diff --git a/qwen/research_attention.py b/qwen/research_attention.py
index 87fc294..37dd818 100644
--- a/qwen/research_attention.py
+++ b/qwen/research_attention.py
@@ -47,7 +47,7 @@ model = model.eval()
 
 def Dump_tokens_list(model):
     tokens = []
-    for token in range(config.eos_token_id):
+    for token in range(151851):
         decoded, response, end_reason = decode_tokens(
             [token],
             tokenizer,
diff --git a/qwen/research_token.py b/qwen/research_token.py
new file mode 100644
index 0000000..fac6bf3
--- /dev/null
+++ b/qwen/research_token.py
@@ -0,0 +1,136 @@
+import torch
+import sys
+
+# from modelscope import snapshot_download
+from transformers import AutoTokenizer
+from transformers import AutoConfig
+
+from modeling_qwen import QWenLMHeadModel
+from modeling_qwen import QwenRunner
+
+
+from qwen_generation_utils import (
+    make_context,
+    decode_tokens,
+)
+
+import torch.nn.functional as F
+
+sys.path.append("..")
+from tools import show
+
+seed = 4321
+torch.manual_seed(seed)
+torch.cuda.manual_seed_all(seed)
+
+# model_dir = snapshot_download("qwen/Qwen-1_8B-Chat")
+model_dir = "/home/colin/.cache/modelscope/hub/qwen/Qwen-1_8B-Chat"
+
+config, kwargs = AutoConfig.from_pretrained(
+    "./",
+    return_unused_kwargs=True,
+    trust_remote_code=True,
+    code_revision=None,
+    _commit_hash=None,
+)
+
+model = QWenLMHeadModel(config)
+print(model)
+
+tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
+
+model = model.from_pretrained(model_dir)
+if torch.cuda.device_count() > 0:
+    model = model.cuda()
+model = model.eval()
+
+
+class ResearchRunner(QwenRunner):
+    def __init__(self, model):
+        super().__init__(model)
+
+    def forwardQWen(
+        self,
+        input_ids=None,
+        labels=None,
+    ):
+        transfm = self.qwen.transformer
+        input_shape = input_ids.size()
+        input_ids = input_ids.view(-1, input_shape[-1])
+        hidden_states = transfm.wte(input_ids)
+        kv_seq_len = hidden_states.size()[1]
+
+        transfm.update_rotary_pos_emb_cache(kv_seq_len, ntk_alpha=1.0)
+        cos, sin = transfm._rotary_pos_emb_cache
+        rotary_pos_emb_list = [[cos[:, :kv_seq_len], sin[:, :kv_seq_len]]]
+
+        hidden_states = transfm.drop(hidden_states)
+        output_shape = input_shape + (hidden_states.size(-1),)
+
+        for block in transfm.h:
+            self.forwardQWenBlock(block, hidden_states, rotary_pos_emb_list=rotary_pos_emb_list)
+            break
+
+    def forwardQWenBlock(
+        self,
+        block,
+        hidden_states,
+        rotary_pos_emb_list=None,
+    ):
+        layernorm_output = block.ln_1(hidden_states)
+        self.forwardAttention(block.attn, layernorm_output, rotary_pos_emb_list)
+
+    def attention(self, attention, query, key, value, causal_mask):
+        query = query.permute(0, 2, 1, 3)
+        key = key.permute(0, 2, 1, 3)
+        value = value.permute(0, 2, 1, 3)
+        global q
+        global k
+        query = query[:, head_group_index, :, :]
+        key = key[:, head_group_index, :, :]
+        q = torch.cat([q, query], 1)
+        k = torch.cat([k, key], 1)
+
+
+head_group_index = 0
+total_token = 151851
+topk = 10
+
+tokens_str = []
+for token in range(total_token):
+    decoded, response, end_reason = decode_tokens(
+        [token],
+        tokenizer,
+        raw_text_len=0,
+        context_length=0,
+        errors="replace",
+    )
+    tokens_str.append(repr(decoded))
+
+patch_end = list(range(0, total_token, 1000))
+patch_end = patch_end[1:] + [total_token]
+patch_start = 0
+q = torch.zeros((1, 0, 128), dtype=float).to(next(model.parameters()).device)
+k = torch.zeros((1, 0, 128), dtype=float).to(next(model.parameters()).device)
+for end in patch_end:
+    tokens = list(range(patch_start, end))
+    patch_start = end
+    input_ids = torch.tensor([tokens]).to(next(model.parameters()).device)
+    runner = ResearchRunner(model)
+    runner.forwardQWen(input_ids)
+
+q = q[0, :, :]
+k = k[0, :, :].permute(1, 0)
+token_topk = []
+for i in range(total_token):
+    subq = q[i, :]
+    qk = subq @ k
+    values, indices = torch.topk(qk, topk)
+    item = str(i).zfill(7) + " " + tokens_str[i] + " : "
+    for index in indices:
+        item += tokens_str[index] + "  "
+    token_topk.append(item)
+
+show.DumpListToFile(token_topk, "./temp/qwen_token_qk_topk_head_group_" + str(head_group_index) + ".txt")
+
+print("decoded")