diff --git a/README.md b/README.md
index 0299f6c..e7d80fe 100644
--- a/README.md
+++ b/README.md
@@ -39,6 +39,9 @@ We now support two different sequence parallel methods:
 
 We then proceed to train Llama-2-7B on 8 A100 by gradually increasing its rope base frequency to 1B. Notably, our model is only trained with 512K sequence length while generalizing to nearly 1M context.
 
+## Updates
+- [05/06] Add distorctors (multi-needle) in the NIAH evaluation script . You can set the number of distractors using --num_distractor.
+- [05/06] IMPOPTANT! If you want to use eval_needle.py to evaluate the llama3 model, you need to add one extra space (" ") behind the QUESTION_STR. I believe this has something to do with the tokenizer. 
 ## Usage
 
 ```python
diff --git a/eval_needle.py b/eval_needle.py
index c46e0b1..c760e55 100644
--- a/eval_needle.py
+++ b/eval_needle.py
@@ -29,11 +29,17 @@
 
 
 NEEDLE_FORMAT = "\nThe special magic Singapore number is: {}.\n"
+DISTRACTOR_LIST = ["\nThe special magic New York number is: {}.\n", \
+    "\nThe special magic London number is: {}.\n",
+    "\nThe special magic Paris number is: {}.\n",
+    "\nThe special magic Tokyo number is: {}.\n",
+    "\nThe special magic Beijing number is: {}.\n",
+    "\nThe special magic Berlin number is: {}.\n",]
 PREFIX = "This is a very long story book: <book>"
 QUESTION_STR = "</book>.\n Based on the content of the book, Question: What is the special magic Singapore number? Answer: The special magic Singapore number is:"
 
 
-def eval_forward(accelerator, model, input_ids, pad_id, answer_ids):
+def eval_forward(accelerator, model, input_ids, pad_id, answer_ids, tokenizer, distractor_number_list):
     # first append labels to input_ids
     prompt_length = input_ids.shape[1]
     labels_length = answer_ids.shape[1]
@@ -87,6 +93,15 @@ def undo_extract_local(gathered_value, world_size, dim=1):
     # check if the logits are correct, extract argmax id
     # compare the predicted_ids with the labels
     correct = (pred == answer_ids.to(accelerator.device)).all()
+    if not correct and accelerator.is_main_process:
+        print(
+            "Predicted: ",
+            tokenizer.decode(pred.squeeze().tolist()),
+            "Answer: ",
+            tokenizer.decode(answer_ids.squeeze().tolist()),
+            "Distactor: ",
+            distractor_number_list,
+        )
     return int(correct)
 
 
@@ -112,21 +127,30 @@ def construct_prompt(
     tokenized_postfix,
     tokenized_needle,
     context_length,
+    tokenized_distractor_list,
     depth,
 ):
     # insert the needle into depth of the haystack
+    period_tokens = [29889, 869]
     prompt = tokenized_haystack[:context_length]
     if depth == 0:
         start_index = 0
     else:
         start_index = int(len(prompt) * depth)
-        period_tokens = [29889, 869]
         # find the closest period token
         for i in range(start_index, len(prompt)):
             if prompt[i] in period_tokens:
                 start_index = i + 1
                 break
     prompt = prompt[:start_index] + tokenized_needle + prompt[start_index:]
+    # insert distractors
+    for distractor in tokenized_distractor_list:
+        start_index = np.random.randint(0, len(prompt))
+        for i in range(start_index, len(prompt)):
+            if prompt[i] in period_tokens:
+                start_index = i + 1
+                break
+        prompt = prompt[:start_index] + distractor + prompt[start_index:]
     prompt = tokenized_prefix + prompt + tokenized_postfix
     # from transformers import AutoTokenizer
     # tk = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
@@ -168,6 +192,18 @@ def main(args):
         for i in range(args.num_samples)
     ]
     print(random_number_list)
+    distractor_number_list = [
+        int(np.random.randint(10**args.rnd_number_digits))
+        for i in range(args.num_distractor)
+    ]
+    distractor_str_list = [
+        DISTRACTOR_LIST[i % len(DISTRACTOR_LIST)].format(distractor_number_list[i])
+        for i in range(args.num_distractor)
+    ]
+    tokenized_distractor_list = [
+        tokenizer.encode(distractor_str)[1:] for distractor_str in distractor_str_list
+    ]
+    accelerator.print(distractor_str_list)
     all_accuries = []
     for context_length in tqdm(
         range(
@@ -191,12 +227,13 @@ def main(args):
                     tokenized_postfix,
                     tokenized_needle,
                     context_length,
+                    tokenized_distractor_list,
                     depth,
                 )
                 input_ids = torch.tensor([prompt])
                 answer_ids = torch.tensor([tokenizer_answer])
                 correct = eval_forward(
-                    accelerator, model, input_ids, tokenizer.pad_token_id, answer_ids
+                    accelerator, model, input_ids, tokenizer.pad_token_id, answer_ids, tokenizer, distractor_number_list
                 )
                 gc.collect()
                 torch.cuda.empty_cache()
@@ -244,6 +281,13 @@ def main(args):
         # save
         model_name = args.model.split("/")[-1]
         plt.savefig(f"data/heatmap_{model_name}.png".format(model_name))
+        # calculate average accuracy
+        average_accuracy = df["Score"].mean()
+        accelerator.print(f"Average Accuracy: {average_accuracy}")
+        # save as txt
+        with open(f"data/accuracy_{model_name}.txt", "w") as f:
+            f.write(f"Average Accuracy: {average_accuracy}\n")
+        
 
 
 if __name__ == "__main__":
@@ -257,4 +301,5 @@ def main(args):
     args.add_argument("--rope_theta", type=float, default=None)
     args.add_argument("--rnd_number_digits", type=int, default=7)
     args.add_argument("--haystack_dir", type=str, required=True)
+    args.add_argument("--num_distractor", type=int, default=0)
     main(args.parse_args())