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Update README.md

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  1. README.md +2 -3
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@@ -153,7 +153,7 @@ prompt = prompt(headline=headline, body=body)
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  tokenizer = AutoTokenizer.from_pretrained("Iker/ClickbaitFighter-7B")
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  model = AutoModelForCausalLM.from_pretrained(
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- "Iker/ClickbaitFighter-2B", torch_dtype=torch.bfloat16, device_map="auto"
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  )
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  formatted_prompt = tokenizer.apply_chat_template(
@@ -182,7 +182,6 @@ print(summary.strip().split("\n")[-1]) # Get only the summary, without the promp
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  ## Run inference in the NoticIA dataset
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  ```python
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  import torch # pip install torch
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- from newspaper import Article #pip3 install newspaper3k
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  from datasets import load_dataset # pip install datasets
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  from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig # pip install transformers
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@@ -227,7 +226,7 @@ prompt = prompt(headline=headline, body=body)
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  tokenizer = AutoTokenizer.from_pretrained("Iker/ClickbaitFighter-7B")
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  model = AutoModelForCausalLM.from_pretrained(
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- "Iker/ClickbaitFighter-2B", torch_dtype=torch.bfloat16, device_map="auto"
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  )
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  formatted_prompt = tokenizer.apply_chat_template(
 
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  tokenizer = AutoTokenizer.from_pretrained("Iker/ClickbaitFighter-7B")
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  model = AutoModelForCausalLM.from_pretrained(
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+ "Iker/ClickbaitFighter-7B", torch_dtype=torch.bfloat16, device_map="auto"
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  )
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  formatted_prompt = tokenizer.apply_chat_template(
 
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  ## Run inference in the NoticIA dataset
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  ```python
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  import torch # pip install torch
 
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  from datasets import load_dataset # pip install datasets
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  from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig # pip install transformers
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  tokenizer = AutoTokenizer.from_pretrained("Iker/ClickbaitFighter-7B")
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  model = AutoModelForCausalLM.from_pretrained(
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+ "Iker/ClickbaitFighter-7B", torch_dtype=torch.bfloat16, device_map="auto"
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  )
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  formatted_prompt = tokenizer.apply_chat_template(