diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..88b8d07
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+/.classpath
+/.project
diff --git a/Llama3.java b/Llama3.java
old mode 100755
new mode 100644
index 25a495f..bd3d4ab
--- a/Llama3.java
+++ b/Llama3.java
@@ -85,44 +85,45 @@ static void runInteractive(Llama model, Sampler sampler, Options options) {
             conversationTokens.addAll(chatFormat.encodeMessage(new ChatFormat.Message(ChatFormat.Role.SYSTEM, options.systemPrompt())));
         }
         int startPosition = 0;
-        Scanner in = new Scanner(System.in);
-        while (true) {
-            System.out.print("> ");
-            System.out.flush();
-            String userText = in.nextLine();
-            if (List.of("quit", "exit").contains(userText)) {
-                break;
-            }
-            if (state == null) {
-                state = model.createNewState();
-            }
-            conversationTokens.addAll(chatFormat.encodeMessage(new ChatFormat.Message(ChatFormat.Role.USER, userText)));
-            conversationTokens.addAll(chatFormat.encodeHeader(new ChatFormat.Message(ChatFormat.Role.ASSISTANT, "")));
-            Set<Integer> stopTokens = chatFormat.getStopTokens();
-            List<Integer> responseTokens = Llama.generateTokens(model, state, startPosition, conversationTokens.subList(startPosition, conversationTokens.size()), stopTokens, options.maxTokens(), sampler, options.echo(), token -> {
-                if (options.stream()) {
-                    if (!model.tokenizer().isSpecialToken(token)) {
-                        System.out.print(model.tokenizer().decode(List.of(token)));
-                    }
-                }
-            });
-            // Include stop token in the prompt history, but not in the response displayed to the user.
-            conversationTokens.addAll(responseTokens);
-            startPosition = conversationTokens.size();
-            Integer stopToken = null;
-            if (!responseTokens.isEmpty() && stopTokens.contains(responseTokens.getLast())) {
-                stopToken = responseTokens.getLast();
-                responseTokens.removeLast();
-            }
-            if (!options.stream()) {
-                String responseText = model.tokenizer().decode(responseTokens);
-                System.out.println(responseText);
-            }
-            if (stopToken == null) {
-                System.err.println("Ran out of context length...");
-                break;
-            }
-        }
+        try (Scanner in = new Scanner(System.in)) {
+			while (true) {
+			    System.out.print("> ");
+			    System.out.flush();
+			    String userText = in.nextLine();
+			    if (List.of("quit", "exit").contains(userText)) {
+			        break;
+			    }
+			    if (state == null) {
+			        state = model.createNewState();
+			    }
+			    conversationTokens.addAll(chatFormat.encodeMessage(new ChatFormat.Message(ChatFormat.Role.USER, userText)));
+			    conversationTokens.addAll(chatFormat.encodeHeader(new ChatFormat.Message(ChatFormat.Role.ASSISTANT, "")));
+			    Set<Integer> stopTokens = chatFormat.getStopTokens();
+			    List<Integer> responseTokens = Llama.generateTokens(model, state, startPosition, conversationTokens.subList(startPosition, conversationTokens.size()), stopTokens, options.maxTokens(), sampler, options.echo(), token -> {
+			        if (options.stream()) {
+			            if (!model.tokenizer().isSpecialToken(token)) {
+			                System.out.print(model.tokenizer().decode(List.of(token)));
+			            }
+			        }
+			    });
+			    // Include stop token in the prompt history, but not in the response displayed to the user.
+			    conversationTokens.addAll(responseTokens);
+			    startPosition = conversationTokens.size();
+			    Integer stopToken = null;
+			    if (!responseTokens.isEmpty() && stopTokens.contains(responseTokens.getLast())) {
+			        stopToken = responseTokens.getLast();
+			        responseTokens.removeLast();
+			    }
+			    if (!options.stream()) {
+			        String responseText = model.tokenizer().decode(responseTokens);
+			        System.out.println(responseText);
+			    }
+			    if (stopToken == null) {
+			        System.err.println("Ran out of context length...");
+			        break;
+			    }
+			}
+		}
     }
 
     static void runInstructOnce(Llama model, Sampler sampler, Options options) {
@@ -346,7 +347,8 @@ public int byteSize() {
         }
     }
 
-    private void loadModelImpl(FileChannel fileChannel) throws IOException {
+    @SuppressWarnings("preview")
+	private void loadModelImpl(FileChannel fileChannel) throws IOException {
         // The header of the file.
         readHeader(fileChannel); // gguf_header_t header;
         // Tensor infos, which can be used to locate the tensor data.
@@ -726,7 +728,7 @@ private static Tokenizer createTokenizer(Map<String, Object> metadata, Vocabular
 
         int allTokens = vocabulary.size();
         int baseTokens = 128000; // assume all tokens after the base ones are special.
-        int reservedSpecialTokens = allTokens - baseTokens;
+
         List<String> specialTokensList = Arrays.stream(vocabulary.tokens(), baseTokens, allTokens).toList();
 
         assert specialTokensList.stream().allMatch(token -> vocabulary.getIndex(token).isPresent());
@@ -1868,9 +1870,11 @@ public static Pair<float[], float[]> precomputeFreqsCis(int contextLength, int h
                     float loFreqWavelen = oldContextLength / loFreqFactor;
                     float hiFreqWavelen = oldContextLength / hiFreqFactor;
                     float wavelen = (float) (2.0 * Math.PI / freq);
+                   
+                    //This doesn't do anything, so it triggers a warning.
                     if (wavelen < hiFreqWavelen) {
                         freq = freq;
-                    } else if (wavelen > loFreqWavelen) {
+                    } else  if (wavelen > loFreqWavelen) {
                         freq = freq / scaleFactor;
                     } else {
                         float smooth = (oldContextLength / wavelen - loFreqFactor) / (hiFreqFactor - loFreqFactor);