Ollama now supports HuggingFace GGUF models, making it easier for users to run AI models locally without internet. The GGUF format allows for the use of AI models on modest-sized consumer hardware.
Discussion in r/LocalLLaMA about finding a self-hosted, local RAG (Retrieval Augmented Generation) solution for large language models, allowing users to experiment with different prompts, models, and retrieval rankings. Various tools and resources are suggested, such as Open-WebUI, kotaemon, and tldw.
This article guides you through the process of building a local RAG (Retrieval-Augmented Generation) system using Llama 3, Ollama for model management, and LlamaIndex as the RAG framework. The tutorial demonstrates how to get a basic local RAG system up and running with just a few lines of code.
- 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.
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
