diff --git a/README.md b/README.md
index 87808fd..d2b9a70 100644
--- a/README.md
+++ b/README.md
@@ -114,6 +114,5 @@ python3 convert-pth-to-ggml.py models/7B/ 1
   In general, it seems to work, but I think it fails for unicode character support. Hopefully, someone can help with that
 - I don't know yet how much the quantization affects the quality of the generated text
 - Probably the token sampling can be improved
-- No Windows support
 - x86 quantization support [not yet ready](https://github.com/ggerganov/ggml/pull/27). Basically, you want to run this on Apple Silicon
-  
+
diff --git a/main.cpp b/main.cpp
index fb9eb17..982adf1 100644
--- a/main.cpp
+++ b/main.cpp
@@ -728,6 +728,7 @@ int main(int argc, char ** argv) {
 
         // end of text token
         if (embd.back() == 2) {
+            printf(" [end of text]\n");
             break;
         }
     }
diff --git a/models/.gitignore b/models/.gitignore
new file mode 100644
index 0000000..e69de29
diff --git a/utils.cpp b/utils.cpp
index 70a2ac2..cd9c001 100644
--- a/utils.cpp
+++ b/utils.cpp
@@ -231,39 +231,39 @@ std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::stri
 }
 
 std::vector<gpt_vocab::id> llama_tokenize(const gpt_vocab & vocab, const std::string & text, bool bos) {
-    auto res = gpt_tokenize(vocab, text);
+    //auto res = gpt_tokenize(vocab, text);
+
+    //if (bos) {
+    //    res.insert(res.begin(), 1); // TODO: replace with vocab.bos
+    //}
+
+    std::vector<gpt_vocab::id> res;
 
     if (bos) {
-        res.insert(res.begin(), 1); // TODO: replace with vocab.bos
+        res.push_back(1); // TODO: replace with vocab.bos
     }
 
-    //std::vector<gpt_vocab::id> res;
+     //find the longest token that matches the text
+    int pos = 0;
+    while (true) {
+        int l = 0;
+        int t = 0;
+        for (const auto & kv : vocab.id_to_token) {
+            if (kv.second.size() < l) continue;
+            if (kv.second.size() > text.size() - pos) continue;
+            if (text.substr(pos, kv.second.size()) == kv.second) {
+                l = kv.second.size();
+                t = kv.first;
+            }
+        }
 
-    //if (bos) {
-    //    res.push_back(1); // TODO: replace with vocab.bos
-    //}
+        if (l == 0 && t != 13) {
+            break;
+        }
 
-    // find the longest token that matches the text
-    //int pos = 0;
-    //while (true) {
-    //    int l = 0;
-    //    int t = 0;
-    //    for (const auto & kv : vocab.id_to_token) {
-    //        if (kv.second.size() < l) continue;
-    //        if (kv.second.size() > text.size() - pos) continue;
-    //        if (text.substr(pos, kv.second.size()) == kv.second) {
-    //            l = kv.second.size();
-    //            t = kv.first;
-    //        }
-    //    }
-
-    //    if (l == 0 && t != 13) {
-    //        break;
-    //    }
-
-    //    res.push_back(t);
-    //    pos += l;
-    //}
+        res.push_back(t);
+        pos += l;
+    }
 
     return res;
 }
diff --git a/utils.h b/utils.h
index d291964..20c42ba 100644
--- a/utils.h
+++ b/utils.h
@@ -15,12 +15,12 @@
 struct gpt_params {
     int32_t seed      = -1; // RNG seed
     int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
-    int32_t n_predict = 200; // new tokens to predict
+    int32_t n_predict = 128; // new tokens to predict
 
     // sampling parameters
-    int32_t top_k = 100;
+    int32_t top_k = 40;
     float   top_p = 0.95f;
-    float   temp  = 0.8f;
+    float   temp  = 0.80f;
 
     int32_t n_batch = 8; // batch size for prompt processing