMConfig object
+
+  Returns:
+    returns a callable that can be used to sample from a score matching model
+  """
+  params = config.parameterization
+
+  # ruff: noqa: ANN001, ANN202, ANN003
+  def _denoise(model, rng_key, inputs, sigma, context, params):
+    new_shape = (-1,) + tuple(np.ones(inputs.ndim - 1, dtype=np.int32).tolist())
+    inputs_t = inputs * params.in_scaling(sigma).reshape(new_shape)
+    noise_cond = params.noise_conditioning(sigma)
+    outputs = model(
+      inputs=inputs_t,
+      context=context,
+      times=noise_cond,
+    )
+    skip = inputs * params.skip_scaling(sigma).reshape(new_shape)
+    outputs = outputs * params.out_scaling(sigma).reshape(new_shape)
+    outputs = skip + outputs
+    return outputs
+
+  def sample_fn(
+    model: nnx.Module,
+    rng_key: jax.Array,
+    sample_shape: tuple = (),
+    *,
+    context: jax.Array = None,
+  ) -> jax.Array:
+    """Sample from a score matching model.
+
+    Args:
+      model: a nnx.Module that is used as the learned score model in score
+        matching
+      rng_key: a jax.random.key object
+      sample_shape: the shape of the data to be generated, where the first axis
+        is the batch dimension and the other axes are the feature dimensions
+      context: a conditioning variable (if used)
+
+    Returns:
+      returns a sample from the model
+    """
+    if context is not None:
+      chex.assert_equal(sample_shape[0], len(context))
+    n = context.shape[0]
+    noise_key, rng_key = jr.split(rng_key)
+    sigmas = params.sampling_sigmas(config.n_sampling_steps)
+    samples = jr.normal(rng_key, sample_shape) * sigmas[0]
+
+    for i, (sigma, sigma_next) in enumerate(zip(sigmas[:-1], sigmas[1:])):
+      pred_key1, pred_key2, rng_key = jr.split(rng_key, 3)
+      sample_curr = samples
+      pred_curr = _denoise(
+        model,
+        pred_key1,
+        inputs=sample_curr,
+        sigma=jnp.repeat(sigma, n),
+        context=context,
+      )
+      d_cur = (sample_curr - pred_curr) / sigma
+      samples = sample_curr + d_cur * (sigma_next - sigma)
+      # second order correction
+      if i < config.n_sampling_steps - 1:
+        pred_next = _denoise(
+          model,
+          pred_key2,
+          inputs=samples,
+          sigma=jnp.repeat(sigma_next, n),
+          context=context,
+        )
+        d_prime = (samples - pred_next) / sigma_next
+        samples = sample_curr + (sigma_next - sigma) * (
+          0.5 * d_cur + 0.5 * d_prime
+        )
+    return samples
+
+  return sample_fn
diff --git a/blaxbird/_src/trainer.py b/blaxbird/_src/trainer.py
index 483ac64..ec2771f 100644
--- a/blaxbird/_src/trainer.py
+++ b/blaxbird/_src/trainer.py
@@ -1,6 +1,5 @@
 from collections.abc import Callable, Iterable
 
-import grain.python as grain
 import jax
 import wandb
 from absl import logging
@@ -61,8 +60,8 @@ def train(
     rng_key: jax.Array,
     model: nnx.Module,
     optimizer: nnx.Optimizer,
-    train_itr: grain.DataLoader,
-    val_itr: grain.DataLoader,
+    train_itr: Iterable,
+    val_itr: Iterable,
   ) -> None:
     """Train a NNX model.
 
