We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Remove EOS token before passing the tokenized input to model


How to remove eos token id before passing the input tokens to model. I'm trying for fine-tuned mistral model. Just because there is an eos token id at the end of sentence, model generates the results for a different input which is similar to original input

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `vllm/engine/output_processor/stop_checker.py`
Content:
```
1 from typing import Callable, Optional
2 
3 from transformers import PreTrainedTokenizer
4 
5 from vllm.lora.request import LoRARequest
6 from vllm.sampling_params import SamplingParams
7 from vllm.sequence import Sequence, SequenceStatus
8 
9 
10 class StopChecker:
11     """LLMEngine helper class which separates out the logic involving stop
12     checking. This checks things such as: whether the eos token was emitted,
13     whether the max_tokens has been consumed, whether a stop string has been
14     emitted, or if we have exceeded the max model len.
15     """
16 
17     def __init__(self, max_model_len: int,
18                  get_tokenizer_for_seq: Callable[[Sequence],
19                                                  PreTrainedTokenizer]):
20         # Do not use it directly, but use `self._get_max_model_len`.
21         self._max_model_len = max_model_len
22         self.get_tokenizer_for_seq = get_tokenizer_for_seq
23 
24     def _get_max_model_len(self, lora_req: Optional[LoRARequest]):
25         if lora_req and lora_req.long_lora_max_len:
26             return lora_req.long_lora_max_len
27         else:
28             return self._max_model_len
29 
30     def maybe_stop_sequence(
31         self,
32         seq: Sequence,
33         new_char_count: int,
34         sampling_params: SamplingParams,
35         lora_req: Optional[LoRARequest] = None,
36     ) -> None:
37         """Stop the finished sequences.
38 
39        new_char_count is the number of chars added to the
40            sequence's output text for the newly generated token
41         """
42 
43         # Check if the minimum number of tokens has been generated yet;
44         # skip the stop string/token checks if not
45         if seq.get_output_len() < sampling_params.min_tokens:
46             return
47 
48         # Check if the sequence has generated the EOS token.
49         if ((not sampling_params.ignore_eos)
50                 and seq.get_last_token_id() == seq.eos_token_id):
51             seq.status = SequenceStatus.FINISHED_STOPPED
52             return
53 
54         # Check if a stop token was encountered.
55         # This assumes a single token produced per step.
56         last_token_id = seq.get_last_token_id()
57         if last_token_id in sampling_params.stop_token_ids:
58             if new_char_count and (
59                     not sampling_params.include_stop_str_in_output):
60                 # Remove last token
61                 seq.output_text = seq.output_text[:-new_char_count]
62             seq.status = SequenceStatus.FINISHED_STOPPED
63             seq.stop_reason = last_token_id
64             return
65 
66         # Check if any stop strings are matched.
67         stop_str = self._check_stop_strings(seq, new_char_count,
68                                             sampling_params)
69         if stop_str is not None:
70             seq.status = SequenceStatus.FINISHED_STOPPED
71             seq.stop_reason = stop_str
72             return
73 
74         # Check if the sequence has reached max_model_len.
75         if seq.get_len() > self._get_max_model_len(lora_req):
76             seq.status = SequenceStatus.FINISHED_LENGTH_CAPPED
77             return
78 
79         # Check if the sequence has reached max_tokens.
80         if seq.get_output_len() == sampling_params.max_tokens:
81             seq.status = SequenceStatus.FINISHED_LENGTH_CAPPED
82             return
83 
84     @staticmethod
85     def _check_stop_strings(seq: Sequence, new_char_count: int,
86                             sampling_params: SamplingParams) -> Optional[str]:
87         """Check if any stop strings are matched and truncate sequence
88         output text accordingly.
89 
90         Returns the stop string if matched or else None.
91         """
92         if not new_char_count:
93             return None
94 
95         for stop_str in sampling_params.stop:
96             stop_string_len = len(stop_str)
97             # Avoid searching already-searched text.
98             stop_index = seq.output_text.find(
99                 stop_str, -new_char_count - stop_string_len)
100             if stop_index == -1:
101                 continue
102 
103             if sampling_params.include_stop_str_in_output:
104                 # Truncate to end of stop string.
105                 stop_index += stop_string_len
106                 if stop_index >= len(seq.output_text):
107                     # No truncation required.
108                     return stop_str
109 
110             # Truncate the output text to either the beginning
111             # or end of the stop string.
