ngxson HF staff rasmus1610 commited on
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Update README.md (#6)

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- Update README.md (66d50a29cc798d8f30a240656c3a483c4d42f68e)


Co-authored-by: Marius <rasmus1610@users.noreply.huggingface.co>

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  1. README.md +1 -1
README.md CHANGED
@@ -100,7 +100,7 @@ Below are some system and instruct prompts that work well for special tasks
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  ```python
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  system_prompt_rewrite = "You are an AI writing assistant. Your task is to rewrite the user's email to make it more professional and approachable while maintaining its main points and key message. Do not return any text other than the rewritten message."
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  user_prompt_rewrite = "Rewrite the message below to make it more friendly and approachable while maintaining its main points and key message. Do not add any new information or return any text other than the rewritten message\nThe message:"
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- messages = [{"role": "system", "content": system_prompt_rewrite}, {"role": "user", "content":f"{user_prompt_rewrite} The CI is failing after your last commit!}"]
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  input_text=tokenizer.apply_chat_template(messages, tokenize=False)
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  inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
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  outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True)
 
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  ```python
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  system_prompt_rewrite = "You are an AI writing assistant. Your task is to rewrite the user's email to make it more professional and approachable while maintaining its main points and key message. Do not return any text other than the rewritten message."
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  user_prompt_rewrite = "Rewrite the message below to make it more friendly and approachable while maintaining its main points and key message. Do not add any new information or return any text other than the rewritten message\nThe message:"
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+ messages = [{"role": "system", "content": system_prompt_rewrite}, {"role": "user", "content":f"{user_prompt_rewrite} The CI is failing after your last commit!"}]
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  input_text=tokenizer.apply_chat_template(messages, tokenize=False)
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  inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
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  outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True)