模型加载问题

#1
by murphy - opened

每次compute_score都会进行模型加载,耗时严重

  0%|          | 0/8 [00:00<?, ?it/s]You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
100%|██████████| 8/8 [00:12<00:00,  1.53s/it]
Beijing Academy of Artificial Intelligence org

收到反馈。这个是会加载dataloader,而不是加载模型。
后续我们会尝试优化。

现在代码已经更新,在compute_score的时候可以传参use_dataloader=False来解决这个问题

现在代码已经更新,在compute_score的时候可以传参use_dataloader=False来解决这个问题

想问下这个代码更新到哪里了,我填了后报错了。

可以按这样的方式进行更新,git clone https://github.com/FlagOpen/FlagEmbedding.git+pip install .

可以按这样的方式进行更新,git clone https://github.com/FlagOpen/FlagEmbedding.git+pip install .

我是用 xinference 部署也是遇到这个问题,耗时特别严重,也能用这个办法解决吗?

可以按这样的方式进行更新,git clone https://github.com/FlagOpen/FlagEmbedding.git+pip install .

我是用 xinference 部署也是遇到这个问题,耗时特别严重,也能用这个办法解决吗?

可以使用git clone https://github.com/FlagOpen/FlagEmbedding.git+pip install .更新代码,这样就可以在compute_score的时候设置参数use_dataloader=False,从而避免使用dataloader导致的额外耗时问题

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