112             seq.output_text = seq.output_text[:stop_index]
113             return stop_str
114         return None
115
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/vllm/engine/output_processor/stop_checker.py b/vllm/engine/output_processor/stop_checker.py
--- a/vllm/engine/output_processor/stop_checker.py
+++ b/vllm/engine/output_processor/stop_checker.py
@@ -48,6 +48,11 @@
         # Check if the sequence has generated the EOS token.
         if ((not sampling_params.ignore_eos)
                 and seq.get_last_token_id() == seq.eos_token_id):
+            # Remove the last EOS token unless explicitly specified
+            # This prevents unintended exposure of the EOS token
+            if new_char_count and (
+                    not sampling_params.include_stop_str_in_output):
+                seq.output_text = seq.output_text[:-new_char_count]
             seq.status = SequenceStatus.FINISHED_STOPPED
             return

{"golden_diff": "diff --git a/vllm/engine/output_processor/stop_checker.py b/vllm/engine/output_processor/stop_checker.py\n--- a/vllm/engine/output_processor/stop_checker.py\n+++ b/vllm/engine/output_processor/stop_checker.py\n@@ -48,6 +48,11 @@\n         # Check if the sequence has generated the EOS token.\n         if ((not sampling_params.ignore_eos)\n                 and seq.get_last_token_id() == seq.eos_token_id):\n+            # Remove the last EOS token unless explicitly specified\n+            # This prevents unintended exposure of the EOS token\n+            if new_char_count and (\n+                    not sampling_params.include_stop_str_in_output):\n+                seq.output_text = seq.output_text[:-new_char_count]\n             seq.status = SequenceStatus.FINISHED_STOPPED\n             return\n", "issue": "Remove EOS token before passing the tokenized input to model\n\r\n\r\nHow to remove eos token id before passing the input tokens to model. I'm trying for fine-tuned mistral model. Just because there is an eos token id at the end of sentence, model generates the results for a different input which is similar to original input\n", "before_files": [{"content": "from typing import Callable, Optional\n\nfrom transformers import PreTrainedTokenizer\n\nfrom vllm.lora.request import LoRARequest\nfrom vllm.sampling_params import SamplingParams\nfrom vllm.sequence import Sequence, SequenceStatus\n\n\nclass StopChecker:\n    \"\"\"LLMEngine helper class which separates out the logic involving stop\n    checking. This checks things such as: whether the eos token was emitted,\n    whether the max_tokens has been consumed, whether a stop string has been\n    emitted, or if we have exceeded the max model len.\n    \"\"\"\n\n    def __init__(self, max_model_len: int,\n                 get_tokenizer_for_seq: Callable[[Sequence],\n                                                 PreTrainedTokenizer]):\n        # Do not use it directly, but use `self._get_max_model_len`.\n        self._max_model_len = max_model_len\n        self.get_tokenizer_for_seq = get_tokenizer_for_seq\n\n    def _get_max_model_len(self, lora_req: Optional[LoRARequest]):\n        if lora_req and lora_req.long_lora_max_len:\n            return lora_req.long_lora_max_len\n        else:\n            return self._max_model_len\n\n    def maybe_stop_sequence(\n        self,\n        seq: Sequence,\n        new_char_count: int,\n        sampling_params: SamplingParams,\n        lora_req: Optional[LoRARequest] = None,\n    ) -> None:\n        \"\"\"Stop the finished sequences.\n\n       new_char_count is the number of chars added to the\n           sequence's output text for the newly generated token\n        \"\"\"\n\n        # Check if the minimum number of tokens has been generated yet;\n        # skip the stop string/token checks if not\n        if seq.get_output_len() < sampling_params.min_tokens:\n            return\n\n        # Check if the sequence has generated the EOS token.\n        if ((not sampling_params.ignore_eos)\n                and seq.get_last_token_id() == seq.eos_token_id):\n            seq.status = SequenceStatus.FINISHED_STOPPED\n            return\n\n        # Check if a stop token was encountered.\n        # This assumes a single token produced per step.\n        last_token_id = seq.get_last_token_id()\n        if last_token_id in sampling_params.stop_token_ids:\n            if new_char_count and (\n                    not sampling_params.include_stop_str_in_output):\n                # Remove last token\n                seq.output_text = seq.output_text[:-new_char_count]\n            seq.status = SequenceStatus.FINISHED_STOPPED\n            seq.stop_reason = last_token_id\n            return\n\n        # Check if any stop strings are matched.\n        stop_str = self._check_stop_strings(seq, new_char_count,\n                                            sampling_params)\n        if stop_str is not None:\n            seq.status = SequenceStatus.FINISHED_STOPPED\n            seq.stop_reason = stop_str\n            return\n\n        # Check if the sequence has reached max_model_len.\n        if seq.get_len() > self._get_max_model_len(lora_req):\n            seq.status = SequenceStatus.FINISHED_LENGTH_CAPPED\n            return\n\n        # Check if the sequence has reached max_tokens.\n        if seq.get_output_len() == sampling_params.max_tokens:\n            seq.status = SequenceStatus.FINISHED_LENGTH_CAPPED\n            return\n\n    @staticmethod\n    def _check_stop_strings(seq: Sequence, new_char_count: int,\n                            sampling_params: SamplingParams) -> Optional[str]:\n        \"\"\"Check if any stop strings are matched and truncate sequence\n        output text accordingly.\n\n        Returns the stop string if matched or else None.