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.
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Trusted by the teams building state-of-the-art LLMs
"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."
"We use W&B for pretty much all of our model training."
"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."
Scalable and Secure
We offer solutions that scale up with massive distributed training, and can be hosted in our secure hosted cloud or on your own private cloud in a self-hosted deployment.
With Weights & Biases you can:
Focus critical developer resources on your core business
Launch new machine learning models faster, with less back and forth
Safeguard IP with a central system of record
Onboard new ML engineers fast, and avoid duplicated work
A Case Study with TRI
Overview
Toyota Research Institute's mission is to build the safest mobility in the world. Machine learning teams at TRI are pursuing autonomous driving, and they use the Weights & Biases system of record to make their models reproducible.
Company size: 300+
Industry: Autonomous vehicles
Problem
Led by Adrien Gaidon, the ML team built up world-class infrastructure for training models, but lacked a good way to track and version the valuable results.
They quickly realized the need for a central system of record, but building a solution internally was a distraction from the team's core goals.
Solution
The TRI team compared different solutions for their experiment tracking problem, and settled on Weights & Biases as the best platform to coordinate machine learning projects.
Instead of tinkering with brittle internal tools and ad-hoc solutions for experiment tracking and prediction visualizations, the ML team was able to standardize with W&B's lightweight experiment tracking and visualization solutions.
The W&B dashboard gave machine learning practitioners a command center to compare across dataset and model versions, maintaining a reliable record of every experiment and result. ML engineers are now free to focus on the valuable work of model development, accelerating project progress.
