diff --git a/.gitignore b/.gitignore
index f0a15d2..b8fa4b7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,6 @@
 # Compiled python modules.
 *.pyc
+venv
 
 # Byte-compiled
 _pycache__/
diff --git a/gemma/gm/text/_chat_sampler.py b/gemma/gm/text/_chat_sampler.py
index bed39b2..4a42db9 100644
--- a/gemma/gm/text/_chat_sampler.py
+++ b/gemma/gm/text/_chat_sampler.py
@@ -28,6 +28,27 @@
 from gemma.gm.text import _tokenizer
 # from gemma.gm.vision import _token_utils
 from kauldron.typing import PRNGKeyLike, UInt8  # pylint: disable=g-multiple-import,g-importing-member
+import jax.numpy as jnp
+
+def resize_tensor(cache_tensor, new_length):
+    """
+    Resize a cache tensor from shape (1, old_length, 1, 256) to (1, new_length, 1, 256).
+    If new_length > old_length, pad with zeros.
+    If new_length < old_length, truncate.
+    """
+    cache_tensor = jnp.asarray(cache_tensor)  # Ensure it's a JAX array
+    old_length = cache_tensor.shape[1]
+
+    if new_length > old_length:
+        # Padding needed (pad along the second dimension)
+        pad_width = [(0, 0), (0, new_length - old_length), (0, 0), (0, 0)]
+        return jnp.pad(cache_tensor, pad_width, mode='constant', constant_values=0)
+
+    elif new_length < old_length:
+        # Truncate (keep the first `new_length` values)
+        return cache_tensor[:, :new_length, :, :]
+
+    return cache_tensor  # If sizes are the same, return as is
 
 
 @dataclasses.dataclass(frozen=True, kw_only=True, eq=False)
@@ -119,6 +140,37 @@ def sampler(self) -> _sampler.Sampler:
         forbidden_tokens=self.forbidden_tokens,
         cache_length=self.cache_length,
     )
+  def resize_cache(self, new_length: int):
+    """Update the cache length dynamically"""
+    object.__setattr__(self, 'cache_length', new_length)
+
+    # Update the last_state with the new cache size if last_state is not None
+    if self.last_state is not None:
+        # Initialize a new cache with the updated size
+        updated_cache = self.model.init_cache(
+            batch_size=len(self.last_state.cache),
+            dtype=self._dtype,
+            cache_length=new_length,
+        )
+
+        # Resize the existing cache data and copy it into the new cache
+        for layer in self.last_state.cache.keys():
+            updated_cache[layer]["v"] = resize_tensor(self.last_state.cache[layer]["v"], new_length)
+            updated_cache[layer]["k"] = resize_tensor(self.last_state.cache[layer]["k"], new_length)
+
+        # Create a new SamplingState with the updated cache
+        updated_last_state = _sampler_call.SamplingState(
+            cache=updated_cache,
+            predicted_tokens=self.last_state.predicted_tokens,
+            used_cache_length=min(self.last_state.used_cache_length, new_length),
+            # Add any other fields that are part of SamplingState
+        )
+        object.__setattr__(self, 'last_state', updated_last_state)
+
+    # Invalidate the cached sampler so it gets recomputed
+    if 'sampler' in self.__dict__:
+        del self.__dict__['sampler']
+
 
   def chat(
       self,