@@ -70,8 +69,12 @@ def train(
       rng_key: a jax.random.key object
       model: a NNX model
       optimizer: a nnx.Optimizer object
-      train_itr: a data loader
-      val_itr: a data loader
+      train_itr: an infinite data loader, i.e., an iteratlor that keeps running.
+        You can, for instance, construct this as a tfds.NumpyIterator or a
+        grain.DataLoader.
+      val_itr: an infinite data loader, i.e., an iteratlor that keeps running.
+        You can, for instance, construct this as a tfds.NumpyIterator or a
+        grain.DataLoader.
     """
     # get train and val fns
     step_fn, eval_fn = _step_and_val_fns(fns)
diff --git a/blaxbird/experimental.py b/blaxbird/experimental.py
new file mode 100644
index 0000000..73e0cfa
--- /dev/null
+++ b/blaxbird/experimental.py
@@ -0,0 +1,32 @@
+"""Experimental models that might be moved to the main code base."""
+
+from blaxbird._src.experimental.edm import EDMConfig, edm
+from blaxbird._src.experimental.nn.dit import (
+  BaseDiT,
+  DiT,
+  DiTBlock,
+  LargeDiT,
+  SmallDiT,
+  XtraLargeDiT,
+)
+from blaxbird._src.experimental.nn.mlp import MLP
+from blaxbird._src.experimental.rfm import (
+  RFMConfig,
+  rfm,
+)
+
+__all__ = [
+  "edm",
+  "EDMConfig",
+  "rfm",
+  "RFMConfig",
+  #
+  "DiT",
+  "DiTBlock",
+  "SmallDiT",
+  "BaseDiT",
+  "LargeDiT",
+  "XtraLargeDiT",
+  #
+  "MLP",
+]
diff --git a/examples/.gitignore b/examples/.gitignore
new file mode 100644
index 0000000..4dbe136
--- /dev/null
+++ b/examples/.gitignore
@@ -0,0 +1,2 @@
+workdir/
+workdir/*
diff --git a/examples/cifar10_flow_matching/dataloader.py b/examples/cifar10_flow_matching/dataloader.py
new file mode 100644
index 0000000..8c7ed93
--- /dev/null
+++ b/examples/cifar10_flow_matching/dataloader.py
@@ -0,0 +1,57 @@
+import tensorflow as tf
+import tensorflow_datasets as tfds
+from jax import numpy as jnp
+from jax import random as jr
+
+
+def data_loaders(
+  rng_key,
+  outfolder,
+  *,
+  batch_size=128,
+  buffer_size=1,
+  prefetch_size=1,
+  shuffle=True,
+  split="train",
+):
+  datasets = tfds.load(
+    "cifar10",
+    try_gcs=False,
+    split=split,
+    data_dir=outfolder,
+  )
+  if isinstance(split, str):
+    datasets = [datasets]
+  itrs = []
+  if isinstance(shuffle, bool):
+    shuffle = [shuffle]
+  assert len(datasets) == len(shuffle)
+  for dataset, shuffle_me in zip(datasets, shuffle):
+    itr_key, rng_key = jr.split(rng_key)
+    itr = as_iterable(
+      itr_key, dataset, batch_size, buffer_size, prefetch_size, shuffle_me
+    )
+    itrs.append(itr)
+  return itrs
+
+
+def as_iterable(rng_key, itr, batch_size, buffer_size, prefetch_size, shuffle):
+  def process_fn(batch):
+    img = tf.cast(batch["image"], tf.float32) / 255.0
+    img = 2.0 * img - 1.0
+    return {"inputs": img, "context": batch["label"]}
+
+  max_int32 = jnp.iinfo(jnp.int32).max
+  seed = jr.randint(rng_key, shape=(), minval=0, maxval=max_int32)
+  return (
+    itr.repeat()
+    .shuffle(
+      buffer_size,
+      reshuffle_each_iteration=shuffle,
+      seed=int(seed),
+    )
+    .map(process_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
+    .batch(batch_size, drop_remainder=True)
+    .prefetch(prefetch_size)
+    .as_numpy_iterator()
+  )
diff --git a/examples/cifar10_flow_matching/main.py b/examples/cifar10_flow_matching/main.py
new file mode 100644
index 0000000..08946ae
--- /dev/null
+++ b/examples/cifar10_flow_matching/main.py
@@ -0,0 +1,127 @@
+import argparse
+import os
+
+import jax
+import matplotlib.pyplot as plt
+import numpy as np
+import optax
+import wandb
+from absl import logging
+from flax import nnx
+from jax import random as jr
+from jax.experimental import mesh_utils
+
+import blaxbird
+import dataloader
+from blaxbird import get_default_checkpointer, train_fn
+from blaxbird.experimental import rfm
+
+
+def get_optimizer(model, lr=1e-4):
+  tx = optax.adamw(lr)
+  tx = nnx.Optimizer(model, tx=tx)
+  return tx
+
+
+def get_sharding():
+  num_devices = jax.local_device_count()
+  mesh = jax.sharding.Mesh(
