Train large scale models and craft the perfect prompts with Weights & Biases
The most innovative large model teams in the world rely on Weights & Biases to train, track, and tune their large scale and generative models.
Trusted by the teams building the largest models
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. When any issues arose, we found the support team at W&B to be quick and helpful.”
Wojciech Zaremba
Co-Founder – OpenAI
Co-Founder – OpenAI
“Weights & Biases moved the AI field from traditionally babysitting a single experiment to managing multiple experiments across many teams spanning entire companies. Collaboration and sharing of scientific insights and results are central tenets of AI today, and only grow more prevalent each day. We are limited as individuals, and can overcome this weakness together.”
Emad Mostaque
CEO and Co-Founder – Stability AI
CEO and Co-Founder – Stability AI
“Not everyone uses excellent tools like Weights & Biases, for example, to track their runs. We would like to move to more and more open runs so you can actually see how they’re doing. So there’s a lot of work to go, but we’re trying to be as collaborative as possible.”
Train concurrently and collaboratein real-time
From pretraining to fine-tuning, large scale model training requires multiple GPUs, multiple nodes and even High Performance Clusters. No matter how distributed or how many experiments, Weights & Biases scales reliably with your organization. Join OpenAI, Cohere, FAIR, and hundreds of other teams building the large scale models shaping the future of machine learning.
Examples
Avoid wasting dataset and model versioning
Easily spot failure and waste with W&B’s real-time model metric and system metric monitoring. Analyze edge cases, highlight regressions, and prune hyperparameters to get the best results from the least resources.
Examples
Iterative prompt development
W&B supports prompt engineering for zero-shot or few-shot tasks by organizing experiments, providing visual and interactive analysis tools, and keeping track of work across chained prompts. It makes exploring a model’s latent space for functional prompts more efficient.
Examples
Large scale dataset exploration
W&B enables dynamic exploration and optimization of large scale model data, predictions, and outputs. It helps you debug datasets and models for continuous improvement and easily share results with your organization.
Examples
See W&B in action
Processing Data for LLMs
Evaluating LLMs
DeepMind Flamingo
