LLaMA2

Gold definitionUpdated Apr 2, 2026

Definition

LLaMA2 is a prominent open-source large language model (LLM) developed by Meta, widely adopted by researchers as a benchmark for evaluating advancements in model efficiency, data selection for instruction tuning, and verifiable inference techniques.

At a glance

Executive summary

LLaMA2 is a widely used open-source large language model by Meta, serving as a critical benchmark for researchers. It helps evaluate new methods for making LLMs more efficient through pruning, more effective with less data via smart selection, and more secure through verifiable inference.

TL;DR

LLaMA2 is a popular open-source AI model used by researchers to test and improve how large language models are made smaller, trained better with less data, and run securely.

Key points

A powerful, open-source large language model (LLM) serving as a foundational architecture for research.
Provides a widely adopted, accessible benchmark for developing and evaluating LLM optimization techniques.
Used by researchers and ML engineers focused on LLM efficiency, data optimization, and verifiable inference.
Offers a robust, open-source alternative to proprietary LLMs for research and development, fostering transparency.
A key target for advancements in LLM pruning, efficient instruction tuning, and secure, verifiable inference.

Use cases

Benchmarking new pruning algorithms to reduce the parameter count of LLMs for edge deployment.
Evaluating data selection techniques for instruction tuning to achieve high performance with smaller datasets.
Developing and testing verifiable inference protocols to ensure secure and untampered LLM execution in cloud environments.
Serving as a base model for fine-tuning in various domain-specific applications due to its strong general capabilities.
Comparative studies of novel LLM architectures or training methodologies against a well-established baseline.

Also known as

LLaMA 2