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Language models on the command-line

Jun 25, 2024 - simonwillison.net
The article discusses the use of Large Language Models (LLMs) and how they can be accessed and utilized from the command-line. The author gave a talk on this topic at the Mastering LLMs: A Conference For Developers & Data Scientists, focusing on his LLM Python command-line utility and its applications. The utility, which the author began building the previous year, allows users to run LLM prompts directly from a command-line terminal. The article also includes instructions for installing the utility and provides examples of its use. The author also discusses the use of plugins to support additional models and demonstrates how to use the utility to run prompts, save and execute parameterized prompts, and scrape and summarize search results.

Key takeaways:

The markdown data is a detailed description of a talk given by the author about Large Language Models (LLMs) and how to access them from the command-line. The author discusses a Python command-line utility they created for LLMs, which allows users to explore and use LLMs for various tasks. The utility, called LLM, can be installed using pipx, pip, or brew. The author also discusses how to use LLM with OpenAI models and plugins for other providers. They provide examples of how to use LLM, including basic usage and more advanced features like using the `-c` option for follow-up prompts. The author also talks about LLM's support for additional models via plugins and how to use the llm-claude-3 plugin for the Anthropic Claude 3 family of models. They also discuss how LLM logs every prompt and response to a SQLite database, which can be browsed using Datasette. The author also provides examples of how to use LLM to run local models, scrape Google search results, and create embeddings for data. They also discuss how to contribute to LLM by writing plugins that support new models. The author concludes by discussing how to use templates to save and execute parameterized prompts and how to use LLM to embed content in bulk.
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