## LLaMA: Open and Efficient Foundation Language Models
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This section is under heavy development.
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## What's new?
This paper introduces a collection of foundation language models ranging from 7B to 65B parameters.
The models are trained on trillion of tokens with publicly available datasets.
The work by [(Hoffman et al. 2022)](https://arxiv.org/abs/2203.15556) shows that given a compute budget smaller models trained on a lot more data can achieve better performance than the larger counterparts. This work recommends training 10B models on 200B tokens. However, the LLaMA paper finds that the performance of a 7B model continues to improve even after 1T tokens.
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This work focuses on training models (LLaMA) that achieve the best possible performance at various inference budgets, by training on more tokens.
## Capabilities & Key Results
Overall, LLaMA-13B outperform GPT-3(175B) on many benchmarks despite being 10x smaller and possible to run a single GPU. LLaMA 65B is competitive with models like Chinchilla-70B and PaLM-540B.
*Paper:* [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971)
