Week 6: Understanding LLM APIs

Week 6: Understanding LLM APIs#

Key Learning Content#

  • OpenAI API Usage: Learn how to interact with OpenAI’s language model APIs for various NLP tasks.

  • Tokenization: Understand how tokenization works in large language models, including token counting and limitations.

  • Sampling Methods: Explore different sampling techniques like temperature, top-k, and top-p sampling to control output randomness and creativity.

Lecture#

  • Introduction to LLM APIs: Overview of Large Language Model APIs, their capabilities, and how they can be leveraged in NLP applications.

  • Deep Dive into OpenAI API: Walkthrough of OpenAI’s API features, authentication, rate limits, and best practices.

  • Tokenization in LLMs: Explanation of how text is tokenized in language models, the significance of tokens, and how to estimate token counts.

  • Sampling Methods Explained: Detailed look at sampling methods used during text generation, their parameters, and impact on the output.

Practice#

  • API Setup and Authentication: Hands-on exercise to set up the OpenAI API, including obtaining API keys and configuring the environment.

  • Simple Text Generation: Practice making API calls to generate text based on prompts.

  • Experimenting with Sampling Parameters: Modify temperature and top_p values to see how they affect text generation.

  • Token Counting: Use tokenizers to count tokens in prompts and outputs, ensuring adherence to model limits.

Learning Objectives#

By the end of Week 6, students will be able to:

  1. Set up and authenticate with the OpenAI API.

  2. Understand how tokenization affects input and output in LLMs.

  3. Utilize different sampling methods to control text generation.

  4. Generate text using API calls and interpret the results.

  5. Handle common issues and errors when working with LLM APIs.

Assignments#

  • Assignment 6.1: Write a script that generates text based on a user-provided prompt using the OpenAI API.

  • Assignment 6.2: Experiment with different sampling parameters (temperature, top_p) and document how they affect the generated text.

  • Assignment 6.3: Calculate the number of tokens in various prompts and outputs to ensure they are within model limits.

Additional Resources#