羊驼Llama是当前最流行的开源大模型，其卓越的性能和广泛的应用领域使其成为业界瞩目的焦点。作为一款由Meta AI发布的开放且高效的大型基础语言模型，Llama拥有7B、13B和70B（700亿）三种版本，满足不同场景和需求。

吴恩达教授推出了全新的Llama课程，旨在帮助学习者全面理解并掌握Llama大模型这一前沿技术。

课程地址：DLAI - Prompt Engineering with Llama 2

 

知识点笔记

Chat vs. base models

### chat model
prompt = "What is the capital of France?"
response = llama(prompt, verbose=True,model="togethercomputer/llama-2-7b-chat")### base model
prompt = "What is the capital of France?"
response = llama(prompt, verbose=True,add_inst=False,model="togethercomputer/llama-2-7b")

chat model 传送过去的prompt有[inst] [/inst]标记，而base model在设置add_inst=False之后没有标记。

但是具体呢？
原来chat model只返回一个回答，而base model在有[inst]标记的情况下返回所有相同的回答，在没有[inst]标记时返回相似问题。

shot Prompting 样本提示分类

 Zero-shot Prompting 、One-shot Prompting 和Few-shot Prompting

零样本提示、单样本提示和多样本提示，顾名思义，零样本提示，就是prompt里面没有样例，直接提问问题，单样本提示就是给出一个例子，多样本提示就是给出几个例子。大模型最厉害的地方就是即使 多样本提示，一般也只需要区区几个例子，而不想传统的CV、NLP模型，需要几十甚至几百个样例才能达到较好的效果。

零样本提示

prompt = """
Message: Hi Amit, thanks for the thoughtful birthday card!
Sentiment: ?
"""
response = llama(prompt)
print(response)

多样本提示

prompt = """
Message: Hi Dad, you're 20 minutes late to my piano recital!
Sentiment: NegativeMessage: Can't wait to order pizza for dinner tonight
Sentiment: PositiveMessage: Hi Amit, thanks for the thoughtful birthday card!
Sentiment: ?
"""
response = llama(prompt)
print(response)

角色提示

角色为LLM提供了所需答案类型的上下文。
当提供角色时，Llama 2通常会给出更一致的回答。

普通写法

prompt = """
How can I answer this question from my friend:
What is the meaning of life?
"""
response = llama(prompt)
print(response)

有角色提示写法

role = """
Your role is a life coach \
who gives advice to people about living a good life.\
You attempt to provide unbiased advice.
You respond in the tone of an English pirate.
"""prompt = f"""
{role}
How can I answer this question from my friend:
What is the meaning of life?
"""
response = llama(prompt)
print(response)