diff --git a/llama_cpp/llama.py b/llama_cpp/llama.py
index 6babebd..4f10227 100644
--- a/llama_cpp/llama.py
+++ b/llama_cpp/llama.py
@@ -299,6 +299,8 @@ class Llama:
         """Reset the model state."""
         self.eval_tokens.clear()
         self.eval_logits.clear()
+        self._input_ids = np.array([], dtype=np.intc)
+        self._scores = np.ndarray((0, self._n_vocab), dtype=np.single)
 
     def eval(self, tokens: Sequence[int]):
         """Evaluate a list of tokens.
@@ -310,7 +312,7 @@ class Llama:
         n_ctx = self._n_ctx
         for i in range(0, len(tokens), self.n_batch):
             batch = tokens[i : min(len(tokens), i + self.n_batch)]
-            n_past = min(n_ctx - len(batch), len(self.eval_tokens))
+            n_past = min(n_ctx - len(batch), len(self._input_ids))
             n_tokens = len(batch)
             return_code = llama_cpp.llama_eval(
                 ctx=self.ctx,
@@ -356,6 +358,7 @@ class Llama:
     ):
         assert self.ctx is not None
         assert len(self.eval_logits) > 0
+        assert self._scores.shape[0] > 0
         n_vocab = self._n_vocab
         n_ctx = self._n_ctx
         top_k = llama_cpp.c_int(n_vocab) if top_k.value <= 0 else top_k
@@ -368,7 +371,7 @@ class Llama:
 
         if logits_processor is not None:
             logits = np.array(
-                logits_processor(list(self.eval_tokens), logits.tolist()),
+                logits_processor(self._input_ids.tolist(), logits.tolist()),
                 dtype=np.single,
             )
             self._scores[-1, :] = logits
@@ -498,8 +501,8 @@ class Llama:
         """
         assert self.ctx is not None
         last_n_tokens_data = [llama_cpp.llama_token(0)] * max(
-            0, self.last_n_tokens_size - len(self.eval_tokens)
-        ) + list(self.eval_tokens)[-self.last_n_tokens_size :]
+            0, self.last_n_tokens_size - len(self._input_ids)
+        ) + self._input_ids[-self.last_n_tokens_size :].tolist()
         return self._sample(
             last_n_tokens_data=(llama_cpp.llama_token * self.last_n_tokens_size)(
                 *last_n_tokens_data
@@ -557,9 +560,9 @@ class Llama:
         """
         assert self.ctx is not None
 
-        if reset and len(self.eval_tokens) > 0:
+        if reset and len(self._input_ids) > 0:
             longest_prefix = 0
-            for a, b in zip(self.eval_tokens, tokens[:-1]):
+            for a, b in zip(self._input_ids, tokens[:-1]):
                 if a == b:
                     longest_prefix += 1
                 else:
@@ -569,6 +572,8 @@ class Llama:
                     print("Llama.generate: prefix-match hit", file=sys.stderr)
                 reset = False
                 tokens = tokens[longest_prefix:]
+                self._input_ids = self._input_ids[:longest_prefix]
+                self._scores = self._scores[:longest_prefix, :]
                 for _ in range(len(self.eval_tokens) - longest_prefix):
                     self.eval_tokens.pop()
                     try:
@@ -595,7 +600,7 @@ class Llama:
                 logits_processor=logits_processor,
             )
             if stopping_criteria is not None and stopping_criteria(
-                list(self.eval_tokens), self.eval_logits[-1]
+                self._input_ids.tolist(), self._scores[-1, :].tolist()
             ):
                 return
             tokens_or_none = yield token
@@ -820,7 +825,7 @@ class Llama:
                             self.detokenize(completion_tokens[:returned_tokens])
                         )
                         token_offset = len(prompt_tokens) + returned_tokens
-                        logits = self.eval_logits[token_offset - 1]
+                        logits = self._scores[token_offset - 1, :].tolist()
                         current_logprobs = Llama.logits_to_logprobs(logits)
                         sorted_logprobs = list(
                             sorted(
@@ -869,7 +874,7 @@ class Llama:
                 break
 
         if stopping_criteria is not None and stopping_criteria(
-            list(self.eval_tokens), self.eval_logits[-1]
+            self._input_ids.tolist(), self._scores[-1, :].tolist()
         ):
             text = self.detokenize(completion_tokens)
             finish_reason = "stop"
@@ -899,7 +904,7 @@ class Llama:
                         self.detokenize(completion_tokens[:returned_tokens])
                     )
                     token_offset = len(prompt_tokens) + returned_tokens - 1
-                    logits = self.eval_logits[token_offset]
+                    logits = self._scores[token_offset, :].tolist()
                     current_logprobs = Llama.logits_to_logprobs(logits)
                     sorted_logprobs = list(
                         sorted(
@@ -1001,8 +1006,7 @@ class Llama:
                 for token in all_tokens
             ]
             all_logprobs = [
-                Llama.logits_to_logprobs(list(map(float, row)))
-                for row in self.eval_logits
+                Llama.logits_to_logprobs(row.tolist()) for row in self._scores
             ][token_offset:]
             for token, token_str, logprobs_token in zip(
                 all_tokens, all_token_strs, all_logprobs