+    mesh_utils.create_device_mesh((num_devices,)), ("data",)
+  )
+  model_sharding = jax.NamedSharding(mesh, jax.sharding.PartitionSpec())
+  data_sharding = jax.NamedSharding(mesh, jax.sharding.PartitionSpec("data"))
+  return model_sharding, data_sharding
+
+
+def visualize_hook(sample_fn, val_iter, hook_every_n_steps, log_to_wandb):
+  n_row, n_col, img_size = 12, 32, (32, 32, 3)
+
+  def convert_batch_to_image_grid(image_batch):
+    reshaped = (
+        image_batch.reshape(n_row, n_col, *img_size)
+        .transpose([0, 2, 1, 3, 4])
+        .reshape(n_row * img_size[0], n_col * img_size[1], img_size[2])
+    )
+    return (reshaped + 1.0) / 2.0
+
+  def plot(images):
+    fig = plt.figure(figsize=(16, 6))
+    ax = fig.add_subplot(1, 1, 1)
+    ax.imshow(
+        images,
+        interpolation="nearest",
+        cmap="gray",
+    )
+    plt.axis("off")
+    plt.tight_layout()
+    return fig
+
+  def fn(step, *, model, **kwargs):
+    if step % hook_every_n_steps != 0:
+      return
+    all_samples = []
+    for i, batch in enumerate(val_iter):
+        samples = sample_fn(model, jr.fold_in(jr.key(step), i), sample_shape=batch["inputs"].shape)
+        all_samples.append(samples)
+        if len(all_samples) * all_samples[0].shape[0] >= n_row * n_col:
+          break
+    all_samples = np.concatenate(all_samples, axis=0)[:(n_row * n_col)]
+    all_samples = convert_batch_to_image_grid(all_samples)
+    fig = plot(all_samples)
+    if jax.process_index() == 0 and log_to_wandb:
+       wandb.log({"images": wandb.Image(fig)}, step=step)
+
+  return fn
+
+
+def get_hooks(sample_fn, val_itr, hook_every_n_steps, log_to_wandb ):
+  return [visualize_hook(sample_fn, val_itr, hook_every_n_steps, log_to_wandb)]
+
+
+def get_train_and_val_itrs(rng_key, outfolder):
+  return dataloader.data_loaders(
+    rng_key, outfolder, split=["train[:90%]", "train[90%:]"], shuffle=[True, False],
+  )
+
+
+def run(n_steps, eval_every_n_steps, n_eval_batches, dit_type, log_to_wandb):
+  logging.set_verbosity(logging.INFO)
+  outfolder = os.path.join(os.path.dirname(__file__), "workdir")
+
+  train_itr, val_itr = get_train_and_val_itrs(
+    jr.key(0), os.path.join(outfolder, "data")
+  )
+
+  model = getattr(blaxbird.experimental, dit_type)(image_size=(32, 32, 3), rngs=nnx.rnglib.Rngs(jr.key(1)))
+  train_step, val_step, sample_fn = rfm()
+  optimizer = get_optimizer(model)
+
+  save_fn, _, restore_last_fn = get_default_checkpointer(
+    os.path.join(outfolder, "checkpoints"), save_every_n_steps=eval_every_n_steps
+  )
+  hooks = get_hooks(sample_fn, val_itr, eval_every_n_steps, log_to_wandb) + [save_fn]
+
+  model_sharding, data_sharding = get_sharding()
+  model, optimizer = restore_last_fn(model, optimizer)
+
+  train = train_fn(
+    fns=(train_step, val_step),
+    n_steps=n_steps,
+    eval_every_n_steps=eval_every_n_steps,
+    n_eval_batches=n_eval_batches,
+    shardings=(model_sharding, data_sharding),
+    hooks=hooks,
+    log_to_wandb=False,
+  )
+  train(jr.key(2), model, optimizer, train_itr, val_itr)
+
+
+if __name__ == "__main__":
+  parser = argparse.ArgumentParser()
+  parser.add_argument("--n-steps", type=int, default=1_000)
+  parser.add_argument("--eval-every-n-steps", type=int, default=50)
+  parser.add_argument("--n-eval-batches", type=int, default=10)
+  parser.add_argument("--dit", type=str, choices=["SmallDiT", "BaseDiT"], default="SmallDiT")
+  parser.add_argument("--log-to-wandb", action="store_true")
+  args = parser.parse_args()
+  run(args.n_steps, args.eval_every_n_steps, args.n_eval_batches, args.dit, args.log_to_wandb)
diff --git a/examples/mnist_classification/dataloader.py b/examples/mnist_classification/dataloader.py
index ebecf04..cf9d8a3 100644
--- a/examples/mnist_classification/dataloader.py
+++ b/examples/mnist_classification/dataloader.py
@@ -1,6 +1,5 @@
-import grain.python as grain
+import tensorflow as tf
 import tensorflow_datasets as tfds
-from grain._src.core.transforms import Batch
 from jax import numpy as jnp
 from jax import random as jr
 
