I’m new to the field of large language models (LLMs) and I’m really interested in learning how to train and use my own models for qualitative analysis. However, I’m not sure where to start or what resources would be most helpful for a complete beginner. Could anyone provide some guidance and advice on the best way to get started with LLM training and usage? Specifically, I’d appreciate insights on learning resources or tutorials, tips on preparing datasets, common pitfalls or challenges, and any other general advice or words of wisdom for someone just embarking on this journey.

Thanks!

    • TehPers@beehaw.orgEnglish
      3·
      6 months ago

      I managed to get ollama running through Docker easily. It’s by far the least painful of the options I tried, and I just make requests to the API it exposes. You can also give it GPU resources through Docker if you want to, and there’s a CLI tool for a quick chat interface if you want to play with that. I can get LLAMA 3 (8B) running on my 3070 without issues.

      Training a LLM is very difficult and expensive. I don’t think it’s a good place for anyone to start. Many of the popular models (LLAMA, GPT, etc) are astronomically expensive to train and require and ungodly number of resources.

    • its_me_xiphos@beehaw.orgOP
      2·
      5 months ago

      Month later update: This is the route I’ve gone down. I’ve used WSL to get Ollama and WebopenUI to work and started playing around with document analysis using Llama 3. I’m going to try a few other models and see what the same document outputs now. Prompting the model to chat with the documents is…a learning experience, but I’m at the point where I can get it to spit out quotes and provide evidence for it’s interpretation, at least in Llama3. Super fascinating stuff.