The best way to predict a future is to look for it. We live in the moment where the world is a vast machine for predicting the future.
This summer, I spent a significant amount of time contemplating large language models and delving deeper into their research. My first encounter with GPT-2 was back in 2019, where I explored its code and experimented with it. During this period, I became curious about transfer learning and its applications. Additionally, I had some prior knowledge about transformers, but it wasn’t as comprehensive as my understanding of LSTMs and RNNs. I couldn’t confidently explain what they did, for example.
While researching transfer learning with smaller models like GPT-2, I stumbled upon Gwern Branwen’s website (https://gwern.net/) and, in particular, his TWDNE Project (https://gwern.net/twdne). I found it clever because it combined a generative model for both images and text. I decided to focus on the text side of the project, as the image aspect was already well-addressed by applications like Stable Diffusion….
I might revisit the image style transfer aspect in the future, as I had previously explored it to some extent. You can find more about this in my “How to Generate Art Demo Followup.”
Before this, I had predominantly explored machine learning with code from the ground up using Python (PMLC). I have used ML practically in the form of genetic algorithms for tuning parameters on investing models for years, non-differentiable, so no chain rule! An offshoot was a project called gen-gen-algo, a generic genetic algorithm. Now, finally after all these side quests, I was ready to tackle something more complex and cutting-edge using GPT.
I found excellent resources on GitHub and in video format from Andrej Karpathy (https://github.com/karpathy). The following repositories were particularly helpful in my learning journey. The first one, “nn-zero-to-hero,” features a series of videos that provided a solid foundation in understanding transformers.
The second repository, “makemore,” served as my warm-up exercise to get back into working with transformers and Large Language Models (LLMs) after a period of dormancy in the field. You can access these repositories here:
1. “nn-zero-to-hero”: https://github.com/karpathy/nn-zero-to-hero
2. “makemore”: https://github.com/karpathy/makemore
Fork of makemore
My experience with “makemore” went beyond the basic examples provided in the original repository, which generated new names based on a dataset of names. Initially, my goal was to apply “makemore” to various datasets other than “names.txt.” I experimented with larger and smaller datasets, including those with extensive collections of English words, numbers for addition, square roots, and a substantial dataset of quotes containing nearly 10 million entries, some of which had lines as long as 505 characters. By using scripts and modifications to “makemore.py,” I conducted a grid search to optimize hyperparameters, including constraints on model size. Output from “makemore.py” was saved to a CSV file, along with hexadecimal hash values for easy tracking and analysis during the tuning process.
To further enhance the code, I introduced a grid search optimization method using a Bash script. This allowed for exploring the hyperparameter space while maintaining a ceiling on the model size. Without such constraints, optimization typically led to increasingly larger models that resulted in the lowest loss.
I also introduced the concept of assigning a random hexadecimal tag to the output of “makemore.py.” This tagging system facilitated the easy identification of the best loss and the associated set of hyperparameters that produced it. Additionally, I incorporated an early stopping mechanism into the “makemore.py” code.
If you’re interested in exploring my fork of Andrej Karpathy’s “makemore” code, you can find it here:
https://github.com/erickclasen/makemore
For a more detailed understanding, I’ve created a comprehensive “verbose-readme.pdf” that provides additional information:
Version on this site, opens in browser:
GitHub Version requires downloading:
https://github.com/erickclasen/makemore/blob/master/verbose-readme.pdf
