Hi everyone,
I’m currently finishing the first year of my Master’s in AI and Data Science, and I’m very interested in doing a research project (70h of work) focused on Large Language Models. This could include re-implementing well-known techniques, benchmarking different methods, or exploring interesting variations of existing approaches.
I’d really appreciate any suggestions on concrete project ideas, open problems, or good starting points.
Thanks in advance!
Welcome to Hugging Face @JordanCarter
Good luck with your request.
One of the recent initiatives I have seen was related to Inference Time Compute Paradigm. It’s incredibly interesting. Would suggest checking this paper out.
I designed a system prompt that aims to drive major LLMs (ChatGPT, Gemini, Claude, Grok) from their digital thinking mode (token chain) to a (simulated) analogy thinking mode (Field Tension), fostering more balanced and creative outputs.
For your information, the system prompt in that post was built upon a well established “Field Tension” thinking mode concept that already well known by my LLM. Then I ask my LLM to work back a prompt with related strong attractors.
I hope these information can illustrate the usage of strong “attractor” in LLM Prompts. May be you can find some interesting insights from it.
AI Systems Architect here. I believe large language models (LLMs) are rapidly becoming obsolete. I’m about to release a new generation of AI bots and wanted to share an early milestone.
I’ve successfully packed 17 billion clean, deduplicated tokens into a d32 model and achieved zero loss. This was accomplished entirely offline, without any cloud or online services. The results speak for themselves efficient, deterministic, and capable models that break the mold of today’s oversized LLMs. In other words. They’re done. I’ll be launching 5 new foundational base models targeting every industry on the 31st of August.
Happy to answer any questions you might have too.
Each additional epoch = another dataset installed for me.
I humbly bow. Thank you for sharing your perspective.