Current Best Practices for Training LLMs from Scratch
The best teams building the large language models transforming our world train those models on Weights & Biases. In this whitepaper, we’ll share what we’ve learned from an insider’s perspective. You’ll read about:
- How much data you need to train a competitive LLM
- Balancing memory and compute efficiency
- Different techniques for parallelization
- Tokenization strategies and their tradeoffs
- Model evaluation
- How to mitigate bias and toxicity in your modeling
- And a whole lot more
W&B enables the collaboration required to produce these complex, expensive models and push them to production. We’re happy to showcase a few things we’ve learned along the way. The whitepaper is free and will be emailed to you via the form on the right.
