LLaMA: Open and Efficient Foundation Language Models
LLaMA: Open and Efficient Foundation Language Models is one of 15 landmark papers in TheLLMWiki's research index — explained here in plain language, without the jargon.
What this paper showed
Showed that smaller models trained on far more tokens than earlier scaling laws recommended could match or beat larger models at inference time, and released the model weights to researchers. This paper is widely credited with kickstarting the modern open-weight LLM ecosystem.
For the full technical detail — architecture diagrams, training data, ablations and exact benchmark numbers — the original paper is the authoritative source; this page exists to give you the plain-language version before you decide whether to read further.
The lasting impact of LLaMA: Open and Efficient Foundation Language Models
This paper is a large part of why open-weight models exist at the scale they do today — Mistral, DeepSeek, Qwen and dozens of other open releases followed the path LLaMA opened up, of training smaller models on more data rather than only scaling parameter count.
Papers earn a place in this index specifically because their core idea is still visible in production systems today, not just because they were influential when published. If you're trying to understand why a current model or technique works the way it does, tracing it back to a paper like this one is usually more useful than reading a summary of the model's release notes alone.
LLaMA: Open and Efficient Foundation Language Models, answered
Who published LLaMA: Open and Efficient Foundation Language Models?
This paper came out of Meta.
Why does this paper matter today?
This paper is a large part of why open-weight models exist at the scale they do today — Mistral, DeepSeek, Qwen and dozens of other open releases followed the path LLaMA opened up, of training smaller models on more data rather than only scaling parameter count.
Where can I read the full paper?
Search the paper title on arXiv or Google Scholar for the original PDF and any follow-up work that has cited it.
Do I need to read the full paper to understand the idea?
Not necessarily — the plain-language summary above covers the core contribution. The full paper matters most if you're implementing the technique yourself or need the exact experimental details.
What should I read next?
See the related papers above, or the AI research hub for more landmark work organized by topic.
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