diff --git a/tests/test_hf_generation/test_generation.py b/tests/test_hf_generation/test_generation.py
index de6d244..86698a5 100644
--- a/tests/test_hf_generation/test_generation.py
+++ b/tests/test_hf_generation/test_generation.py
@@ -6,7 +6,7 @@
 MODEL_IDS = [
     "hf-internal-testing/tiny-random-GPTJForCausalLM",
     "JackFram/llama-68m",
-    # "hf-internal-testing/tiny-random-PhiForCausalLM",
+    "hf-internal-testing/tiny-random-PhiForCausalLM",
     "hf-internal-testing/tiny-random-gpt2",
     # "hf-internal-testing/tiny-random-BlenderbotForCausalLM",
 ]
diff --git a/tests/test_hf_generation/test_generation_w_expanded_emb.py b/tests/test_hf_generation/test_generation_w_expanded_emb.py
new file mode 100644
index 0000000..1b8fb5a
--- /dev/null
+++ b/tests/test_hf_generation/test_generation_w_expanded_emb.py
@@ -0,0 +1,121 @@
+from unittest import TestCase
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers_cfg.token_grammar_recognizer import IncrementalTokenRecognizer
+from transformers_cfg.generation.logits_process import GrammarConstrainedLogitsProcessor
+
+MODEL_IDS = [
+    "JackFram/llama-68m",
+]
+
+
+def check_parentheses(generation):
+    stack = []
+    for char in generation:
+        if char == "(":
+            stack.append(char)
+        elif char == ")":
+            if not stack:
+                return False
+            stack.pop()
+    return not stack
+
+
+class TestGreedyDecoding(TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.models = {}
+        cls.tokenizers = {}
+        for model_id in MODEL_IDS:
+            cls.models[model_id] = AutoModelForCausalLM.from_pretrained(model_id)
+            cls.tokenizers[model_id] = AutoTokenizer.from_pretrained(model_id)
+            cls.tokenizers[model_id].pad_token = cls.tokenizers[model_id].eos_token
+            # we expand the embedding layer to simulate the case where the model has a larger embedding layer than the tokenizer
+            cls.models[model_id].resize_token_embeddings(
+                10 + len(cls.tokenizers[model_id])
+            )
+
+    def test_generate_only_number(self):
+        # test greedy decoding with grammar constraints
+        grammar_str = """
+        root ::= [0-9]+
+        """
+
+        for model_id in MODEL_IDS:
+            model = self.models[model_id]
+            tokenizer = self.tokenizers[model_id]
+
+            grammar = IncrementalTokenRecognizer(
+                grammar_str, start_rule_name="root", tokenizer=tokenizer
+            )
+            grammar_processor = GrammarConstrainedLogitsProcessor(grammar)
+
+            prefix = "This is a valid number:"
+
+            input_ids = tokenizer(
+                [prefix], add_special_tokens=False, return_tensors="pt", padding=True
+            )["input_ids"]
+
+            output = model.generate(
+                input_ids,
+                do_sample=False,
+                pad_token_id=tokenizer.eos_token_id,
+                eos_token_id=tokenizer.eos_token_id,
+                num_beams=1,
+                max_new_tokens=40,
+                top_p=0.92,
+                top_k=5,
+                logits_processor=[grammar_processor],
+                repetition_penalty=100.0,
+                early_stopping=True,
+            )
+
+            generations = tokenizer.batch_decode(
+                output[:, input_ids.shape[1] :], skip_special_tokens=True
+            )
+            self.assertTrue(
+                generations[0].isdigit(), f"generations: {generations} is not a number"
+            )
+
+    def test_generate_balanced_parenthesis(self):
+        # test greedy decoding with grammar constraints
+        grammar_str = """
+        root ::= "(" root ")" | ""
+        """
+
+        for model_id in MODEL_IDS:
+            model = self.models[model_id]
+            tokenizer = self.tokenizers[model_id]
+
+            grammar = IncrementalTokenRecognizer(
+                grammar_str, start_rule_name="root", tokenizer=tokenizer
+            )
+            grammar_processor = GrammarConstrainedLogitsProcessor(grammar)
+
+            prefix = "This is a valid json:"
+
+            input_ids = tokenizer(
+                [prefix], add_special_tokens=False, return_tensors="pt", padding=True
+            )["input_ids"]
+
+            output = model.generate(
+                input_ids,
+                do_sample=True,
+                pad_token_id=tokenizer.eos_token_id,
+                eos_token_id=tokenizer.eos_token_id,
+                num_beams=1,
+                max_new_tokens=40,
+                top_p=0.92,
+                top_k=5,
+                logits_processor=[grammar_processor],
+                repetition_penalty=100.0,
+                early_stopping=True,
+            )
+
+            generation: str = tokenizer.batch_decode(
+                output[:, input_ids.shape[1] :], skip_special_tokens=True
+            )[0]
+
+            self.assertTrue(
+                check_parentheses(generation),
+                f"generations: {generation} is not a balanced parenthesis",
+            )