diff --git a/src/axolotl/flash_attn.py b/src/axolotl/monkeypatch/llama_attn_hijack_flash.py
similarity index 100%
rename from src/axolotl/flash_attn.py
rename to src/axolotl/monkeypatch/llama_attn_hijack_flash.py
diff --git a/src/axolotl/utils/models.py b/src/axolotl/utils/models.py
index 2c199a16e..253bdcbd8 100644
--- a/src/axolotl/utils/models.py
+++ b/src/axolotl/utils/models.py
@@ -92,7 +92,9 @@ def load_model(
 
     if cfg.is_llama_derived_model and cfg.flash_attention:
         if cfg.device not in ["mps", "cpu"] and not cfg.inference:
-            from axolotl.flash_attn import replace_llama_attn_with_flash_attn
+            from axolotl.monkeypatch.llama_attn_hijack_flash import (
+                replace_llama_attn_with_flash_attn,
+            )
 
             LOG.info("patching with flash attention")
             replace_llama_attn_with_flash_attn()
@@ -331,6 +333,16 @@ def load_model(
             model, use_gradient_checkpointing=cfg.gradient_checkpointing
         )
 
+        # LlamaRMSNorm layers are in fp32 after kbit_training, so we need to
+        # convert them back to fp16/bf16 for flash-attn compatibility.
+        if cfg.flash_attention and cfg.is_llama_derived_model:
+            for name, module in model.named_modules():
+                if "norm" in name:
+                    module.to(torch_dtype)
+                if "lm_head" in name or "embed_tokens" in name:
+                    if hasattr(module, "weight"):
+                        module.to(torch_dtype)
+
     model, lora_config = load_adapter(model, cfg, adapter)
 
     if cfg.ddp and not load_in_8bit:
@@ -407,14 +419,6 @@ def load_llama_adapter(model, cfg):
     else:
         model = get_peft_model(model, peft_config)
 
-    if cfg.flash_attention:
-        for name, module in model.named_modules():
-            if "norm" in name:
-                module.to(torch.float16)
-            if "lm_head" in name or "embed_tokens" in name:
-                if hasattr(module, "weight"):
-                    module.to(torch.float16)
-
     model.print_trainable_parameters()
 
     return model, peft_config