diff --git a/crates/llama-cpp-bindings/src/engine.cc b/crates/llama-cpp-bindings/src/engine.cc
index 10b1bb7..476e0fb 100644
--- a/crates/llama-cpp-bindings/src/engine.cc
+++ b/crates/llama-cpp-bindings/src/engine.cc
@@ -28,25 +28,29 @@ struct Request {
 
   std::vector<llama_token> tokens;
   size_t i_batch = -1;
-  size_t n_past = 0;
   size_t n_draft = 0;
 
   int32_t multibyte_pending = 0;
   std::string generated_text;
 
 
-  std::vector<llama_token> all_tokens;
-  std::vector<llama_token> find_candidate_pred_tokens(size_t ngram_size = 3, size_t n_pred_tokens = 8) {
-    if (all_tokens.size() < ngram_size) return {};
-    std::vector<llama_token> ngram(all_tokens.begin() + all_tokens.size() - ngram_size, all_tokens.end());
+  std::vector<llama_token> past_tokens;
 
-    const auto end = all_tokens.end() - ngram_size - n_pred_tokens;
-    const auto matched = std::search(all_tokens.begin(), end, ngram.begin(), ngram.end());
+  std::vector<llama_token> find_candidate_pred_tokens(size_t ngram_size = 3, size_t n_pred_tokens = 8) {
+    if (past_tokens.size() < ngram_size) return {};
+    std::vector<llama_token> ngram(past_tokens.begin() + past_tokens.size() - ngram_size, past_tokens.end());
+
+    const auto end = past_tokens.end() - ngram_size - n_pred_tokens;
+    const auto matched = std::search(past_tokens.begin(), end, ngram.begin(), ngram.end());
     if (matched == end) return {};
 
     const auto begin = matched + ngram_size;
     return std::vector<llama_token>(begin, begin + n_pred_tokens);
   }
+
+  size_t n_past() {
+    return past_tokens.size();
+  }
 };
 
 
@@ -188,7 +192,7 @@ class TextInferenceEngineImpl : public TextInferenceEngine {
       const size_t n_tokens = batch_.n_tokens;
       for (size_t i = 0; i < request.tokens.size(); ++i) {
         batch_.token[n_tokens + i] = request.tokens[i];
-        batch_.pos[n_tokens + i] = request.n_past + i;
+        batch_.pos[n_tokens + i] = request.n_past() + i;
         batch_.n_seq_id[n_tokens + i] = 1;
         batch_.seq_id[n_tokens + i][0] = request.id;
         batch_.logits[n_tokens + i] = false;
@@ -232,18 +236,16 @@ class TextInferenceEngineImpl : public TextInferenceEngine {
 
         llama_token next_token = -1;
         size_t n_tokens = request.tokens.size() - request.n_draft - 1;
-        request.all_tokens.insert(request.all_tokens.end(), request.tokens.begin(), request.tokens.begin() + n_tokens);
-        request.n_past += n_tokens;
+        request.past_tokens.insert(request.past_tokens.end(), request.tokens.begin(), request.tokens.begin() + n_tokens);
 
         // FIXME: ensure batching logic always put i_batch - request.n_draft in this batch.
         for (int k = -request.n_draft; k < 1; ++k) {
           auto logits = llama_get_logits_ith(ctx, i_batch + k);
           next_token = std::distance(logits, std::max_element(logits, logits + n_vocab));
 
-          request.all_tokens.push_back(next_token);
+          request.past_tokens.push_back(next_token);
           const auto token_str = llama_token_to_piece(ctx, next_token);
           request.generated_text += token_str;
-          request.n_past += 1;
 
           // FIXME: Hack for codellama to simplify tabby's implementation.
           const bool is_eos = next_token == eos_id || token_str == " <EOT>";
@@ -285,7 +287,7 @@ class TextInferenceEngineImpl : public TextInferenceEngine {
           }
 
           if ((k < 0 && next_token != request.tokens[request.tokens.size() + k])) {
-            llama_kv_cache_seq_rm(ctx_.get(), request.id, request.n_past, -1);
+            llama_kv_cache_seq_rm(ctx_.get(), request.id, request.n_past(), -1);
             break;
           }
         }