Outperform generic LLMs with specialized distilled models that require %80 less data to train.
Deploy distilled models that are up to 2000× smaller than generic LLMs, drastically reducing training and inference costs.
Ensure safer outputs with distilled models, decreasing the chance of inadvertently releasing sensitive or harmful content.
Researchers from Google and Snorkel found a new method for training smaller models that outperform large language models (LLMs) using less training data.
Improved accuracy with 50% less training data
Compared to a previously fine-tuned LLM.
2000x smaller models
From duplicates and search misses across a catalog of 45M products
Distill LLMs into specialized AI
Programmatically slice, filter, curate, and rank your data to build custom LLMs that you can trust.
Leverage the latest LLMs to distill your model
Streamline data curation by encoding your subject matter knowledge into programmatic data operations such as labeling, filtering, sampling, slicing, augmentation, and more to build smaller specialized models.
Ensure your models are compliant
Reduce data exposure by removing sensitive data programmatically to distill LLMs into compact models that safeguard privacy while maintaining or improving accuracy.
Accelerate fine-tuning with guided error analysis
Evaluate model performance with expert and model-based feedback, and rapidly correct error modes by focusing on the data slices that matter.
Foster innovation through multi-persona collaboration
Enhance the productivity of your subject matter experts (SMEs) and annotation teams with direct and seamless collaboration between business and data science teams.
Distilled models for the enterprise
Go from an initial demo to a robust production-ready application using Snorkel Flow.
Distill bulky models into smaller ones with faster response times that can be deployed on mobile and edge devices or used for real-time chat.
Leverage smaller AI models for better extraction, classification, and other predictive tasks for domain-specific use cases or improve existing models already in production.
Develop enterprise-grade generative models tailored to your business needs 10x faster through programmatic distillation and guided fine-tuning.
Generative AI Data Development Blueprint
Model distillation capabilities
Programmatic data curation
Unique programmatic approach to sampling, filtering, and ranking, based on our existing programmatic/weak supervision techniques.
Guided error analysis
Get real-time quantitative and qualitative feedback on the labeling functions you write for guided iteration.
Customization and fine-tuning
Incorporate subject matter knowledge to quickly customize models for specific tasks and response styles 100x faster.
Control content creation and transmission through prompts or other methods to reduce the risk of sensitive data exposure.
Continuously update model performance and respond to changes in requirements and markets faster.
Easily Iterate on prompts and prompt templates programmatically to refine data, correct errors, and incorporate SME feedback to distill smaller, more accurate models.
Create new data to protect PII or adjust underrepresented or over-represented variables within your data with the same distribution.
Transparency & audibility
Improve model trustworthiness and predictability by knowing with certainty the data that was used to train your model.