Llama 2
By Meta
Llama 2 is an open-weight large language model family released by Meta, offered in multiple parameter sizes and both base and chat-tuned variants, that became widely adopted for research and commercial applications due to its permissive…
Definition
Llama 2 is an open-weight large language model family released by Meta, offered in multiple parameter sizes and both base and chat-tuned variants, that became widely adopted for research and commercial applications due to its permissive license and competitive performance among open models of its era. It became widely adopted for both research and commercial applications because its license permitted most organizations to use it commercially without a separate agreement, and it served as a common baseline for community fine-tuning before later Llama generations superseded it.
Overview
Llama 2 followed Meta's original Llama release and was notable for shipping with a license that permitted commercial use for most organizations, a shift from the research-only restrictions on the first Llama models. This change significantly broadened adoption, as startups and enterprises could build products on top of Llama 2 without negotiating a separate commercial agreement in most cases. The training process combined pretraining on a very large collection of publicly available text with a subsequent supervised fine-tuning stage and reinforcement learning from human feedback for the chat-tuned variants, a pipeline aimed at making the model follow instructions and hold multi-turn conversations more naturally than the base pretrained weights alone would. The family was released in several parameter sizes, giving developers a choice between smaller models suited to constrained hardware and larger models offering stronger general capability, along with a chat-tuned variant fine-tuned using supervised instruction data and reinforcement learning from human feedback to make it more suitable for conversational use out of the box. Meta published a technical report describing the training data mixture, safety evaluation methodology, and red-teaming process it used before release, giving outside researchers more insight into how the chat behavior was shaped than is typical for a closed proprietary model, even though the raw training data itself was not released. Llama 2 was trained on a large corpus of publicly available text and benefited from an increased context length compared to the original Llama, which improved its ability to handle longer prompts and conversations. It became a common baseline in academic research on fine-tuning, alignment, and efficient inference techniques, partly because its open weights allowed reproducible experimentation that closed models did not permit. Where the original Llama was licensed for research only, Llama 2's permissive commercial terms placed it in more direct competition with contemporaneous open models like early Mistral releases and Falcon, and it quickly became the default starting point that many derivative fine-tunes and community tools were built around during that period. Despite its strong reception at release, Llama 2 has since been surpassed in capability by later Llama versions and other open-weight competitors, and it lacks the multimodal input support and longer context windows introduced in subsequent generations. It also carries the general limitations common to models of its generation, including a fixed training cutoff and no built-in access to real-time information. Beyond building products directly on the base weights, many teams used Llama 2 as a foundation for further instruction-tuning on domain-specific data, producing specialized variants for customer support, legal document review, or coding assistance without needing to train a model from scratch. Llama 2 remains historically significant as a turning point that accelerated the broader open-weight LLM ecosystem, spurring a wave of fine-tuned derivatives and community tooling built around its weights. It has no native image or audio input support and a comparatively short context window by later standards, both limitations that Meta addressed in subsequent Llama releases, so projects with multimodal or long-document requirements generally moved on to newer versions once those became available.
Key Features
- Open weights with a license permitting most commercial use
- Available in multiple parameter sizes for different hardware budgets
- Chat-tuned variant using supervised fine-tuning and RLHF
- Increased context length compared to the original Llama
- Widely used as a research baseline for fine-tuning studies
- Strong community ecosystem of derivative fine-tuned models