Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).
- The Open Interpreter repository provides a natural language interface for computers.
- It enables users to interact with their computer systems through a chat-like interface in the terminal.
- Open Interpreter supports various programming languages, including Python, Javascript, Shell, and more.
- The repository offers installation instructions, usage examples, and an interactive demo.
- It enables users to interact with their computer systems through a chat-like interface in the terminal.
- Open Interpreter supports various programming languages, including Python, Javascript, Shell, and more.
- The repository offers installation instructions, usage examples, and an interactive demo.
- GitHub repository for a tutorial series called "0 to LitGPT."
- Provides an overview of how to get started with LitGPT, which is an open-source implementation of GPT-3.
- Offers various resources such as codes, issues, pull requests, actions, security features, insights, and more related to the LitGPT project.
- Provides an overview of how to get started with LitGPT, which is an open-source implementation of GPT-3.
- Offers various resources such as codes, issues, pull requests, actions, security features, insights, and more related to the LitGPT project.
Swirl is open-source software that uses AI to simultaneously search multiple content and data sources, finds the best results using a reader LLM, then prompts Generative AI, enabling you to get answers from your own data.
llm-tool provides a command-line utility for running large language models locally. It includes scripts for pulling models from the internet, starting them, and managing them using various commands such as 'run', 'ps', 'kill', 'rm', and 'pull'. Additionally, it offers a Python script named 'querylocal.py' for querying these models. The repository also come
- create a custom base image for a Cloud Workstation environment using a Dockerfile
. Uses:
Quantized models from
. Uses:
Quantized models from
The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). It provides a simple yet robust interface using llama-cpp-python, allowing users to chat with LLM models, execute structured function calls and get structured output.
