diff --git a/src/peft/mapping_func.py b/src/peft/mapping_func.py
index adcb55a8e5..fae671819e 100644
--- a/src/peft/mapping_func.py
+++ b/src/peft/mapping_func.py
@@ -25,7 +25,6 @@
 from .mixed_model import PeftMixedModel
 from .peft_model import PeftModel
 from .tuners.tuners_utils import BaseTuner, BaseTunerLayer
-from .utils import _prepare_prompt_learning_config
 
 
 def get_peft_model(
@@ -120,8 +119,6 @@ def get_peft_model(
             low_cpu_mem_usage=low_cpu_mem_usage,
         )
 
-    if peft_config.is_prompt_learning:
-        peft_config = _prepare_prompt_learning_config(peft_config, model_config)
     return MODEL_TYPE_TO_PEFT_MODEL_MAPPING[peft_config.task_type](
         model,
         peft_config,
diff --git a/src/peft/utils/other.py b/src/peft/utils/other.py
index 07985e9b6f..b24858ebb9 100644
--- a/src/peft/utils/other.py
+++ b/src/peft/utils/other.py
@@ -1105,9 +1105,13 @@ def _prepare_prompt_learning_config(peft_config, model_config):
         peft_config.num_attention_heads = num_attention_heads
 
     # For grouped-query attention, see #1901.
-    if peft_config.peft_type == "PREFIX_TUNING" and "num_key_value_heads" in model_config:
+    if (peft_config.peft_type == "PREFIX_TUNING") and ("num_key_value_heads" in model_config):
         num_key_value_heads = model_config["num_key_value_heads"]
-        peft_config.token_dim = peft_config.token_dim // peft_config.num_attention_heads * num_key_value_heads
+        if model_config.get("head_dim", None) is not None:
+            head_dim = model_config["head_dim"]
+        else:
+            head_dim = peft_config.token_dim // peft_config.num_attention_heads
+        peft_config.token_dim = head_dim * num_key_value_heads
         peft_config.num_attention_heads = num_key_value_heads
 
     if getattr(peft_config, "encoder_hidden_size", None) is None:
diff --git a/tests/test_decoder_models.py b/tests/test_decoder_models.py
index 72925e2d18..cf5e882eca 100644
--- a/tests/test_decoder_models.py
+++ b/tests/test_decoder_models.py
@@ -665,15 +665,27 @@ def test_lora_layer_replication(self):
         self._test_prepare_for_training(model_id, LoraConfig, config_kwargs.copy())
         self._test_generate(model_id, LoraConfig, config_kwargs.copy())
 
-    def test_prompt_learning_with_grouped_query_attention(self):
+    def test_prefix_tuning_qwen2_with_grouped_query_attention(self):
         # See 1901, fixes a bug with handling GQA
         model_id = "peft-internal-testing/tiny-dummy-qwen2"
-        base_model = AutoModelForCausalLM.from_pretrained(model_id)
-        peft_config = PrefixTuningConfig(num_virtual_tokens=10, task_type="CAUSAL_LM")
-        model = get_peft_model(base_model, peft_config)
-        x = torch.tensor([[1, 2, 3]])
-        # does not raise
-        model(x)
+        with hub_online_once(model_id):
+            base_model = AutoModelForCausalLM.from_pretrained(model_id)
+            peft_config = PrefixTuningConfig(num_virtual_tokens=10, task_type="CAUSAL_LM")
+            model = get_peft_model(base_model, peft_config)
+            x = torch.tensor([[1, 2, 3]])
+            # does not raise
+            model(x)
+
+    def test_prefix_tuning_qwen3_with_grouped_query_attention(self):
+        # See 2881, fixes a bug with handling GQA
+        model_id = "trl-internal-testing/tiny-Qwen3ForCausalLM"
+        with hub_online_once(model_id):
+            base_model = AutoModelForCausalLM.from_pretrained(model_id)
+            peft_config = PrefixTuningConfig(num_virtual_tokens=10, task_type="CAUSAL_LM")
+            model = get_peft_model(base_model, peft_config)
+            x = torch.tensor([[1, 2, 3]])
+            # does not raise
+            model(x)
 
     def test_prefix_tuning_mistral(self):
         # See issue 869, 1962