@@ -9,53 +8,66 @@ def data_loaders(
   rng_key,
   outfolder,
   *,
-  n_epochs=100,
-  n_workers=0,
   batch_size=128,
   buffer_size=1,
+  prefetch_size=1,
   shuffle=True,
   split="train",
 ):
-  datasets = tfds.data_source(
+  datasets = tfds.load(
     "mnist",
+    try_gcs=False,
     split=split,
     data_dir=outfolder,
-    download_and_prepare_kwargs={"file_format": "parquet"},
   )
   if isinstance(split, str):
     datasets = [datasets]
   itrs = []
-  for dataset in datasets:
+  if isinstance(shuffle, bool):
+    shuffle = [shuffle]
+  assert len(datasets) == len(shuffle)
+  for dataset, shuffle_me in zip(datasets, shuffle):
     itr_key, rng_key = jr.split(rng_key)
-    itr = _as_grain_data_loader(
-      itr_key, dataset, n_epochs, n_workers, batch_size, buffer_size, shuffle
+    itr = as_iterable(
+      itr_key, dataset, batch_size, buffer_size, prefetch_size, shuffle_me
     )
     itrs.append(itr)
   return itrs
 
 
-def _as_grain_data_loader(
-  rng_key, itr, n_epochs, n_workers, batch_size, buffer_size, shuffle
-):
+def _crop_resize(image, resolution):
+  h, w = tf.shape(image)[0], tf.shape(image)[1]
+  crop = tf.minimum(h, w)
+  image = image[
+    (h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2
+  ]
+  image = tf.image.resize(
+    image,
+    size=(resolution, resolution),
+    antialias=True,
+    method=tf.image.ResizeMethod.BICUBIC,
+  )
+  return tf.cast(image, tf.float32)
+
+
+def as_iterable(rng_key, itr, batch_size, buffer_size, prefetch_size, shuffle):
+  def process_fn(batch):
+    img = tf.cast(batch["image"], tf.float32) / 255.0
+    img = _crop_resize(img, 32)
+    img = 2.0 * img - 1.0
+    return {"image": img, "label": batch["label"]}
+
   max_int32 = jnp.iinfo(jnp.int32).max
   seed = jr.randint(rng_key, shape=(), minval=0, maxval=max_int32)
-
-  index_sampler = grain.IndexSampler(
-    num_records=len(itr),
-    num_epochs=n_epochs,
-    shard_options=grain.ShardOptions(
-      shard_index=0, shard_count=1, drop_remainder=True
-    ),
-    shuffle=shuffle,
-    seed=int(seed),
-  )
-  data_loader = grain.DataLoader(
-    data_source=itr,
-    operations=[Batch(batch_size, drop_remainder=True)],
-    shard_options=grain.ShardOptions(
-      shard_index=0, shard_count=1, drop_remainder=True
-    ),
-    sampler=index_sampler,
-    worker_count=n_workers,
+  return (
+    itr.repeat()
+    .shuffle(
+      buffer_size,
+      reshuffle_each_iteration=shuffle,
+      seed=int(seed),
+    )
+    .map(process_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
+    .batch(batch_size, drop_remainder=True)
+    .prefetch(prefetch_size)
+    .as_numpy_iterator()
   )
-  return data_loader
diff --git a/examples/mnist_classification/main.py b/examples/mnist_classification/main.py
index 09d3db6..16db26a 100644
--- a/examples/mnist_classification/main.py
+++ b/examples/mnist_classification/main.py
@@ -1,3 +1,4 @@
+import argparse
 import os
 
 import dataloader
@@ -29,17 +30,18 @@ def get_sharding():
   return model_sharding, data_sharding
 
