AssertionError: Padding_idx must be within num_embeddings, demo.py

[Lynnzake]

Describe the bug
during the running of demo.py, when i upload the picture, and chat with the model, it raised error

PS D:\Courses\gitHub Projects\MiniGPT4main> python demo.py --cfg-path eval_configs/minigpt4_eval.yaml  --gpu-id 0                                                                                        
bin C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\bitsandbytes\libbitsandbytes_cuda118.dll
Initializing Chat
Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:30<00:00, 15.40s/it]
Loading Q-Former
C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\huggingface_hub\file_download.py:1150: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
Loading Q-Former Done
Load MiniGPT-4 Checkpoint: D:\Courses\gitHub Projects\MiniGPT4main\pretrained_minigptv\prerained_minigpt4_7b.pth
Initialization Finished
D:\Courses\gitHub Projects\MiniGPT4main\demo.py:171: GradioDeprecationWarning: The `enable_queue` parameter has been deprecated. Please use the `.queue()` method instead.
  demo.launch(share=True, enable_queue=True)
Running on local URL:  http://127.0.0.1:7860

Could not create share link. Please check your internet connection or our status page: https://status.gradio.app.
IMPORTANT: You are using gradio version 3.47.1, however version 4.29.0 is available, please upgrade.
--------
C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\gradio\helpers.py:818: UserWarning: Using the update method is deprecated. Simply return a new object instead, e.g. `return gr.Textbox(...)` instead of `return gr.update(...)
  warnings.warn(
C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\gradio\components\button.py:89: UserWarning: Using the update method is deprecated. Simply return a new object instead, e.g. `return gr.Button(...)` instead of `return gr.Button.update(...)`.
  warnings.warn(
C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\gradio\helpers.py:818: UserWarning: Using the update method is deprecated. Simply return a new object instead, e.g. `return gr.Textbox(...)` instead of `return gr.update(...)
  warnings.warn(
C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\gradio\components\image.py:193: UserWarning: Using the update method is deprecated. Simply return a new object instead, e.g. `return gr.Image(...)` instead of `return gr.Image.update(...)`.
  warnings.warn(
C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\gradio\components\textbox.py:163: UserWarning: Using the update method is deprecated. Simply return a new object instead, e.g. `return gr.Textbox(...)` instead of `return gr.Textbox.update(...)`.
  warnings.warn(
C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\gradio\components\button.py:89: UserWarning: Using the update method is deprecated. Simply return a new object instead, e.g. `return gr.Button(...)` instead of `return gr.Button.update(...)`.
  warnings.warn(
Traceback (most recent call last):
  File "C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\gradio\queueing.py", line 406, in call_prediction
    output = await route_utils.call_process_api(
  File "C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\gradio\route_utils.py", line 226, in call_process_api
    output = await app.get_blocks().process_api(
  File "C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\gradio\blocks.py", line 1554, in process_api
    result = await self.call_function(
  File "C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\gradio\blocks.py", line 1192, in call_function
    prediction = await anyio.to_thread.run_sync(
  File "C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\anyio\to_thread.py", line 56, in run_sync
    return await get_async_backend().run_sync_in_worker_thread(
  File "C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\anyio\_backends\_asyncio.py", line 2177, in run_sync_in_worker_thread
    return await future
  File "C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\anyio\_backends\_asyncio.py", line 859, in run
    result = context.run(func, *args)
  File "C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\gradio\utils.py", line 659, in wrapper
    response = f(*args, **kwargs)
  File "D:\Courses\gitHub Projects\MiniGPT4main\demo.py", line 112, in gradio_answer
    llm_message = chat.answer(conv=chat_state,
  File "D:\Courses\gitHub Projects\MiniGPT4main\minigpt4\conversation\conversation.py", line 186, in answer
    generation_dict = self.answer_prepare(conv, img_list, **kargs)
  File "D:\Courses\gitHub Projects\MiniGPT4main\minigpt4\conversation\conversation.py", line 162, in answer_prepare
    embs = self.model.get_context_emb(prompt, img_list)
  File "D:\Courses\gitHub Projects\MiniGPT4main\minigpt4\models\minigpt_base.py", line 77, in get_context_emb
    seg_embs = [self.embed_tokens(seg_t) for seg_t in seg_tokens]
  File "D:\Courses\gitHub Projects\MiniGPT4main\minigpt4\models\minigpt_base.py", line 77, in <listcomp>
    seg_embs = [self.embed_tokens(seg_t) for seg_t in seg_tokens]
  File "D:\Courses\gitHub Projects\MiniGPT4main\minigpt4\models\minigpt_base.py", line 314, in embed_tokens
    embeds = self.llama_model.base_model.embed_tokens(token_ids)
  File "C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\torch\nn\modules\module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\torch\nn\modules\module.py", line 1520, in _call_impl
    return forward_call(*args, **kwargs)
  File "C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\accelerate\hooks.py", line 165, in new_forward
    output = old_forward(*args, **kwargs)
  File "C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\torch\nn\modules\sparse.py", line 163, in forward
    return F.embedding(
  File "C:\Users\12871\anaconda3\envs\minigptv\lib\site-packages\torch\nn\functional.py", line 2219, in embedding
    assert padding_idx < weight.size(0), "Padding_idx must be within num_embeddings"
AssertionError: Padding_idx must be within num_embeddings

Screenshots

what's the cause of this problem, and how to solve it, help!

[12138k]

Do you solve the problem? I meet the same problem.