Gretel

Gretel Navigator's synthetic data generation outperformed OpenAI's GPT-4 by 25.6%, surpassed Llama3-70b by 48.1%, and exceeded human expert-curated data by 73.6%. 🤩 Here's how to use Navigator to create high-quality synthetic data for fine-tuning LLMs. https://lnkd.in/eg2tSFes

Try our interactive Streamlit app for a low-code synthetic data generation, easy iteration and experimentation. 👉 https://lnkd.in/enMChBUx #SyntheticData #AI #LowCode

Complex SQL queries can be time-consuming for many developers. But there's good news: Predibase and Gretel are transforming this process by using LLMs to translate natural language into SQL code. Learn how to create powerful, cost-effective AI models for SQL generation.👇 https://lnkd.in/erWkgGRT

💣 Small Language Models (#SLMs) have absolutely exploded in the last two years, resulting in an astounding number of open-weight models published on Hugging Face. However, open-weight isn't the same thing as open-source. Mix in the fact that not everything claiming to be open-source on Hugging Face -- whether it's a model or a dataset -- is actually that, sprinkle in some license confusion, and you have a giant layer cake of complexity. 🍰 This presents real challenges for enterprises trying to digest open-weight models and #synthetic #data ownership. Alexander Watson and I wrote a piece to provide some clarity on this: https://lnkd.in/d4DTKCzT We hope you find it helpful!

The rapid surge of SLM releases underscores the critical need for clear licensing and lineage, essential for organizations managing open-weight models and synthetic data ownership. Key Points: - Open-weight ≠ Open-source: Many open-weight models have restrictions on use, distribution, and commercialization. - Synthetic Data Ownership: Model licenses impact the ownership and usage rights of synthetic data. - High-Quality Data: Creating top-tier synthetic data requires a compound AI approach. Licensing Clarity: Closed-source models, like OpenAI’s, have strict restrictions. Open-weight models, like Llama-3, come with specific conditions. True open-source models, such as Mistral-7B, offer more flexibility and fewer restrictions. Licensing Landscape from July 2024 data from Hugging Face shows: - Only 37% of open-weight models have clear licensing info. - 75% of models are not truly open-source. - Apache 2.0 and MIT licenses are the most common open-source licenses but represent a small fraction of models. Protecting Data Ownership: At Gretel, we ensure customers fully own and use the data they generate, respecting all licensing requirements. This is crucial for regulated industries and enterprises needing high-quality data without compromising ownership.

The rapid growth of language models has blurred the lines around model licenses, creating confusion. Clear lineage and licenses are essential for enterprises to effectively navigate these data ownership and open-source issues. Read more: https://lnkd.in/d4DTKCzT TL;DR - Open-weight models ≠ open-source models - Most open-weight models have significant restrictions - Model licenses impact synthetic data production - High-quality data shouldn't compromise data ownership - Generating best-in-class synthetic data requires a compound AI approach, not just a single LLM call

Exciting results from recent tests of Gretel Navigator, our agent-based, compound AI synthesizer: 🔥 Surpassed human expert-generated data in 73.6% of cases 🔥 Outperformed GPT-4 by 25.6% in comparative tests 🔥 Crushed GPT-3.5-turbo by 97.3% 🔥 Beat Llama3-70b by 48.1% Huge potential for synthetic data enhancing AI model training, particularly in domains with limited data availability. Full report and code: https://lnkd.in/eg2tSFes #SyntheticData #AI #LLM

The pioneering work with ImageNet revolutionized AI training by leveraging internet images, sparking an era of data-intensive deep learning. However, the looming "data wall" and the need for high-quality, diverse data sources present significant challenges for future AI advancements. Enter differentially private synthetic data – a promising solution to this impasse. By generating high-fidelity synthetic datasets, we can ensure robust training without compromising privacy or relying on diminishing real-world data. This approach not only preserves user confidentiality but also offers limitless, high-quality data tailored for specific needs. This has been our focus Gretel since day one.

"Synthetic data is also likely to grow in popularity due to its ability to train AI models at a much faster pace by generating large, clean, relevant datasets." https://lnkd.in/gzpJQZ8W #SyntheticData #OpenDataQuality #AI

Join us for a livestream on the latest in applied science and privacy research, along with demos on creating AI-ready synthetic data. Date: July 30th at 9 AM PT / 12 PM ET Topics: ✨ Open synthetic datasets ✨ Fine-tuning secure LLMs ✨ Live Demos + Q&A Register: https://t.co/IZENg4QIP1