Lamini is an Enterprise AI Platform that helps users run and fine-tune open Language and Vision Models (LLMs) with leading accuracy and reduced hallucinations. It is infrastructure-agnostic and can be run anywhere - on-premise, VPC, Nvidia, or AMD GPUs - to help users get the most out of their computational resources.

The platform is designed to provide exceptional performance and is equipped with various features that help improve accuracy, reduce hallucinations, and deliver structured outputs. The features include optimized JSON decoding, photographic memory through retrieval-augmented fine-tuning, DPO training with human preferences, integrated RAG-Finetuning framework, and evaluation frameworks for tuned models.

The platform's performance and features are geared towards accelerating development and improving time to market. Highly parallelized inference for large batch inference, parameter-efficient fine-tuning that scales to millions of production adapters, infrastructure-agnostic features, and scalable costs with ROI are all built-in to help enterprise companies develop and control their own LLMs safely and quickly.

The Lamini team is made up of experts, including Co-founder and CEO, Sharon Zhou. Zhou is a Stanford CS faculty in Generative AI with a PhD in the same field from Stanford under Andrew Ng. Co-founder and CTO, Greg Diamos, is an MLPerf co-founder who has led teams at Baidu, NVIDIA, and Georgia Tech.

The Lamini platform also offers various technical recipes and evaluation suites, accessible through its blogs and documentation. The blogs contain technical discussions and guides on optimizing LLMs for different hardware configurations, guaranteeing valid JSON output, and multi-node LLM training on AMD GPUs.

Lamini offers enterprise companies a unique platform that delivers top-quality LLM development, scalability, and security. The platform's functions, advantages, and features make it a must-have tool for companies that want to develop specialized LLM building blocks to accelerate their AI initiatives.