 
-def visualize_hook(val_iter, eval_every_n_steps):
-  def hook_fn(metrics, val_iter, eval_every_n_steps):
+def metrics_hook(val_iter, hook_every_n_steps):
+  def hook_fn(metrics, val_iter, hook_every_n_steps):
     def fn(step, *, model, **kwargs):
-      if step % eval_every_n_steps != 0:
+      if step % hook_every_n_steps != 0:
         return
-      batch = next(iter(val_iter))
-      logits = model(batch["image"])
-      loss = optax.softmax_cross_entropy_with_integer_labels(
-        logits=logits, labels=batch["label"]
-      ).mean()
-      metrics.update(loss=loss, logits=logits, labels=batch["label"])
+      for _, batch in zip(range(5), val_iter):
+        batch = next(iter(val_iter))
+        logits = model(batch["image"])
+        loss = optax.softmax_cross_entropy_with_integer_labels(
+          logits=logits, labels=batch["label"]
+        ).mean()
+        metrics.update(loss=loss, logits=logits, labels=batch["label"])
       if jax.process_index() == 0:
         curr_metrics = ", ".join(
           [f"{k}: {v}" for k, v in metrics.compute().items()]
@@ -53,38 +55,40 @@ def fn(step, *, model, **kwargs):
     accuracy=nnx.metrics.Accuracy(),
     loss=nnx.metrics.Average("loss"),
   )
-  return hook_fn(metrics, val_iter, eval_every_n_steps)
+  return hook_fn(metrics, val_iter, hook_every_n_steps)
 
 
-def get_hooks(val_itr, eval_every_n_steps):
-  return [visualize_hook(val_itr, eval_every_n_steps)]
+def get_hooks(val_itr, hook_every_n_steps):
+  return [metrics_hook(val_itr, hook_every_n_steps)]
 
 
 def get_train_and_val_itrs(rng_key, outfolder):
   return dataloader.data_loaders(
-    rng_key, outfolder, split=["train[:90%]", "train[90%:]"]
+    rng_key,
+    outfolder,
+    split=["train[:90%]", "train[90%:]"],
+    shuffle=[True, False],
   )
 
 
-def run():
+def run(n_steps, eval_every_n_steps, n_eval_batches):
   logging.set_verbosity(logging.INFO)
+  outfolder = os.path.join(os.path.dirname(__file__), "workdir")
 
-  outfolder = os.path.dirname(__file__)
-  n_steps, eval_every_n_steps, n_eval_batches = 10_000, 100, 20
   train_itr, val_itr = get_train_and_val_itrs(
     jr.key(0), os.path.join(outfolder, "data")
   )
 
   model = CNN(rngs=nnx.rnglib.Rngs(jr.key(1)))
   optimizer = get_optimizer(model)
-  model_sharding, data_sharding = get_sharding()
-  hooks = get_hooks(val_itr, eval_every_n_steps)
 
   save_fn, _, restore_last_fn = get_default_checkpointer(
-    os.path.join(outfolder, "checkpoints"), save_every_n_steps=100
+    os.path.join(outfolder, "checkpoints"), save_every_n_steps=eval_every_n_steps
   )
+  hooks = get_hooks(val_itr, eval_every_n_steps) + [save_fn]
+
+  model_sharding, data_sharding = get_sharding()
   model, optimizer = restore_last_fn(model, optimizer)
-  hooks.append(save_fn)
 
   train = train_fn(
     fns=(train_step, val_step),
@@ -99,4 +103,9 @@ def run():
 
