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  1. README.md +4 -4
README.md CHANGED
@@ -63,14 +63,14 @@ It is recommended to directly call the [`generate`](https://huggingface.co/docs/
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  >>> # the fast tokenizer currently does not work correctly
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  >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-6.7b", use_fast=False)
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- >>> prompt = "Hello, I'm am conscious and"
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  >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
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  >>> generated_ids = model.generate(input_ids)
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  >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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- ["Hello, I'm am conscious and aware of my surroundings. I'm not sure what you mean"]
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  ```
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  By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`.
@@ -84,7 +84,7 @@ By default, generation is deterministic. In order to use the top-k sampling, ple
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  >>> # the fast tokenizer currently does not work correctly
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  >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-6.7b", use_fast=False)
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- >>> prompt = "Hello, I'm am conscious and"
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  >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
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@@ -92,7 +92,7 @@ By default, generation is deterministic. In order to use the top-k sampling, ple
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  >>> generated_ids = model.generate(input_ids, do_sample=True)
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  >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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- ["Hello, I'm am conscious and aware of my surroundings. I'm not sure if I'm"]
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  ```
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  ### Limitations and bias
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  >>> # the fast tokenizer currently does not work correctly
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  >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-6.7b", use_fast=False)
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+ >>> prompt = "Hello, I'm conscious and"
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  >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
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  >>> generated_ids = model.generate(input_ids)
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  >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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+ ['Hello, I am conscious and aware of my surroundings.\nI am also conscious and aware of']
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  ```
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  By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`.
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  >>> # the fast tokenizer currently does not work correctly
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  >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-6.7b", use_fast=False)
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+ >>> prompt = "Hello, I'm conscious and"
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  >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
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  >>> generated_ids = model.generate(input_ids, do_sample=True)
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  >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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+ ['Hello, I am conscious and aware.\nSo that makes you dead\nNot always.']
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  ```
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  ### Limitations and bias