diff --git a/examples/qwen2/qlora-fsdp.yaml b/examples/qwen2/qlora-fsdp.yaml
index 44f9c7e49..d61c72a37 100644
--- a/examples/qwen2/qlora-fsdp.yaml
+++ b/examples/qwen2/qlora-fsdp.yaml
@@ -72,4 +72,5 @@ fsdp_config:
   fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
   fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
   fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD
 special_tokens:
diff --git a/src/axolotl/core/trainer_builder.py b/src/axolotl/core/trainer_builder.py
index 4e8b36905..1a073ca04 100755
--- a/src/axolotl/core/trainer_builder.py
+++ b/src/axolotl/core/trainer_builder.py
@@ -1846,6 +1846,8 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         )
         if self.cfg.fsdp:
             ensure_dtype(dpo_trainer.model, dtype=self.cfg.torch_dtype)
+            if self.cfg.rl in ["dpo", "ipo"] and dpo_trainer.ref_model:
+                ensure_dtype(dpo_trainer.ref_model, dtype=self.cfg.torch_dtype)
 
         dpo_trainer = self.hook_post_create_trainer(dpo_trainer)
         for callback in self.get_post_trainer_create_callbacks(dpo_trainer):
diff --git a/src/axolotl/utils/models.py b/src/axolotl/utils/models.py
index 3e8d50f5e..4f47d59bf 100644
--- a/src/axolotl/utils/models.py
+++ b/src/axolotl/utils/models.py
@@ -1102,9 +1102,20 @@ def load_lora(model, cfg, inference=False, config_only=False):
 
 def ensure_dtype(model, dtype=torch.bfloat16):
     for name, module in model.named_modules():
+        weight_mismatch = False
+        bias_mismatch = False
         try:
-            if module.weight.dtype != dtype:
-                print(f"Converting module {name}: {module.weight.dtype} -> {dtype}")
-                module.to(dtype)
+            weight_mismatch = module.weight.dtype != dtype
         except AttributeError:
             pass
+        try:
+            bias_mismatch = module.bias.dtype != dtype
+        except AttributeError:
+            pass
+
+        if weight_mismatch:
+            print(f"Converting module {name}.weight: {module.weight.dtype} -> {dtype}")
+        if bias_mismatch:
+            print(f"Converting module {name}.bias: {module.bias.dtype} -> {dtype}")
+        if weight_mismatch or bias_mismatch:
+            module.to(dtype)
diff --git a/tests/e2e/multigpu/test_qwen2.py b/tests/e2e/multigpu/test_qwen2.py
new file mode 100644
index 000000000..2513be69e
--- /dev/null
+++ b/tests/e2e/multigpu/test_qwen2.py
@@ -0,0 +1,98 @@
+"""
+E2E tests for multigpu qwen2
+"""
+
+import logging
+import os
+import unittest
+from pathlib import Path
+
+import yaml
+from accelerate.test_utils import execute_subprocess_async
+
+from axolotl.utils.dict import DictDefault
+
+from ..utils import with_temp_dir
+
+LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestMultiGPUQwen2(unittest.TestCase):
+    """
+    Test case for Llama models using LoRA
+    """
+
+    @with_temp_dir
+    def test_qlora_fsdp_dpo(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "Qwen/Qwen2-1.5B",
+                "load_in_4bit": True,
+                "rl": "dpo",
+                "chat_template": "chatml",
+                "sequence_len": 2048,
+                "adapter": "qlora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.05,
+                "datasets": [
+                    {
+                        "path": "Intel/orca_dpo_pairs",
+                        "split": "train",
+                        "type": "chatml.intel",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 100,
+                "warmup_steps": 20,
+                "micro_batch_size": 4,
+                "gradient_accumulation_steps": 2,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "bf16": "auto",
+                "tf32": True,
+                "gradient_checkpointing": True,
+                "gradient_checkpointing_kwargs": {
+                    "use_reentrant": False,
+                },
+                "fsdp": [
+                    "full_shard",
+                    "auto_wrap",
+                ],
+                "fsdp_config": {
+                    "fsdp_limit_all_gathers": True,
+                    "fsdp_offload_params": False,
+                    "fsdp_sync_module_states": True,
+                    "fsdp_use_orig_params": False,
+                    "fsdp_cpu_ram_efficient_loading": False,
+                    "fsdp_transformer_layer_cls_to_wrap": "Qwen2DecoderLayer",
+                    "fsdp_state_dict_type": "FULL_STATE_DICT",
+                    "fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
+                    "fsdp_sharding_strategy": "FULL_SHARD",
+                },
+            }
+        )
+
+        # write cfg to yaml file
+        Path(temp_dir).mkdir(parents=True, exist_ok=True)
+        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+        execute_subprocess_async(
+            [
+                "accelerate",
+                "launch",
+                "--num-processes",
+                "2",
+                "-m",
+                "axolotl.cli.train",
+                str(Path(temp_dir) / "config.yaml"),
+            ]
+        )