Zing Data now natively supports fine tuned LLMs. This new capability enables Zing Data customers to take advantage of the latest advances in natural language processing (NLP) to build more powerful and intelligent data analytics applications.
Fine tuned LLMs are large language models that have been trained on a specific dataset or task. This results in models that are more accurate and relevant for data analytics tasks. Fine tuned LLMs have several benefits over general purpose LLMs such as:
Fine-tuning allows LLMs to be tailored to specific tasks such as understanding custom database models, query optimization, question answering, text summarization and visualization inference. This can lead to significant improvements in performance over using a general-purpose LLM.
Fine-tuning can help LLMs to learn more accurate representations of language, which can lead to improved accuracy on a variety of analytical questions and data representations.
Fine-tuning can help LLMs to generalize better to new data, which can be important for situations where data analysis tasks are run on a small size database.
Reduced training time and cost
Fine-tuning LLMs can be done on a much smaller dataset than training them from scratch. This can lead to significant savings in terms of training time and cost.
Zing Data’s new support for fine tuned LLMs makes it easy for customers to deploy these models in production. Customers can simply upload their trained model with its inference endpoint to the Zing Data platform, and then use it to power their data analytics applications. Zing Data also provides a variety of tools and resources to help customers fine tune their LLMs such as secure transmission boundaries and training optimizations.
Zing Data has initial support for Google’s latest AI models such as PaLM 2, Codey that are available in the Vertex AI model garden. Other fine tuned LLMs available on HuggingFace, AWS Sagemaker, OpenAI are coming soon.
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