On this day in 2019, OpenAI released the final GPT-2 model as part of their staged release. I still remember that November well - so much was happening, but GPT-2's release felt like a watershed moment for the field. It showed us what was possible with carefully trained language models.
To recreate some of that GPT-2 magic, I recently tackled an interesting challenge: can you pretrain a language model with just 1 billion tokens - roughly 1/10th of what GPT-2 used - and still get comparable performance? After 50+ systematic experiments testing different dataset mixtures, the answer is yes.
The result is codelion/gpt-2-70m, which achieves over 90% of GPT-2's benchmark performance despite being trained on 10x less data. The key was finding the optimal dataset composition: 50% high-quality textbook PDFs, 30% filtered web content, and 20% educational resources. It even beats GPT-2 on TruthfulQA (47.31% vs 40.69%).
Just applied for HF Community Grant for “Hugging Research” — a lightweight CodeAgent‑based research assistant built on Hugging Face’s Open Deep Research project for the Hugging Face Hub (models, datasets, Spaces, users, collections, papers). It gathers links via dedicated tools and organizes them for easy review.
As this is for the community, comments and suggestions are appreciated: daqc/hugging-research#1