diff --git a/linear_relational/lib/torch_utils.py b/linear_relational/lib/torch_utils.py
index a1a4eeb..43049d7 100644
--- a/linear_relational/lib/torch_utils.py
+++ b/linear_relational/lib/torch_utils.py
@@ -18,6 +18,13 @@ def get_module(model: nn.Module, name: str) -> nn.Module:
     raise LookupError(name)
 
 
+def get_dtype(model: nn.Module) -> torch.dtype:
+    """
+    Returns the dtype of the model.
+    """
+    return next(model.parameters()).dtype
+
+
 def get_device(model: nn.Module) -> torch.device:
     """
     Returns the device on which the model is running.
diff --git a/linear_relational/training/train_lre.py b/linear_relational/training/train_lre.py
index 6268fa6..9be23fa 100644
--- a/linear_relational/training/train_lre.py
+++ b/linear_relational/training/train_lre.py
@@ -15,7 +15,11 @@
     find_final_word_token_index,
     find_prompt_answer_data,
 )
-from linear_relational.lib.torch_utils import get_device, untuple_tensor
+from linear_relational.lib.torch_utils import (
+    get_device,
+    get_dtype,
+    untuple_tensor,
+)
 from linear_relational.lib.TraceLayer import TraceLayer
 from linear_relational.lib.TraceLayerDict import TraceLayerDict
 from linear_relational.Lre import Lre
@@ -112,7 +116,12 @@ def order_1_approx(
         hasattr(model, "config")
         and getattr(model.config, "cache_implementation", None) == "hybrid"
     ):
-        cache = HybridCache(model.config, input_ids.shape[0], input_ids.shape[1] + 1)
+        cache = HybridCache(
+            model.config,
+            input_ids.shape[0],
+            input_ids.shape[1] + 1,
+            dtype=get_dtype(model),
+        )
         cache_position = torch.arange(input_ids.shape[1], device=device)
         precache_extra_params["past_key_values"] = cache
         precache_extra_params["cache_position"] = cache_position[:subject_index]
diff --git a/tests/conftest.py b/tests/conftest.py
index a4792ad..dcb4a35 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -22,8 +22,8 @@ def model() -> GPT2LMHeadModel:
 def empty_gemma2_model() -> Gemma2ForCausalLM:
     config = Gemma2Config(
         num_hidden_layers=3,
-        hidden_size=1024,
-        intermediate_size=2752,
+        hidden_size=64,
+        intermediate_size=128,
         vocab_size=_tokenizer.vocab_size,
     )
     return Gemma2ForCausalLM(config).eval()
diff --git a/tests/training/test_train_lre.py b/tests/training/test_train_lre.py
index 2b3d3df..23b5b58 100644
--- a/tests/training/test_train_lre.py
+++ b/tests/training/test_train_lre.py
@@ -138,6 +138,43 @@ def test_train_lre_on_single_prompt_with_gemma2_perfectly_replicates_object(
         texts=[prompt.text],
         layers=["model.layers.2"],
     )[0]["model.layers.2"]
-    print(lre(subj_act))
-    print(obj_act)
+    assert torch.allclose(lre(subj_act), obj_act, atol=1e-4)
+
+
+def test_train_lre_works_with_gemma2_and_float16(
+    empty_gemma2_model: PreTrainedModel, tokenizer: GPT2TokenizerFast
+) -> None:
+    model = empty_gemma2_model.half()
+    prompt = create_prompt(
+        text="Tokyo is located in the country of",
+        answer="Japan",
+        subject="Tokyo",
+    )
+    lre = train_lre(
+        model=model,
+        tokenizer=tokenizer,
+        layer_matcher="model.layers.{num}",
+        relation="city in country",
+        subject_layer=1,
+        object_layer=2,
+        prompts=[prompt],
+    ).float()
+
+    subj_index = (
+        find_token_range(tokenizer, tokenizer.encode(prompt.text), prompt.subject)[-1]
+        - 1
+    )
+    subj_act = extract_token_activations(
+        model=model,
+        tokenizer=tokenizer,
+        texts=[prompt.text],
+        layers=["model.layers.1"],
+        token_indices=[subj_index],
+    )[0]["model.layers.1"][0]
+    obj_act = extract_final_token_activations(
+        model=model,
+        tokenizer=tokenizer,
+        texts=[prompt.text],
+        layers=["model.layers.2"],
+    )[0]["model.layers.2"]
     assert torch.allclose(lre(subj_act), obj_act, atol=1e-4)