 
 if __name__ == "__main__":
-  run()
+  parser = argparse.ArgumentParser()
+  parser.add_argument("--n-steps", type=int, default=1_000)
+  parser.add_argument("--eval-every-n-steps", type=int, default=50)
+  parser.add_argument("--n-eval-batches", type=int, default=10)
+  args = parser.parse_args()
+  run(args.n_steps, args.eval_every_n_steps, args.n_eval_batches)
diff --git a/examples/mnist_classification/model.py b/examples/mnist_classification/model.py
index a9ad54c..1f0d6d6 100644
--- a/examples/mnist_classification/model.py
+++ b/examples/mnist_classification/model.py
@@ -14,7 +14,7 @@ def __init__(self, *, rngs: nnx.Rngs):
     self.conv2 = nnx.Conv(32, 64, kernel_size=(3, 3), rngs=rngs)
     self.batch_norm2 = nnx.BatchNorm(64, rngs=rngs)
     self.avg_pool = partial(nnx.avg_pool, window_shape=(2, 2), strides=(2, 2))
-    self.linear1 = nnx.Linear(3136, 256, rngs=rngs)
+    self.linear1 = nnx.Linear(4096, 256, rngs=rngs)
     self.dropout2 = nnx.Dropout(rate=0.025, rngs=rngs)
     self.linear2 = nnx.Linear(256, 10, rngs=rngs)
 
diff --git a/pyproject.toml b/pyproject.toml
index 6df55f9..29e0bea 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,8 +20,7 @@ classifiers = [
 requires-python = ">=3.10"
 dependencies = [
     "einops>=0.8.1",
-    "grain",
-    "jax-ai-stack[tfds]",
+    "jax-ai-stack",
     "ml-collections>=1.1.0",
 ]
 dynamic = ["version"]
@@ -33,7 +32,6 @@ all = []
 dev = [
     "gitlint",
     "jupyter",
-    "matplotlib>=3.10.5",
     "pre-commit",
     "pytest>=7.2.0",
     "pytest-cov>=4.0.0",
@@ -41,6 +39,11 @@ dev = [
     "wandb>=0.21.1",
 ]
 examples = [
+    "tensorflow-datasets==4.9.6",
+    "tensorflow==2.17.1",
+    "protobuf==3.20.3",
+    "matplotlib==3.1.0",
+    "wandb>=0.21.1",
 ]
 
 [project.urls]
@@ -68,14 +71,14 @@ line-length = 80
 exclude = ["*_test.py", "docs/**", "examples/**"]
 
 [tool.ruff.lint]
-select = ["ANN", "D", "E", "F", "W", "I001"]
+select = ["D", "E", "F", "W", "I001"]
 extend-select = [
   "UP", "I", "PL", "S"
 ]
-ignore = ["S101"]
+ignore = ["S101", "ANN101", "PLR2044", "PLR0913"]
 
 [tool.ruff.lint.pydocstyle]
 convention= 'google'
 
