Appreciate you taking out the trash. :)

Bottom line...perceived and real vulnerabilities impact risk management. We need to help balance these through education, clear information, and practical solutions.

not a bot

Open AI API Ollama promptGenAI

@Clintin Lyle Kruger I use it extensively throughout the day for nearly all text-related tasks. I hardly ever need to perform a web search anymore. What once took hours or even days—research, coding, and troubleshooting—now takes just minutes. Whether it's drafting emails, managing data, or analyzing logs, I continually discover new uses for it. The bulk of my time is dedicated to coding-related tasks, proof-of-concept, enhancements and other improvements.

If you previously tried using Ollama but faced challenges due to required system changes, frameworks, third-party calls, or containers, consider giving it another shot. I've uploaded some of my home-brew helpers to GitHub, specifically designed for individuals who are new to local LLMs, curious and eager to experiment, or interested in exploring the full inner workings of an AI-enhanced chat conversation interface. These tools aim to facilitate the use of local LLMs on a single workstation with a simple browser-based chat interface. You can find these helpers on GitHub here: https://github.com/aznight85048/unofficial-ollama-helpers ### To get started: 1. **Ensure you have Ollama installed with default settings and access to Python at your command line.** 2. **Run `ollama_proxy.py` from the command line.** 3. **Open `api-chat-interface.html` in your browser** to start using the chat interface. If you encounter any issues, there's an `availability_checker.html` file available in the repository. Open it in your browser to view the related ports and assist with troubleshooting. These helpers aim to simplify the process and make it more accessible for anyone wanting to experiment with local LLMs. Enjoy the journey and happy experimenting!

When starting out with local LLMs, it's a good idea to begin with a smaller model to ensure your configuration is working correctly. Personally, I use `tinydolphin:1.1b-v2.8-q5_K_M`. It does not excel at any specific task, but it generates responses relatively quickly compared to larger models. If you achieve good performance with smaller models, you can then experiment with larger ones until your machine reaches its limits or the token generation rate becomes too slow. If you have a graphics card, make sure that ollama recognizes and utilizes it, as even a small amount of graphics capability is better than none. If you want a super simple ollama setup, just to start chatting with the ollama API and get an idea of how your laptop will perform with a bare bones browser interface; I pushed a helper kit out to github last week: https://github.com/aznight85048/unofficial-ollama-helpers