Exception in thread Thread-1 (load_model):
Traceback (most recent call last):
File "D:\BaiduNetdiskDownload\wenda\wenda\wenda\WPy64-31110\python-3.11.1.amd64\Lib\threading.py", line 1038, in _bootstrap_inner
self.run()
File "D:\BaiduNetdiskDownload\wenda\wenda\wenda\WPy64-31110\python-3.11.1.amd64\Lib\threading.py", line 975, in run
self._target(*self._args, **self._kwargs)
File "D:\BaiduNetdiskDownload\wenda\wenda\wenda\wenda\wenda.py", line 53, in load_model
LLM.load_model()
File "D:\BaiduNetdiskDownload\wenda\wenda\wenda\wenda\llms\llm_llama.py", line 295, in load_model
tokenizer = AutoTokenizer.from_pretrained(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\BaiduNetdiskDownload\wenda\wenda\wenda\WPy64-31110\python-3.11.1.amd64\Lib\site-packages\transformers\models\auto\tokenization_auto.py", line 655, in from_pretrained
raise ValueError(
ValueError: Tokenizer class LlamaTokenizer does not exist or is not currently imported.
No sentence-transformers model found with name model/m3e-base. Creating a new one with MEAN pooling.
Exception in thread Thread-1 (load_model): Traceback (most recent call last): File "D:\BaiduNetdiskDownload\wenda\wenda\wenda\WPy64-31110\python-3.11.1.amd64\Lib\threading.py", line 1038, in _bootstrap_inner self.run() File "D:\BaiduNetdiskDownload\wenda\wenda\wenda\WPy64-31110\python-3.11.1.amd64\Lib\threading.py", line 975, in run self._target(*self._args, **self._kwargs) File "D:\BaiduNetdiskDownload\wenda\wenda\wenda\wenda\wenda.py", line 53, in load_model LLM.load_model() File "D:\BaiduNetdiskDownload\wenda\wenda\wenda\wenda\llms\llm_llama.py", line 295, in load_model tokenizer = AutoTokenizer.from_pretrained( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\BaiduNetdiskDownload\wenda\wenda\wenda\WPy64-31110\python-3.11.1.amd64\Lib\site-packages\transformers\models\auto\tokenization_auto.py", line 655, in from_pretrained raise ValueError( ValueError: Tokenizer class LlamaTokenizer does not exist or is not currently imported. No sentence-transformers model found with name model/m3e-base. Creating a new one with MEAN pooling.