LLaMA, which stands for Large Language Model Meta AI, is a suite of foundational language models from Meta AI that was created to demonstrate that state-of-the-art language models can be created using only publicly available data. The models range from 7B to 65B parameters and were trained on trillions of tokens from several sources, including CommonCrawl, C4, Github, Wikipedia, Books, ArXiv, and StackExchange. LLaMA achieved comparable state-of-the-art performance when compared to the best models available today, such as Chinchilla70B and PaLM-540B [2][3].

Like other large language models, LLaMA takes a sequence of words as input and predicts the next word in a sequence of predetermined length to recursively generate text. LLaMA was trained on text from the 20 languages with the most speakers, focusing on those with Latin and Cyrillic alphabets, making it capable of predicting sentences in a wide variety of languages and contexts with little difficulty [3].

The authors of the LLaMA paper compared LLaMA to existing large language models on two closed-book question answering benchmarks: Natural Questions and TriviaQA. They found that LLaMA consistently outperformed GPT3, Gopher, Chinchilla, and PaLM. Additionally, they found that smaller models trained on more data achieved the best performances, not the largest models. LLaMA-13B outperformed GPT-3 (175B) on most benchmarks despite being 10× smaller [2][4].

The LLaMA models were trained on a mixture of many open datasets of diverse domains. This diversity of pre-training data is helping LLaMA achieve few-shot capabilities [4]. The authors of the LLaMA paper trained their models on publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. This approach makes LLaMA more open and efficient than other language models [2].

LLaMA has also been tested for code generation capabilities. In both HumanEval and MBPP benchmarks, a description of the program in a few sentences is inputted to the model, in addition to a few input-output examples. The model then generates a script of Python code that fits the description and/or satisfies the test cases [3].

When sampling the LLaMA model, it is important to note that LLaMA, unlike popular models like ChatGPT, was not optimized or fine-tuned for human-level inputs. LLaMA works best when the prompt is put into an additional context of some sort [3]. The smaller models in particular can be tripped up easily and made to follow a looping pattern [3].

In conclusion, LLaMA is a powerful model for a huge variety of natural language understanding tasks. It achieves state-of-the-art performance despite being trained exclusively on publicly available datasets. LLaMA's code generation capabilities and few-shot capabilities make it a versatile model for a wide range of applications [2][3][4].