\n        \"\"\"\n        if not new_char_count:\n            return None\n\n        for stop_str in sampling_params.stop:\n            stop_string_len = len(stop_str)\n            # Avoid searching already-searched text.\n            stop_index = seq.output_text.find(\n                stop_str, -new_char_count - stop_string_len)\n            if stop_index == -1:\n                continue\n\n            if sampling_params.include_stop_str_in_output:\n                # Truncate to end of stop string.\n                stop_index += stop_string_len\n                if stop_index >= len(seq.output_text):\n                    # No truncation required.\n                    return stop_str\n\n            # Truncate the output text to either the beginning\n            # or end of the stop string.\n            seq.output_text = seq.output_text[:stop_index]\n            return stop_str\n        return None\n", "path": "vllm/engine/output_processor/stop_checker.py"}], "after_files": [{"content": "from typing import Callable, Optional\n\nfrom transformers import PreTrainedTokenizer\n\nfrom vllm.lora.request import LoRARequest\nfrom vllm.sampling_params import SamplingParams\nfrom vllm.sequence import Sequence, SequenceStatus\n\n\nclass StopChecker:\n    \"\"\"LLMEngine helper class which separates out the logic involving stop\n    checking. This checks things such as: whether the eos token was emitted,\n    whether the max_tokens has been consumed, whether a stop string has been\n    emitted, or if we have exceeded the max model len.\n    \"\"\"\n\n    def __init__(self, max_model_len: int,\n                 get_tokenizer_for_seq: Callable[[Sequence],\n                                                 PreTrainedTokenizer]):\n        # Do not use it directly, but use `self._get_max_model_len`.\n        self._max_model_len = max_model_len\n        self.get_tokenizer_for_seq = get_tokenizer_for_seq\n\n    def _get_max_model_len(self, lora_req: Optional[LoRARequest]):\n        if lora_req and lora_req.long_lora_max_len:\n            return lora_req.long_lora_max_len\n        else:\n            return self._max_model_len\n\n    def maybe_stop_sequence(\n        self,\n        seq: Sequence,\n        new_char_count: int,\n        sampling_params: SamplingParams,\n        lora_req: Optional[LoRARequest] = None,\n    ) -> None:\n        \"\"\"Stop the finished sequences.\n\n       new_char_count is the number of chars added to the\n           sequence's output text for the newly generated token\n        \"\"\"\n\n        # Check if the minimum number of tokens has been generated yet;\n        # skip the stop string/token checks if not\n        if seq.get_output_len() < sampling_params.min_tokens:\n            return\n\n        # Check if the sequence has generated the EOS token.\n        if ((not sampling_params.ignore_eos)\n                and seq.get_last_token_id() == seq.eos_token_id):\n            # Remove the last EOS token unless explicitly specified\n            # This prevents unintended exposure of the EOS token\n            if new_char_count and (\n                    not sampling_params.include_stop_str_in_output):\n                seq.output_text = seq.output_text[:-new_char_count]\n            seq.status = SequenceStatus.FINISHED_STOPPED\n            return\n\n        # Check if a stop token was encountered.\n        # This assumes a single token produced per step.\n        last_token_id = seq.get_last_token_id()\n        if last_token_id in sampling_params.stop_token_ids:\n            if new_char_count and (\n                    not sampling_params.include_stop_str_in_output):\n                # Remove last token\n                seq.output_text = seq.output_text[:-new_char_count]\n            seq.status = SequenceStatus.FINISHED_STOPPED\n            seq.stop_reason = last_token_id\n            return\n\n        # Check if any stop strings are matched.\n        stop_str = self._check_stop_strings(seq, new_char_count,\n                                            sampling_params)\n        if stop_str is not None:\n            seq.status = SequenceStatus.FINISHED_STOPPED\n            seq.stop_reason = stop_str\n            return\n\n        # Check if the sequence has reached max_model_len.\n        if seq.get_len() > self._get_max_model_len(lora_req):\n            seq.status = SequenceStatus.FINISHED_LENGTH_CAPPED\n            return\n\n        # Check if the sequence has reached max_tokens.\n        if seq.get_output_len() == sampling_params.max_tokens:\n            seq.status = SequenceStatus.FINISHED_LENGTH_CAPPED\n            return\n\n    @staticmethod\n    def _check_stop_strings(seq: Sequence, new_char_count: int,\n                            sampling_params: SamplingParams) -> Optional[str]:\n        \"\"\"Check if any stop strings are matched and truncate sequence\n        output text accordingly.\n\n        Returns the stop string if matched or else None.\n        \"\"\"\n        if not new_char_count:\n            return None\n\n        for stop_str in sampling_params.stop:\n            stop_string_len = len(stop_str)\n            # Avoid searching already-searched text.\n            stop_index = seq.output_text.find(\n                stop_str, -new_char_count - stop_string_len)\n            if stop_index == -1:\n                continue\n\n            if sampling_params.include_stop_str_in_output:\n                # Truncate to end of stop string.\n                stop_index += stop_string_len\n                if stop_index >= len(seq.output_text):\n                    # No truncation required.\n                    return stop_str\n\n            # Truncate the output text to either the beginning\n            # or end of the stop string.\n            seq.output_text = seq.output_text[:stop_index]\n            return stop_str\n        return None\n", "path": "vllm/engine/output_processor/stop_checker.py"}]}