Free Guide: How to Train 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.
Trusted by the teams building state-of-the-art LLMs
Heinrich Kuttler
Research Engineer – Facebook AI Research
Research Engineer – Facebook AI Research
“For us, Weights and Biases was a game-changer. No other MLOps tool available allows for rapid iteration of AI experiments with the same ease of sharing results, annotating interesting behavior, and long-term storage of logging data.”
Peter Welinder
VP of Product- OpenAI
VP of Product- OpenAI
“We use W&B for pretty much all of our model training.”
Ellie Evans
Product Manager- Cohere
Product Manager- Cohere
“W&B lets us examine all of our candidate models at once. This is vital for understanding which model will work best for each customer. Reports have [also] been great for us. They allow us to seamlessly communicate nuanced technical information in a way that’s digestible for non-technical teams.”
