LLM Theory
Published:
Awesome LLM theory articles I chanced upon
- State of LLMs in 2023 by Hyung Won Chung
- Transformer Math 101 by Eleuther AI.
- Transformer Inference Arithmetic by Kipply.
less than 1 minute read
Published:
Awesome LLM theory articles I chanced upon
6 minute read
Published:
Having done about 1.5 years of personalization across multiple clients, I wanted to consolidate my takeaways here. Personalization is here to stay because of it’s contextual nature; hopefully someone doing perso will find them useful!
3 minute read
Published:
I’ve always wanted to compete in Kaggle. And the cool thing about being a consultant is that on case break, I actually have some time to commit to Kaggling!
2 minute read
Published:
As I’m learning more about 3D parallelism, I wonder - suppose that every device takes in a batch of tensors where the tensors across devices are of different sizes, will 3D parallelism still work? Turns out, it works for data and pipeline parallelism, but tensor parallelism will need some work.
2 minute read
Published:
Retrieval Augmented Generation (RAG) is a framework for retrieving facts from an external knowledge base to ground large language models (LLMs) on the most accurate, up-to-date information. RAG is increasingly popular in industry as it’s simple to implement yet powerful. Here I’ll share some tricks to improve RAG systems.