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A family of foundation large language models from Meta optimized for efficiency, performance, and broad research and commercial use.
DESCRIPTION
Llama is a series of transformer-based language models released by Meta, spanning multiple parameter sizes and optimized for training and inference efficiency. Newer generations (e.g., Llama 2 and Llama 3) improved capabilities and broadened licensing for commercial use. Llama models power chatbots, coding assistants, and enterprise copilots. They are commonly fine-tuned via parameter-efficient methods and deployed with optimized runtimes. Llama is central to the open model ecosystem and widely distributed via model hubs.
ALTERNATIVE DEFINITIONS
Meta AI
LLaMA is a collection of foundation language models ranging from 7B to 65B parameters designed to help researchers advance their work in AI.
Original announcement emphasizing research orientation and model sizes.
Meta AI
Llama 2 is a family of pretrained and fine-tuned large language models that range in scale up to 70 billion parameters.
Highlights Llama 2 scope and availability.
Wikipedia
LLaMA is a family of large language models released by Meta AI, intended to be more efficient and accessible than many proprietary models.
Neutral overview noting accessibility and efficiency goals.
RELATED TERMS
Large Language Model Transformer Model Meta AI Llama 2 LLaMA 3 Fine-Tuning Tokenization Inference Hugging Face Retrieval-Augmented Generation
COMPETING TERMS
GPT Mistral
KNOWLEDGE AREAS
AI Data Science MLOps Infrastructure
TARGET AUDIENCE
AI Engineer Data Scientist MLOps Engineer AI Product Manager Cloud Architect
EXTERNAL SOURCES
- Wikipedia - LLaMA
- Meta AI - Official Llama models portal
- arXiv - Llama 2 technical report
- Hugging Face - Model cards and weights hub
TOP QUESTIONS
What distinguishes Llama from proprietary models?
Llama emphasizes openness and efficiency, offering strong performance with accessible licensing and community tooling.
Category: Comparison
Which Llama size should I choose?
Match size to latency, cost, and quality needs; smaller models respond faster, larger models offer stronger capabilities.
Category: Best practices
How is Llama used in RAG?
It serves as the generator while vector databases provide retrieved context, improving factuality and grounding.
Category: Application
Can Llama be fine-tuned efficiently?
Yes. Techniques like LoRA and QLoRA reduce compute and memory requirements for customization.
Category: How-to
What are deployment considerations for Llama?
Quantization, optimized runtimes, and hardware acceleration are key for low-latency inference.
Category: Requirements
Is Llama licensed for commercial use?
Llama 2 and later versions allow commercial use under Meta’s license terms; review specifics before deployment.
Category: Compliance
How does Llama compare to GPT?
Llama offers open model access and self-hosting options; GPT provides managed APIs with state-of-the-art capabilities.
Category: Comparison
What are common mistakes with Llama?
Using overly large models without optimization, insufficient prompt controls, and weak evaluation lead to cost and quality issues.
Category: Mistakes
Where can I find Llama weights?
Through Meta’s portal and on Hugging Face, subject to license acceptance.
Category: Learning