diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 00059bd01..e9bb4b20b 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -2910,7 +2910,6 @@ class JambaModel(Model):
             n_kv_head if (i - attn_offset) % attn_period == 0 else 0 for i in range(attn_offset, self.block_count)
         ]
 
-        self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_block_count(self.block_count)
         self.gguf_writer.add_context_length(self.find_hparam(["max_position_embeddings", "n_ctx"]))
         self.gguf_writer.add_embedding_length(d_model)
@@ -2979,8 +2978,8 @@ class JambaModel(Model):
 
         yield new_name, data_torch
 
-    def write_tensors(self):
-        super().write_tensors()
+    def prepare_tensors(self):
+        super().prepare_tensors()
 
         if self._experts is not None:
             # flatten `list[dict[str, Tensor]]` into `list[str]`
@@ -2988,20 +2987,6 @@ class JambaModel(Model):
             if len(experts) > 0:
                 raise ValueError(f"Unprocessed experts: {experts}")
 
-    # same as Mamba
-    def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
-        del n_dims  # unused
-
-        return bid is not None and new_name in (
-            self.format_tensor_name(n, bid, ".weight" if name.endswith(".weight") else "") for n in [
-                gguf.MODEL_TENSOR.SSM_CONV1D,
-                gguf.MODEL_TENSOR.SSM_X,
-                gguf.MODEL_TENSOR.SSM_DT,
-                gguf.MODEL_TENSOR.SSM_A,
-                gguf.MODEL_TENSOR.SSM_D,
-            ]
-        )
-
 
 @Model.register("CohereForCausalLM")
 class CommandR2Model(Model):