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LLM prompts, llama3 prompts, llama2 prompts
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<p align="center"><h1>🧠 Awesome Llama Prompts </h1></p>
[](https://awesome.re)
[](https://github.com/langgptai/awesome-llama-prompts/blob/main/LICENSE)
Welcome to the "Awesome Llama Prompts" repository! This is a collection of prompt examples to be used with the Llama model.
The [Llama](https://github.com/meta-llama/llama) model is an Open Foundation and Fine-Tuned Chat Models developed by Meta. By providing it with a prompt, it can generate responses that continue the conversation or expand on the given prompt.
In this repository, you will find a variety of prompts that can be used with Llama. We encourage you to [add your own prompts](https://github.com/langgptai/awesome-llama-prompts/edit/main/README.md) to the list, and to use Llama to generate new prompts as well.
For Chinese you can find:
* [Llama-Chinese](https://github.com/LlamaFamily/Llama-Chinese) Llama中文社区，最好的中文Llama大模型，完全开源可商用
* [Llama2 提示词结构与编写指南](articles/llama_prompting.md)
## Content
- [How to Prompt Llama 3](#how-to-prompt-llama-3)
- [How to Prompt Llama 2](#how-to-prompt-llama-2)
- [Asking for JSON output.](#asking-for-json-output)
- [Prompts](#prompts)
- [Syatem Prompt](#syatem-prompt)
- [Llama2-Code-Interpreter Prompts](#llama2-code-interpreter-prompts)
- [Write expert identity for each instruction](#write-expert-identity-for-each-instruction)
- [Dungeon Master Game](#dungeon-master-game)
- [RolePlay](#roleplay)
- [ChatGPT Imitator](#chatgpt-imitator)
- [Attempted Chatbot](#attempted-chatbot)
- [Physicists](#physicists)
- [Tweet Sentiment](#tweet-sentiment)
- [Alpaca](#alpaca)
- [Reference](#reference)
## How to Prompt Llama 3
from:
> https://huggingface.co/blog/llama3#how-to-prompt-llama-3
The base models have no prompt format. Like other base models, they can be used to continue an input sequence with a plausible continuation or for zero-shot/few-shot inference. They are also a great foundation for fine-tuning your own use cases. The Instruct versions use the following conversation structure:
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_msg_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{{ model_answer_1 }}<|eot_id|>
```
This format has to be exactly reproduced for effective use. We’ll later show how easy it is to reproduce the instruct prompt with the chat template available in transformers.
## How to Prompt Llama 2
from
> https://huggingface.co/blog/llama2#how-to-prompt-llama-2
One of the unsung advantages of open-access models is that you have full control over the system prompt in chat applications. This is essential to specify the behavior of your chat assistant –and even imbue it with some personality–, but it's unreachable in models served behind APIs.
We're adding this section just a few days after the initial release of Llama 2, as we've had many questions from the community about how to prompt the models and how to change the system prompt. We hope this helps!
The prompt template for the first turn looks like this:
```
<s>[INST] <<SYS>>
{{ system_prompt }}
<</SYS>>
{{ user_message }} [/INST]
```
Let’s break down the different parts of the prompt structure:
- `<s>`: the beginning of the entire sequence.
- `<<SYS>>`: the beginning of the system message.
- `<</SYS>>`: the end of the system message.
- `[INST]`: the beginning of some instructions.
- `[/INST]`: the end of some instructions.
- `{{ system_prompt }}`: Where the user should edit the system prompt to give overall context to model responses.
- `{{ user_message }}`: Where the user should provide instructions to the model for generating outputs.
This template follows the model's training procedure, as described in the Llama 2 paper. We can use any system_prompt we want, but it's crucial that the format matches the one used during Excerpt of 21,527 characters
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