diff --git a/llama/README.md b/llama/README.md
index 1314ca86..4ac4aa84 100644
--- a/llama/README.md
+++ b/llama/README.md
@@ -29,7 +29,7 @@ Once you've converted the weights to MLX format, you can interact with the
 LLaMA model:
 
 ```
-python llama.py mlx_llama.npz tokenizer.model "hello"
+python llama.py mlx_llama_weights.npz <path_to_tokenizer.model> "hello"
 ```
 
 Run `python llama.py --help` for more details.
diff --git a/llama/convert.py b/llama/convert.py
index 153e056f..9e30a8d4 100644
--- a/llama/convert.py
+++ b/llama/convert.py
@@ -12,7 +12,8 @@ def map_torch_to_mlx(key, value):
         key = "embedding.weight"
 
     elif "norm" in key:
-        key = key.replace("attention_norm", "norm1").replace("ffn_norm", "norm2")
+        key = key.replace("attention_norm", "norm1").replace(
+            "ffn_norm", "norm2")
 
     elif "wq" in key or "wk" in key or "wv" in key or "wo" in key:
         key = key.replace("wq", "query_proj")
@@ -32,11 +33,12 @@ def map_torch_to_mlx(key, value):
     elif "rope" in key:
         return None, None
 
-    return key, value.numpy()
+    return key, value.numpy() if value.dtype != torch.bfloat16 else value.to(torch.float32).numpy()
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Convert Llama weights to MLX")
+    parser = argparse.ArgumentParser(
+        description="Convert Llama weights to MLX")
     parser.add_argument("torch_weights")
     parser.add_argument("output_file")
     args = parser.parse_args()