LLaMA (Large Language Model Meta AI) is a family of transformer-based foundational language models released for research and downstream fine-tuning; they provide various model sizes to trade off compute cost and capability for tasks like generation, summarization, and embeddings.

LLaMA is characterized by three key components:

• Transformer architecture:large-scale self-attention models trained on diverse text corpora.
• Multiple model sizes: offers options from smaller, efficient models to large-capacity variants for different compute envelopes.
• Fine-tuning & adaptation: designed to be adapted via fine-tuning or instruction-tuning for specialized tasks.

LLaMA redefined NLP by democratizing access to powerful research models, accelerating experimentation. Its flexible architecture enables efficient deployment across clouds and edge servers, leveraging diverse hardware (e.g., Arm-based Ampere CPUs, GPUs) and ONNX-optimized runtimes), when cost or power efficiency matters.

• LLaMA paper (arXiv)
• Hugging Face: LLM ecosystem
• ONNX Runtime: optimized inference runtimes
• Ampere Altra for ML inference on CPUs