diff --git a/src/frontends/pytorch/src/op/addmm.cpp b/src/frontends/pytorch/src/op/addmm.cpp
index 4ecfd403afc6dd..a61898aadbdbdb 100644
--- a/src/frontends/pytorch/src/op/addmm.cpp
+++ b/src/frontends/pytorch/src/op/addmm.cpp
@@ -73,17 +73,8 @@ OutputVector translate_conv1d_ext(const NodeContext& context) {
     auto bias = context.get_input(2);
     bias = context.mark_node(std::make_shared<ov::op::v1::ConvertLike>(bias, x));
 
-    auto neg_one = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {-1}));
-    auto zero = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {0}));
-    auto shape_x = context.mark_node(std::make_shared<v3::ShapeOf>(x, element::i32));
-    auto x_last_dim = context.mark_node(std::make_shared<v8::Gather>(shape_x, neg_one, zero));
-    auto x_new_shape = context.mark_node(std::make_shared<v0::Concat>(OutputVector{neg_one, x_last_dim}, 0));
-
-    auto x_new = context.mark_node(std::make_shared<v1::Reshape>(x, x_new_shape, false));
-    auto mm = context.mark_node(std::make_shared<v0::MatMul>(x_new, weight));
-    auto addmm = context.mark_node(std::make_shared<v1::Add>(bias, mm));
-    auto size_out = context.mark_node(std::make_shared<v12::ScatterElementsUpdate>(shape_x, neg_one, neg_one, zero));
-    return {context.mark_node(std::make_shared<v1::Reshape>(addmm, size_out, false))};
+    auto mm = context.mark_node(std::make_shared<v0::MatMul>(x, weight));
+    return {context.mark_node(std::make_shared<v1::Add>(mm, bias))};
 };
 
 }  // namespace op
diff --git a/tests/model_hub_tests/pytorch/test_llm.py b/tests/model_hub_tests/pytorch/test_llm.py
index d48ac60e24db71..9acf8e2100c520 100644
--- a/tests/model_hub_tests/pytorch/test_llm.py
+++ b/tests/model_hub_tests/pytorch/test_llm.py
@@ -100,13 +100,16 @@ def load_model(self, name, type):
             config = {}
         model_kwargs = {"torchscript": True, "trust_remote_code": True}
         is_gptq = is_gptq_model(config)
+        is_gpt2 = name == "openai-community/gpt2"
+
         if is_gptq:
             self.cuda_available, self.gptq_postinit = patch_gptq()
             model_kwargs["torch_dtype"] = torch.float32
             self.ov_config = {"DYNAMIC_QUANTIZATION_GROUP_SIZE": "0"}
+        elif is_gpt2:
+            model_kwargs["torch_dtype"] = torch.float16
         else:
             model_kwargs["torch_dtype"] = "auto"
-            pass
 
         t = AutoTokenizer.from_pretrained(name, trust_remote_code=True)
         self.model = AutoModelForCausalLM.from_pretrained(name, **model_kwargs)
@@ -114,7 +117,7 @@ def load_model(self, name, type):
             model = self.model
         else:
             assert self.model.config.torch_dtype in [
-                torch.float16, torch.bfloat16]
+                torch.float16, torch.bfloat16] or is_gpt2
             model = copy.deepcopy(self.model).float()
 
         example = t("Some input text to verify that model works.",
@@ -188,6 +191,7 @@ def get_pkv(model, tokenizer):
     @pytest.mark.parametrize("type,name", [
         ("opt_gptq", "katuni4ka/opt-125m-gptq"),
         ("llama", "TinyLlama/TinyLlama-1.1B-Chat-v1.0"),
+        ("gpt2", "openai-community/gpt2")
     ])
     @pytest.mark.precommit
     @pytest.mark.nightly