diff --git a/llms/mlx_lm/utils.py b/llms/mlx_lm/utils.py
index 5621609d..2d93fe84 100644
--- a/llms/mlx_lm/utils.py
+++ b/llms/mlx_lm/utils.py
@@ -15,7 +15,7 @@ import mlx.core as mx
 import mlx.nn as nn
 from huggingface_hub import snapshot_download
 from mlx.utils import tree_flatten
-from transformers import PreTrainedTokenizer
+from transformers import PreTrainedTokenizer, AddedToken
 
 # Local imports
 from .models.base import KVCache, RotatingKVCache
@@ -309,6 +309,12 @@ def stream_generate(
     yield detokenizer.last_segment
 
 
+class TokenizerConfigEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, AddedToken):
+            return str(obj)
+        return super().default(obj)
+
 def generate(
     model: nn.Module,
     tokenizer: Union[PreTrainedTokenizer, TokenizerWrapper],
@@ -316,6 +322,7 @@ def generate(
     max_tokens: int = 100,
     verbose: bool = False,
     formatter: Optional[Callable] = None,
+    save_cache_path: Optional[str] = None,
     **kwargs,
 ) -> Union[str, Generator[str, None, None]]:
     """
@@ -330,6 +337,7 @@ def generate(
            Default: ``False``.
        formatter (Optional[Callable]): A function which takes a token and a
            probability and displays it.
+       save_cache_path (Optional[str]): If provided, save the final KV cache to this path.
        kwargs: The remaining options get passed to :func:`generate_step`.
           See :func:`generate_step` for more details.
     """
@@ -346,10 +354,11 @@ def generate(
     tic = time.perf_counter()
     detokenizer.reset()
 
-    for (token, logprobs), n in zip(
-        generate_step(prompt_tokens, model, **kwargs),
-        range(max_tokens),
-    ):
+    # Get the generate_step generator
+    gen_step = generate_step(prompt_tokens, model, **kwargs)
+
+    # Actual generation loop is here
+    for (token, logprobs), n in zip(gen_step, range(max_tokens)):
         if n == 0:
             prompt_time = time.perf_counter() - tic
             tic = time.perf_counter()
@@ -359,7 +368,6 @@ def generate(
 
         if verbose:
             if formatter:
-                # We have to finalize so that the prob corresponds to the last segment
                 detokenizer.finalize()
                 formatter(detokenizer.last_segment, mx.exp(logprobs[token]).item())
             else:
@@ -382,6 +390,32 @@ def generate(
         peak_mem = mx.metal.get_peak_memory() / 2**30
         print(f"Peak memory: {peak_mem:.3f} GB")
 
+    # Save the final KV cache if requested
+    if save_cache_path:
+        # Extract the final cache state
+        cache = gen_step.gi_frame.f_locals['cache']
+        
+        # Prepare the cache data
+        cache_dict = {}
+        for i, c in enumerate(cache):
+            cache_dict[f"{i}_keys"] = c.state[0][..., :c.offset, :]
+            cache_dict[f"{i}_values"] = c.state[1][..., :c.offset, :]
+        
+        # Prepare metadata
+        metadata = {
+            "model": model.__class__.__name__,
+            "chat_template": str(getattr(tokenizer, "chat_template", None)),
+            "tokenizer_config": json.dumps({
+                k: (str(v) if isinstance(v, AddedToken) else v)
+                for k, v in tokenizer.init_kwargs.items()
+            }),
+            "max_kv_size": str(kwargs.get("max_kv_size", "None")),
+        }
+        
+        # Save to safetensors file
+        mx.save_safetensors(save_cache_path, cache_dict, metadata=metadata)
+        print(f"Final KV cache saved to {save_cache_path}")
+
     return detokenizer.text