diff --git a/src/axolotl/cli/cloud/modal_.py b/src/axolotl/cli/cloud/modal_.py
index 0509cba69..6d4f999b4 100644
--- a/src/axolotl/cli/cloud/modal_.py
+++ b/src/axolotl/cli/cloud/modal_.py
@@ -200,7 +200,7 @@ class ModalCloud(Cloud):
         if family in ["a10", "a10g"]:
             return modal.gpu.A10G(count=count)
         if family == "h100":
-            return modal.gpu.H100(count=count)
+            return f"H100:{count}"
         if family == "t4":
             return modal.gpu.T4(count=count)
         if family == "l4":
diff --git a/src/axolotl/monkeypatch/accelerate/fsdp2.py b/src/axolotl/monkeypatch/accelerate/fsdp2.py
index efc388294..66d3d0d2d 100644
--- a/src/axolotl/monkeypatch/accelerate/fsdp2.py
+++ b/src/axolotl/monkeypatch/accelerate/fsdp2.py
@@ -187,7 +187,7 @@ def _process_lora_module_for_fsdp(module, fsdp2_kwargs):
 
     # Linear4Bit will keep it's bias term in fp32. If the weight dtype is in bf16 we are not able to
     # wrap this. Therefore we must ensure the bias has the same dtype as the weight
-    if module.base_layer.bias is not None:
+    if hasattr(module.base_layer, "bias") and module.base_layer.bias is not None:
         if module.base_layer.weight.dtype != module.base_layer.bias.dtype:
             log_bias_dtype_mismatch = True
             module.base_layer.bias.data = module.base_layer.bias.data.to(
diff --git a/src/axolotl/train.py b/src/axolotl/train.py
index 8005389f1..dd39cc228 100644
--- a/src/axolotl/train.py
+++ b/src/axolotl/train.py
@@ -253,7 +253,9 @@ def save_trained_model(
             # final model weights have already been saved by `ReLoRACallback.on_train_end`
             return
 
-    if trainer.is_fsdp_enabled or cfg.fsdp_config:
+    if (  # pylint: disable=too-many-nested-blocks
+        trainer.is_fsdp_enabled or cfg.fsdp_config
+    ):
         if cfg.fsdp_config or cfg.fsdp:
             if cfg.fsdp_config.final_state_dict_type:
                 state_dict_type = cfg.fsdp_config.final_state_dict_type
@@ -285,6 +287,8 @@ def save_trained_model(
                 if trainer.accelerator.is_main_process:
                     # move all files in merged_path to cfg.output_dir
                     for merged_file in Path(merged_path).iterdir():
+                        if (Path(cfg.output_dir) / merged_file.name).exists():
+                            (Path(cfg.output_dir) / merged_file.name).unlink()
                         shutil.move(str(merged_file), cfg.output_dir)
                     shutil.rmtree(merged_path)  # remove what should be an empty dir
         # TODO(wing):see https://github.com/huggingface/transformers/pull/40207