Here are some resources where you can find a wealth of educational materials and past papers on machine learning and similar topics:

1. 40 Free Resources to Help You Learn Machine Learning on Your Own: This article provides a list of free resources for learning machine learning, from basics to complex AI techniques1.
2. Machine Learning Repository: The University of California has a repository with over 470 data sets, all classified by the type of problem1.
3. Google DeepMind’s Educational Repository: This GitHub repository contains a collection of educational tutorials prepared for teaching the basics of machine learning2.
4. Best GitHub Repositories For Machine Learning: This article shares a curated list of 100+ widely-known, recommended, and most popular repositories and open-source GitHub projects for Machine Learning and Deep Learning3.
5. My Curated List of AI and Machine Learning Resources from Around the Web: This Medium article contains sections with well-known researchers, AI organizations, video courses, bloggers, Medium writers, books, YouTube channels, Quora topics, subreddits, GitHub repos, podcasts, newsletters, conferences, research links, tutorials, and cheat sheets4.
6. Learn Machine Learning From These GitHub Repositories: This article provides a curated list of machine learning frameworks, libraries, and software to help kickstart your career in machine learning5.

[3:28 PM] Finding University-Level Assessment Data

• The International Journal of STEM Education published a systematic review of the application of AI technologies in STEM education from 2011 to 20212. This could provide insights into the types of assessments used in these fields.
• The OECD Digital Education Outlook 2021 discusses game-based assessment for education3, which might be relevant if you’re interested in innovative assessment methods.
• Mercer | Mettl offers a Machine Learning assessment that measures data analytics skills and knowledge on core and advanced Machine Learning concepts4.
• A study conducted among students in the Republic of Serbia generated data and information on students’ knowledge of AI and ML5. This could give you an idea of the kind of data you might collect.

Fine-Tuning LLaMA Models

• DataCamp has a step-by-step guide on how to fine-tune LLaMA 26. It covers new methodologies and fine-tuning techniques that can help reduce memory usage and speed up the training process.
• Modal Labs provides a guide for fine-tuning Llama/CodeLlama models7. It includes optimizations for fast results, such as fully-sharded data parallelism, parameter-efficient fine-tuning via LoRa adapters, and gradient checkpointing to reduce VRAM footprint.
• Hugging Face has a blog post on training a LlaMa model to answer questions on Stack Exchange with RLHF8.
• An article on arXiv discusses LLaMA-Adapter, a lightweight adaption method to efficiently fine-tune LLaMA into an instruction-following model9.

Might add - Coursera, Udacity (Modern & Fundamental AI), MIT OCW (AI fundamental rigour),