LLaMA1

LLaMA1 is the first family of large language models developed by Meta AI, released in early 2023. It is a collection of decoder-only transformer models designed to be efficient and versatile for research and practical applications across languages and domains. The family was introduced as a foundation model intended to be smaller and more accessible than some contemporaries while maintaining strong performance on a range of tasks.

The LLaMA1 models use a standard decoder-focused Transformer architecture with multi-head self-attention and feed-forward layers. They employ rotary position embeddings to encode sequence information and are available in several sizes, including 7B, 13B, 30B, and 65B parameters. The design emphasizes efficient training and inference, allowing researchers to run sizeable models with conventional hardware compared to larger, more resource-intensive systems.

Training data for LLaMA1 consisted of publicly available text from diverse sources, such as web pages, books, and other large text collections, aggregated to cover broad linguistic and factual knowledge. The exact token count is substantial, reflecting a large-scale pretraining objective aimed at general-purpose language understanding and generation.

Licensing and release details shaped its accessibility. Meta distributed the weights to researchers under a non-commercial license, restricting direct commercial use and limiting public distribution. This approach facilitated academic and industry experimentation while preserving certain usage controls.

LLaMA1 had a notable impact on the field by providing a compact, competitive alternative that could be evaluated and adapted by many groups with constrained compute. It influenced subsequent developments in the LLaMA family and contributed to ongoing discussions about open access, model evaluation, and responsible use of large language models. As with other foundation models, LLaMA1 requires careful handling of biases, safety considerations, and evaluation across languages and tasks.