 [tool.uv]
-upgrade-package = ["jax-ai-stack", "grain"]
+upgrade-package = ["jax-ai-stack"]
diff --git a/uv.lock b/uv.lock
index 37384bc..05a0246 100644
--- a/uv.lock
+++ b/uv.lock
@@ -148,16 +148,12 @@ dependencies = [
     { name = "etils", extra = ["epath"] },
 ]
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/15/a3/7b8eb838015ee555444665a6613aa28bcb818e4568f173cc582d4775616e/array_record-0.7.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:da34339e0d3c27d5758096a08eecb06140634658328f1bf835a63f7120d00e6c", size = 1798241 },
     { url = "https://files.pythonhosted.org/packages/f7/5a/b58f1a2e5752ee765ce348b435358544a99e254f36dd7e726f66f6002d94/array_record-0.7.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8d40d0039b97cd18b7360472ddb160dbb6f17bbb4aa17603828a75680876582b", size = 2135234 },
     { url = "https://files.pythonhosted.org/packages/c5/4b/e08c238bff43d804364f8162f062d63f80ba6ee4d23a381e618379543687/array_record-0.7.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5263047a14aa32a80bcb54dc6282945ffb46e137fc50cdb1827b94ad73a21c6a", size = 2195700 },
-    { url = "https://files.pythonhosted.org/packages/fd/52/389e14107613c052dc5cf73281ba47bc7c7a07a7f6ba866692be2f30bea3/array_record-0.7.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7d76c122702d5b73d9fc5f55a053d4eb88b96fd751d3473adb016cb7c2de6290", size = 1798254 },
     { url = "https://files.pythonhosted.org/packages/da/9e/df7e365bb7516b90709964bd7ca851ad03276a3b33331939bed5cb6d9377/array_record-0.7.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9426431053276e61c9c952393ff37c80825b4edc2fde32aee18b8dc0d653f19c", size = 2135376 },
     { url = "https://files.pythonhosted.org/packages/55/44/944dcbf3c398f0b4c6158d02f6fb70124353cd33bf11c66cdc6c80eb7381/array_record-0.7.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:71437d5acf00d7120dfe7fbfa067efde61947e696ca232d2ebd89646903699e3", size = 2195745 },
-    { url = "https://files.pythonhosted.org/packages/ef/77/dbf73417ad7d4bfed5b48e0ba4cf1d30c1804a42712b10211249e7eb9581/array_record-0.7.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:a833f46a30c3d68d8b6454b959ae376a62b18104259cb8184ed2ac33a7726508", size = 1799078 },
     { url = "https://files.pythonhosted.org/packages/36/f5/df0e0f0c804807bc0c46d0f9ac8d64dd27bba1a1097e8a9173ed9d2ec07d/array_record-0.7.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5e4a502d5e2b65c2d36d281b8d0a2686836e9213da600431f933beaa27702e68", size = 2135019 },
     { url = "https://files.pythonhosted.org/packages/c6/45/e563b02f3b6e312667ecdb908d69617895c368ee4c88a6934845dbc8b608/array_record-0.7.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4be715d0d8575e53b3493af6103d6852e93a535810034657a063da5b11a9fd94", size = 2195948 },
-    { url = "https://files.pythonhosted.org/packages/63/a2/d4aafa97e211639098f206ec614f57ad3dc3d6caf871bdd88a8ea9d9975a/array_record-0.7.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:d4f5e60617b222c59aae912a84c05f49ee4d1b017cdf0c9d17fed8b4063e6b64", size = 1799052 },
     { url = "https://files.pythonhosted.org/packages/28/e5/390c49785dd1d6589c9bb6a1713843f286908ca6b52ed7f4cf79da1567c9/array_record-0.7.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:67c7599dcd834467f89d945e9591dce2b2d3b538b3603258379814ae9f40e3a7", size = 2135039 },
     { url = "https://files.pythonhosted.org/packages/80/00/a1e085ff62a90658b989e004d3c3587f04955570d210d2035221a9c3468c/array_record-0.7.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0bb52fc17574fcd5c0e5d86becd6d4096fca7945a0e70e45d7c68dda80145c04", size = 2196006 },
 ]
@@ -241,12 +237,11 @@ wheels = [
 
 [[package]]
 name = "blaxbird"
-version = "0.0.1"
+version = "0.0.2"
 source = { editable = "." }
 dependencies = [
     { name = "einops" },
-    { name = "grain" },
-    { name = "jax-ai-stack", extra = ["tfds"] },
+    { name = "jax-ai-stack" },
     { name = "ml-collections" },
 ]
 
@@ -254,19 +249,24 @@ dependencies = [
 dev = [
     { name = "gitlint" },
     { name = "jupyter" },
-    { name = "matplotlib" },
     { name = "pre-commit" },
     { name = "pytest" },
     { name = "pytest-cov" },
     { name = "ruff" },
     { name = "wandb" },
 ]
+examples = [
+    { name = "matplotlib" },
+    { name = "protobuf" },
+    { name = "tensorflow" },
+    { name = "tensorflow-datasets" },
+    { name = "wandb" },
+]
 
 [package.metadata]
 requires-dist = [
     { name = "einops", specifier = ">=0.8.1" },
-    { name = "grain" },
-    { name = "jax-ai-stack", extras = ["tfds"] },
+    { name = "jax-ai-stack" },
     { name = "ml-collections", specifier = ">=1.1.0" },
 ]
 
