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@@ -99,7 +99,7 @@ We recommend using the model to respond to human instructions written in natural
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  Since this decoder-only model is fine-tuned with wrapper text, we suggest using the same wrapper text to achieve the best performance.
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  See the example on the right or the code below.
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- We now show you how to load and use our model using HuggingFace `pipline()`.
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  ```python
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  # pip install -q transformers
@@ -107,15 +107,15 @@ from transformers import pipeline
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  checkpoint = "{model_name}"
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- model = pipeline('text-generation', model=checkpoint, use_auth_token=True)
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  instruction = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"'
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  input_prompt = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
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- generated_text = generator(input_prompt, max_length=512, do_sample=True)[0]['generated_text']
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- print("Response": generated_text)
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  ```
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  ## Training Procedure
 
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  Since this decoder-only model is fine-tuned with wrapper text, we suggest using the same wrapper text to achieve the best performance.
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  See the example on the right or the code below.
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+ We now show you how to load and use our model using HuggingFace `pipeline()`.
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  ```python
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  # pip install -q transformers
 
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  checkpoint = "{model_name}"
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+ model = pipeline('text-generation', model = checkpoint)
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  instruction = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"'
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  input_prompt = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
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+ generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text']
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+ print("Response", generated_text)
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  ```
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  ## Training Procedure