Guidance on learning about LLMs deeply

Hey everyone,

I’m diving deeper into the world of Large Language Models (LLMs) and had a many questions I was hoping to get input on from the community. Feel free to give answer to any of my questions! You don’t have to answer all! 1. LLM Frameworks: I’m currently using LangChain and recently exploring LangGraph. Are there any other LLM orchestration frameworks which companies are actively using?

2. Agent Evaluation:

How do you approach the evaluation of agents in your pipelines? Any best practices or tools you rely on? 3. Attention Mechanisms: I’m familiar with multi-head attention, sparse attention, and window attention. Are there other noteworthy attention mechanisms worth checking out? 4. Fine-Tuning Methods: Besides LoRA and QLoRA, are there other commonly used or emerging techniques for LLM fine-tuning? 5. Understanding the Basics: I read a book on attention and LLMs that came out last September. It covered foundational topics well. Has anything crucial come out since then that might not be in the book? 6. Using HuggingFace: I mostly use HuggingFace for embedding models, and for local LLMs, I’ve been using OLAMA. Curious how others are using HuggingFace—especially beyond embeddings. 7. Fine-Tuning Datasets: Where do you typically source data for fine-tuning your models? Are there any reliable public datasets or workflows you’d recommend?

Any book or paper recommendations? (I actively read papers but maybe i see something new)

Would love to hear your approaches or suggestions—thanks in advance!