LLaMA

LLaMA is a collection of transformer-based foundation models available in multiple parameter sizes. The models are pre-trained on large-scale text datasets and can be adapted through fine-tuning or prompting for specialized applications. As open-weight models, LLaMA has become widely used by researchers and enterprises to build and customize AI applications without relying solely on proprietary models.

LLaMA has accelerated AI innovation by making high-performance language models more accessible to researchers and developers. Organizations can deploy, fine-tune, and self-host LLaMA models for greater control over performance, privacy, and customization. Like other large language models, however, LLaMA-based applications require appropriate security, governance, and monitoring to address risks such as prompt injection, data leakage, and hallucinations.