LLM Command Line Tool
LLM is a CLI utility and Python library for interacting with Large Language Models, supporting remote APIs and local installations, prompt execution, embedding generation, and interaction logging in SQLite.
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LLM is a command-line interface (CLI) utility and Python library designed for interacting with Large Language Models (LLMs) through both remote APIs and local installations. Users can execute prompts, store results in SQLite, and generate embeddings. The tool supports self-hosted models via plugins, allowing users to run models like Llama 2 on their local machines. Installation can be done using pip, Homebrew, or pipx, and users can set up their OpenAI API keys for access. The utility also includes features for interactive chatting with models, executing system prompts, and managing plugins for additional functionalities. Users can create and manage embeddings, and the library provides a structured way to log interactions and manage model configurations. The project is continuously updated, with recent versions introducing new features and improvements.
- LLM is a CLI utility and Python library for interacting with Large Language Models.
- It supports both remote API access and local model installations via plugins.
- Users can execute prompts, generate embeddings, and log interactions in SQLite.
- Installation options include pip, Homebrew, and pipx, with support for OpenAI API keys.
- The tool allows for interactive chatting and managing various models and plugins.
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1 commentsImplemented in Rust and using the fastest LLM (Groq) per default.
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You can easily set up and run large language models (LLMs) on your PC using tools like Ollama, LM Suite, and Llama.cpp. Ollama supports AMD GPUs and AVX2-compatible CPUs, with straightforward installation across different systems. It offers commands for managing models and now supports select AMD Radeon cards.
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The `llm2sh` utility translates plain language into shell commands using LLMs like OpenAI and Claude. It offers customization, YOLO mode, and extensibility. Installation via `pip` is simple. User privacy is prioritized. Contributions to the GPLv3-licensed project are welcome. Users should review commands before execution. Visit the GitHub repository for details.
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