madaan/llm-reasoning-tutorial
Resources for few-shot reasoning tutorial
This project provides practical guidance and interactive examples for working with large language models (LLMs). It helps you understand how to design effective instructions (prompts) and interpret the model's responses. By exploring different prompting techniques, you can make LLMs generate more accurate and useful text for various applications. It's intended for anyone who wants to leverage LLMs for tasks like content generation, question answering, or text analysis, and needs to improve the quality of their interactions.
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Use this if you are a data scientist, researcher, or AI practitioner looking to improve the performance of large language models for specific tasks through effective prompting strategies.
Not ideal if you are looking for a ready-to-use application or a deep dive into advanced, cutting-edge LLM architectures rather than practical prompting techniques.
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Language
Jupyter Notebook
License
Apache-2.0
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Last pushed
Oct 16, 2023
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