Scaling Up: Exploring LLM Learning on Google Colab!
Analysis
This article highlights the exciting process of experimenting with a full-scratch Large Language Model (LLM) on Google Colab! The focus on understanding the bottlenecks in LLM training, especially when scaling data and model size, offers valuable insights for developers and researchers aiming to build more powerful AI systems. It's a fantastic initiative to demystify LLM development.
Key Takeaways
- •The experiment focuses on understanding the challenges of scaling a Transformer-based LLM.
- •The research utilizes a full-scratch LLM implementation instead of pre-built models.
- •The study explores the impact of increasing both data volume and model size.
Reference / Citation
View Original"The article shares experimental results on what happens when increasing the amount of data and model size, based on the experiment."
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Zenn MLJan 25, 2026 01:30
* Cited for critical analysis under Article 32.