@@ -274,14 +274,19 @@ requires-dist = [
 dev = [
     { name = "gitlint" },
     { name = "jupyter" },
-    { name = "matplotlib", specifier = ">=3.10.5" },
     { name = "pre-commit" },
     { name = "pytest", specifier = ">=7.2.0" },
     { name = "pytest-cov", specifier = ">=4.0.0" },
     { name = "ruff" },
     { name = "wandb", specifier = ">=0.21.1" },
 ]
-examples = []
+examples = [
+    { name = "matplotlib", specifier = "==3.1.0" },
+    { name = "protobuf", specifier = "==3.20.3" },
+    { name = "tensorflow", specifier = "==2.17.1" },
+    { name = "tensorflow-datasets", specifier = "==4.9.6" },
+    { name = "wandb", specifier = ">=0.21.1" },
+]
 
 [[package]]
 name = "bleach"
@@ -441,7 +446,7 @@ wheels = [
 
 [[package]]
 name = "chex"
-version = "0.1.89"
+version = "0.1.88"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "absl-py" },
@@ -452,9 +457,9 @@ dependencies = [
     { name = "toolz" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/ca/ac/504a8019f7ef372fc6cc3999ec9e3d0fbb38e6992f55d845d5b928010c11/chex-0.1.89.tar.gz", hash = "sha256:78f856e6a0a8459edfcbb402c2c044d2b8102eac4b633838cbdfdcdb09c6c8e0", size = 90676 }
+sdist = { url = "https://files.pythonhosted.org/packages/ff/7a/3b8ca1c693a5ab0a03e48837c6da8659a0dd9f742248d69c4d8beae13d27/chex-0.1.88.tar.gz", hash = "sha256:565de897b1373232cdfca5e699f50fa49403d2c7d23f6c5a75a97ef713d2fe36", size = 90366 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/5e/6c/309972937d931069816dc8b28193a650485bc35cca92c04c8c15c4bd181e/chex-0.1.89-py3-none-any.whl", hash = "sha256:145241c27d8944adb634fb7d472a460e1c1b643f561507d4031ad5156ef82dfa", size = 99908 },
+    { url = "https://files.pythonhosted.org/packages/58/a0/e4bebe76bdd0a68077030f1b5e48b545597473ae1b773c84150311152efc/chex-0.1.88-py3-none-any.whl", hash = "sha256:234b61a5baa8132802e4b9c5657167d6c8a911d90a59a0bec47d537567e41b75", size = 99684 },
 ]
 
 [[package]]
@@ -469,15 +474,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c2/f1/df59e28c642d583f7dacffb1e0965d0e00b218e0186d7858ac5233dce840/click-8.1.3-py3-none-any.whl", hash = "sha256:bb4d8133cb15a609f44e8213d9b391b0809795062913b383c62be0ee95b1db48", size = 96588 },
 ]
 
-[[package]]
-name = "cloudpickle"
-version = "3.1.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/52/39/069100b84d7418bc358d81669d5748efb14b9cceacd2f9c75f550424132f/cloudpickle-3.1.1.tar.gz", hash = "sha256:b216fa8ae4019d5482a8ac3c95d8f6346115d8835911fd4aefd1a445e4242c64", size = 22113 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/7e/e8/64c37fadfc2816a7701fa8a6ed8d87327c7d54eacfbfb6edab14a2f2be75/cloudpickle-3.1.1-py3-none-any.whl", hash = "sha256:c8c5a44295039331ee9dad40ba100a9c7297b6f988e50e87ccdf3765a668350e", size = 20992 },
-]
-
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -496,73 +492,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/60/97/891a0971e1e4a8c5d2b20bbe0e524dc04548d2307fee33cdeba148fd4fc7/comm-0.2.3-py3-none-any.whl", hash = "sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417", size = 7294 },
 ]
 
-[[package]]
-name = "contourpy"
-version = "1.3.2"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "numpy" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/66/54/eb9bfc647b19f2009dd5c7f5ec51c4e6ca831725f1aea7a993034f483147/contourpy-1.3.2.tar.gz", hash = "sha256:b6945942715a034c671b7fc54f9588126b0b8bf23db2696e3ca8328f3ff0ab54", size = 13466130 }
-wheels = [
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